3 Things to Support Your Quality Payment Program

3 Things to Support Your Quality Program

3 Things to Support Your Quality Payment Program

Three specific pillars are essential for your quality program. Initially, what establishes its foundation? Once determined, how do you keep it harmonized with internal resources and external changes? Lastly, how will you cultivate it and make sure it delivers the needed results?

ESTABLISH: Start with quality data. The data needs to be rich; specifically, it needs to come from many sources and can be properly ingested and cleansed.

MAINTAIN: Ensure upkeep is easy. Be aligned with the latest regulatory changes without additional burden to your staff.

CULTIVATE: Drive optimal results. Make sure you can:

  • Reflect on program performance easily and simply
  • Identify where more can be done confidently
  • Take meaningful action to move the needle quickly

Read on for additional takeaways. One specific to quality leaders and the other specific to society leaders.



Improve care beyond the registry.

It sounds expensive.

It seems overwhelming.

Yes. It is complex!

It has never been more attainable. Do these three things to unlock registry value.

  1. Integrate meaningful and high-quality data: successful data acquisition requires the secure and efficient procurement of data from multiple sources powered by integration technologies and expertise that validates and ensures data quality
  2. Apply robust analytics: build trust in the data and uncover drivers of outcomes and variation with the superpowers of risk adjustment, reliability adjustment, benchmarking, and peer group definition
  3. Inform to drive results: deliver intuitive, interactive reports and provide flexible tools that crystalize the patterns and inspire meaningful clinical action

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Leverage your solid foundation of registry data and build for lasting value.

Registry data fuels the trusted insights on clinician and device performance as well as ways to boost quality improvement and impact care delivery. Owners and stewards of quality data are pivotal to extend its impact. Don’t know where to start? Step one, create the framework, then build momentum through data to provide evidence of value, and lastly commercialize your data to extend value across the health ecosystem.

CREATE FRAMEWORK with foundational assets

  • Get data
  • Show value and impact
  • Increase participation

Fuel long-term registry value with data models that combine foundational assets with longitudinal patient data for shared decision-making between providers and patients.

BUILD MOMENTUM with intermediate assets

  • Engage clinicians and patients
  • Identify improvements
  • Change behavior

Add patient-reported outcomes to emphasize the patient experience and impact patient populations.

TRANSFORM CLINICAL CARE with advanced assets

  • Publish findings
  • Predict outcomes via analytics and calculators
  • Change clinical guidelines

Provide stakeholders with the right data at the right time. It is table stakes to drive clinical adoption and value. It is necessary to impact standards of care, improve outcomes, and avoid costly complications that affect reimbursements and patients’ lives.

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What Is Healthcare Benchmarking?

What is Healthcare Benchmarking?

What Is Healthcare Benchmarking?

Striving to improve is always a worthwhile pursuit, but measuring your own improvement is only one piece of the healthcare quality puzzle. To see the full picture of the effectiveness of your efforts, you need to understand how you compare.

That’s where healthcare benchmarking comes in.

Benchmarking allows you to compare yourself or your organization to others and it is a critical component of successful quality and performance improvement. This is especially true in clinical registries where benchmarks aren’t about rankings, but rather, achieving real-world results.

Let's dive into the basics of healthcare benchmarking in registries.

In this post we will cover:

  • What Is Healthcare Benchmarking and Why Does It Matter?
  • How Do You Benchmark Healthcare Performance?
  • What Are Examples of Healthcare Benchmarking?

What Is Healthcare Benchmarking and Why Does It Matter?

Put simply, benchmarking in healthcare means comparing the performance of an organization or clinician to others.

The goal of benchmarking in registries is to improve quality, efficiency, and patient experience.  Accurate comparisons and fair benchmarking are critical components of successful quality improvement initiatives.

When it comes to registries, benchmarks are highly valuable for individual sites or clinicians.

Your registry participants can use benchmarks to understand how they compare to others and why. Benchmarks can help orient their organization in the broader healthcare landscape. They’ll not only understand where they rank — at the bottom, middle, or top of the pack — but also understand why.

Benchmarking allows hospitals and practices, individual clinicians, and other healthcare organizations to monitor their own performance, compared to their de-identified peers, as applicable. Benchmarks can be applied to metrics about patient characteristics, volume, processes, outcomes or other meaningful categories.

How Do You Benchmark Healthcare Performance?

One way we approach benchmarking is through our reporting framework.

We look at:

  1. Descriptive Statistics: Who makes up my population?
  1. Process and Adherence: What am I doing?
  1. Outcomes: What are my outcomes?
  1. Comparison: How do I compare?
Reporting Framework for Healthcare Benchmarking

After understanding the basics of your data, what initiatives you’re undertaking regarding process and adherence, and what your outcomes look like, benchmarking your metrics against your peers is the next crucial step.

Comparison has always been a key component of our analytics and reporting framework because it uncovers opportunities to improve.

We provide reporting and peer benchmarking capabilities at the clinician, practice, multi-practice, multi-facility, and organization levels. This enables organizations to compare individual clinicians or practices/sites to the appropriate benchmark.

To accurately analyze benchmarks, you need to clearly articulate both what metrics you are comparing and what organizations you are using as comparisons.

Metrics of interest for healthcare benchmarking include:

  • Mortality rates
  • Length of stay
  • Readmissions
  • Postoperative complications
  • Patient wait times
  • Adherence to established protocols, such as hand washing
  • Patient satisfaction
  • Pharmaceutical side effects and outcomes
  • Outcomes following the use of medical devices

Comparison metrics are most accurate with a proper comparison group, also called a peer group. A peer group is a group of individuals or entities that share similar characteristics and interests among one another. Importantly, peer groups allow organizations to compare themselves to other, similar organizations.

To accurately identify your peer group, consider the following characteristics of healthcare organizations and practices:

  • Bed size
  • Procedure volume
  • Provider volume
  • Practice type (Private Practice, Academic/Research Hospital, Healthcare System, Outpatient Lab, Federal Government Facility)
  • Private practices
  • Geographic type (e.g. urban, rural suburban)
  • Teaching hospital
  • Department
  • Single-specialty vs. multi-specialty
  • Region

Benchmarking is a key competency of all reporting in our registries. We make the process simple by configuring specific groups for aggregation and benchmarking for you. The benchmark groups available to you can be managed through your administrative dashboard and you can modify them at any time. We include an organization-wide ‘All’ benchmark, as well as the ability to create peer groups of custom benchmarks. You can visually compare your organization’s performance to any benchmark, and benchmark values are color-coded on reports to allow for ease of use.

Sample Peer Group Benchmarking Report

What Is Quality Improvement in Healthcare?

What Is Quality Improvement in Healthcare?

What Is Quality Improvement in Healthcare?

Quality improvement in healthcare delivery benefits everyone — from patients and clinicians to hospitals, payers, and life science organizations. Patients have better outcomes, clinicians make evidence-based decisions, hospitals and payers achieve financial efficiencies, and life science organizations develop safer products.

To achieve these goals, you need a solid understanding of what quality improvement is, who contributes, why it matters, and how to evaluate and track your progress.

In this post we will cover:

  • What Is Quality Improvement in Healthcare and Why Does It Matter?
  • Who Contributes to Healthcare Quality Improvement?
  • How Do You Evaluate Healthcare Quality?
  • How Do You Achieve Quality Improvement?
  • Quality Improvement Projects in Healthcare

What Is Quality Improvement in Healthcare and Why Does It Matter?

Quality improvement is measurable progress toward better patient care and outcomes.

What might quality improvement look like in practice? Examples include:

  • Reducing adverse events
  • Preventing unnecessary hospitalizations
  • Developing safer health products and devices
  • Achieving financial efficiencies and savings
  • Improving patient quality of life
  • Making evidence-based care decisions

Put simply, quality improvement matters because it benefits everyone. Without quality improvement efforts, clinical patient care, pharmaceutical research and development, and medical device innovation would struggle to advance. Additionally, many value-based care programs and other forms of healthcare reimbursement are linked to quality improvement.

Quality improvement is a crucial goal for any healthcare organization. To get better, you need to know where you stand, how you compare, and what you can do to improve.

Who Contributes to Healthcare Quality Improvement?

Healthcare quality improvement requires buy-in and cooperation from a diverse group of healthcare stakeholders.

  • Clinicians and providers are integral to providing care that is safe, efficacious, and has value.
  • Patients and caregivers are vital to healthcare quality through providing feedback through patient-reported outcomes (PROs) programs to help improve quality of care.
  • Hospitals, health systems and practices are fundamental as they provide the structural foundation and systems needed to foster an ecosystem of quality.
  • Payers ensure that providers are compensated fairly and equitably for high-quality care and can drive improved outcomes in a value-based care system.

Other stakeholders in the healthcare quality ecosystem include pharmaceutical companies and medical device organizations, which improve healthcare through providing safe and effective interventions. Non-profit organizations and agencies are also tasked with overseeing and promoting quality practices.

And, last but not least, specialty societies, associations, and collaborative quality initiatives are essential to evaluating quality through the development and implementation of quality measures and often steward clinical data registries that are the hub of quality improvement practices.

Achieving quality improvement at scale requires bringing multiple clinicians, sites, and patients together with a common goal. This is often achieved through the help of a robust data infrastructure, such as a clinical registry, that can support numerous and evolving quality initiatives.

How Do You Evaluate Healthcare Quality?

Although there are many ways to assess and achieve healthcare quality, attaining quality improvement at scale is made easier with an advanced clinical data registry.

Registries collect, organize, and display information with the purpose of evaluating and improving outcomes. Combined with rigorous analytics, clinical registries can point to areas for improvement, identify best practices, and help make informed decisions to reach your quality goals.

Specifically, clinical data registries track information about the health status of patients and the care they receive. They bring together large data sets and analyze trends or patterns in treatments and outcomes to help inform best practices, guidelines, treatment decisions, and more.

Check out this post to learn more about clinical data registries and how they can be leveraged to achieve real-world quality improvement: The Basics of Clinical Data Registries

To best measure quality and achieve improvement, we recommend working with a registry technology partner through a three-step process.

  1. Align your data strategy with your goals.
    Before building a registry for quality improvement, you need to define your goals. What would you like to improve? What would success look like? With the end in mind, you can then pull together relevant data from many sources in one place to produce reliable insights for decision-making.
  1. Ensure measures and reports are clinically meaningful.
    Making real progress requires clinician engagement. By including clinicians and tracking measures that affect real-world clinical quality, you can facilitate evidence-based comparisons that catapult quality improvement efforts.
  1. Leverage insights to make informed decisions.
    With high-quality data, you gain access to high-quality insights, which you can then use to make informed decisions that are rooted in validated, accurate data and rigorous analytics. Put simply, when you have insights you and others trust, you can identify the best path to quality improvement.
3 steps to quality improvement

Check out this post to learn how the long-term success and sustainability of a registry are affected by several decisions: How to Define Success for Your Clinical Registry

Evaluating healthcare quality improvement requires a nested, multifaceted approach with stakeholders being accountable to one another while servicing their own needs.

Hospitals, health systems, and practices provide the structural backbone of quality, and are necessary for the success of quality improvement for clinicians, pharmaceutical companies, and medical device manufacturers.

All quality improvement efforts rely on a backbone of measurement, data collection, and implementation that optimizes clinical workflow and evaluation of quality is best achieved through the registry.

Evaluating Quality for Clinicians

To improve care quality, clinicians need to be able to:

  • Measure and understand their own clinical performance.
  • Optimize decision-making with the right information at the right time.
  • Drive real-time interventions through predictive analytics.
  • Act on performance feedback quickly and decisively.
  • Identify best practices and make evidence-based decisions.
  • Provide value through performance measurement coupled with technology and systems for improvement.

Clinicians achieve quality improvement when patients have better care, better outcomes, and better quality of life. With the right tools, such as an advanced clinical data registry, clinicians can easily track, measure, and compare their quality of care.

Evaluating Quality for Hospitals, Health Systems, and Practices

Large healthcare organizations such as hospitals and health systems also strive to improve care for patients, but their measures of quality improvement differ slightly from clinicians.

To achieve measurable quality improvement, hospitals should:

  • Succeed in value-based care models, such as bundled payment and other at-risk reimbursement programs.
  • Optimize financial performance.
  • Empower and engage clinicians with real-time performance feedback and other vital information that drives results.
  • Deliver high-value patient care as efficiently as possible.
  • Manage resources, reduce costs, and improve outcomes across episodes of care.
  • Drive real-time interventions and optimize clinical workflow with predictive analytics.

Hospitals and health care systems are integral in the quality improvement process and participate in a variety of ways. Clinical registries are essential in the data collection process to fulfill the requirements for many quality indicators and therefore be the vector for quality improvement.

Additional common quality improvement tools for hospitals include:

Evaluating Quality for Pharmaceutical Companies

For pharmaceutical companies with products and medications on the market, understanding those products’ effectiveness and safety in the real world is key, and can help buttress quality improvement efforts.

For quality improvement efforts to be successful, pharmaceutical companies must:

  • Evaluate product effectiveness in large, diverse patient groups.
  • Identify risks, complications, and adverse outcomes as they arise.
  • Compare new products with existing options and the standard of care.
  • Update guidelines as certain populations or groups find more or new benefits.
  • Comply with regulatory requirements.
  • Enhance product safety and efficacy.

Evaluating Quality for Medical Device Manufacturers

Quality improvement for medical device manufacturers relies on real-world evidence. To improve, you must understand how your device performs in the real world, with real patients—and how it compares to existing options and the standard of care. Registry-supported research and post-market surveillance can improve quality throughout the product lifecycle, further enhancing the efficacy and safety of medical devices.

More specifically, quality improvement efforts can help:

  • Address risks, complications, and adverse outcomes.
  • Establish a new standard of care.
  • Update usage guidelines.
  • Comply with regulatory requirements.
  • Ensure your devices perform optimally in real-world settings.
  • Accelerate your path to value.

Device registries are powerful tools for assessing device safety, efficacy, and value, and ultimately delivering high-performing products to patients.

How Do You Achieve Quality Improvement?

Implementing and scaling healthcare quality improvement can be summed up in the three phrases that are integral to ArborMetrix’s (and our customers’) success: Specify, Measure, Act.

Learning health systems, medical specialty societies, and collaborative quality initiatives all depend upon the foundation of registries to manage quality improvement at scale and follow this same methodology.

Specify, Measure, Act on Quality Improvement


The clinical data registry is the workhorse of the specification phase of quality improvement. Breaking down metrics into actionable items and collecting the necessary data to track them is integral to any successful quality improvement program.

Data specification and collection happens through many modalities including patient-reported outcome surveys, as well as collection of efficacy data as related to interventions like drugs and medical devices.


All quality improvement requires robust measurement through determining and prioritizing problem areas that need improvement and creating metrics to track these areas.

Measurement begins with using existing data and research to understand problems and analyzing gaps in care. It occurs at every level of healthcare quality including clinician performance improvement programs such as MIPS, health system-based programs such as HEDIS, and AHRQ quality indicators. Measurement also occurs at industry level, in post-market surveillance and the FDA Pharmaceutical Quality System.


Using the insights gained from the data collection process to act and make changes in your processes is the final step in the quality improvement process.

However, quality improvement does not stop here. The quality improvement process is continuous, and your results should be used as your new baseline data for building ongoing quality improvement processes. This includes sharing performance feedback with clinicians and hospitals, sharing ratings with patients, and disseminating information to the broader medical community.

Collecting additional data and measuring adherence and outcomes in real-time accelerates the process and builds a canon upon which clinical guidelines are developed.

Quality Improvement Projects in Healthcare

Quality improvement can result in a host of benefits — clinical, procedural, financial, and more. Here are a few organizations that have achieved significant accomplishments as a result of their quality improvement projects and initiatives. They each deploy a modern data infrastructure through their clinical data registries to support data-driven action.

Project: Enhancing Prostate Biopsy Safety

The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a clinician-led quality improvement collaborative comprised of a network of urology practices. They use a clinical data registry to support various quality initiatives.

One of MUSIC’s earliest quality improvement projects aimed to enhance the safety of prostate biopsy. Specifically, the collaborative focused on “reducing the frequency of adverse events after transrectal ultrasound guided prostate biopsy as a diagnostic tool for prostate cancer.” MUSIC leveraged registry data and its collaborative to identify the key to reducing the majority of prostate biopsy related hospitalizations. They developed two specific treatment pathways and introduced them via the MUSIC Biopsy Pathway Protocol.

Data in the MUSIC registry demonstrated a nearly 50% reduction in prostate biopsy-related hospitalizations since the start of their QI initiatives in this area.

Project: Improving Outcomes for Children with Critical Heart Conditions

The Pediatric Cardiac Critical Care Consortium (PC4) is one of five founding organizations of Cardiac Networks United (CNU), a national network of physicians and pediatric hospitals that aims to advance pediatric congenital heart disease care and outcomes. More specifically, PC4 aims to improve the quality of care for pediatric heart patients through transparent data sharing that allows hospitals to evaluate their own outcomes and learn best practices.

Their efforts are proving effective and the results are outstanding. Eighteen hospitals significantly reduced mortality (by ~24%) and improved care for children with critical heart conditions, according to a paper published in the December 2019 edition of the Journal of the American College of Cardiology. The study analyzed more than 19,000 hospitalizations that included cardiac surgery at the participating sites in the PC4 registry.

Project: Reducing Readmissions and Emergency Department Visits

The Michigan Bariatric Surgery Collaborative (MBSC) is a quality collaborative that improves the science and practice of metabolic and bariatric surgery through patient-centered obesity care.

One of MBSC’s recent quality improvement projects, called M-PIRRE, aims “to identify and address the underlying causes of avoidable ED visits, then design and implement site-specific evidence-based interventions to reduce them.”

MBSC has developed a four-phase program that involves developing a patient ED questionnaire, analyzing patient-provided data, designing and implementing site-specific intervention plans, and measuring the impact of those plans. MBSC will create a “universal toolkit based on successful evaluations of underlying causes and interventions to eliminate them” and, ultimately, “reduce ED visit rates among sites and MBSC as a whole.”

Check out this post for additional examples of quality improvement achieved through clinical registries: Top Examples of Quality Improvement through Clinical Registries

How to Preserve Data Quality in Healthcare When Working with Big Data Sets

How to Preserve Data Quality in Healthcare When Working with Big Data Sets

How to Preserve Data Quality in Healthcare When Working with Big Data Sets

A clinical registry is only as valuable and impactful as its data is trusted and usable.

High-quality data is the foundation of any clinical data registry, yet it does not develop on its own. In the world of healthcare, we have millions of unique data elements of different sizes, formats, and characteristics.

Before we get into the process of achieving high quality data, let’s start with a brief definition.

What Is High-Quality Data in Healthcare?

Characteristics of data quality include data that is precise, validated, and comprehensive. These characteristics provide valuable assets to meet the evolving data requirements of varied healthcare stakeholders.

Acquiring data from various sources is the first step. The necessary and important next step is to take all the data blocks and build something grand, nimble, and valuable.

Data Integrity Process in Healthcare

In order to preserve and extract data value, you need a solution that is centered around a multi-layered data integrity process. This is of paramount importance for registry activities.

Multi-layered data integrity processes enable comprehensive, persistent, and widespread data integrity protocols that protect registry data and reporting in serving the needs of diverse stakeholders.

There are five methods for perpetually protecting data integrity

Data Review & Validation

Whenever connecting to a new data source, EHR system, or modifying an existing source, registries should engage in a thorough analysis to assess data integrity across several domains:

  • Completeness: Is the data element available and populated?
  • Concordance: Do the values of the data element agree with those of related variables?
  • Plausibility: Do the values of the data element make clinical sense?
  • Currency: How recent is the available data?

Comprehensive Validation

It is critical to preserve data quality throughout the life of a registry. The best way to accomplish this is through real-time and ongoing data validation checks that deliver the following components.

  1. Validation Rules: Each data integration interface should be configured to include robust and comprehensive validation rules that ensure only valid records enter the registry.
  2. Data Quality Reporting: Dedicated registry dashboards should provide registry participants with real-time information on their data qualityacross dimensions such as completeness, concordance, plausibility, and currency.
  1. Systems Monitoring and Alerting: Real-time monitoring and alerting tools should report any unexpected data events during the data processing. Alerts should go to the right stakeholders for immediate action and resolution. For example, a practice might receive an alert that their most recent data included data elements outside of normal ranges.

Using this multi-layered strategy ensures that all registry data is efficiently ingested, transformed, persisted, and analyzed while maintaining complete fidelity to the original clinical record.

Documentation and Transparency

A registry is only as valuable as it is trusted. Registry stakeholders need to feel confident interacting with their data and interpreting its results. Thus, all data transformations need to be fully documented and approved.

This includes documentation of data processing rules, data validation logic, and measure calculations. This information provides full transparency into how data is used in the registry and helps to increase trust in the clinical insights generated from the registry.

Change Management

Registries will evolve and grow as new priorities emerge – from new data elements being added to changes in measure calculations. These updates increase the value of the registry, but in the process, they must not interrupt current data protections that might affect registry results and further confuse users.

Any data change should go through a systematic change control process to transparently document, test, and have approved any update so its impact is known and widely shared prior to implementation.

Data Logging and Auditing

Health data is sensitive and requires data protections to ensure it is responsibly handled. Furthermore, registry engagement will lead to numerous questions from registry stakeholders on how data was processed and handled.  All data entering the registry must be fully traced from the receipt of data to when it is displayed on reports.

This includes auditable logs of all activity related to data submission, data manipulation, and registry interactions. Each data element underlying the registry is tied to information on the date of receipt, data submission protocols, date and time, data quality results, data transformation steps, and the capturing of raw source data for comprehensive auditing and tracking.

Dedicated Team

Data integrity is best protected when the above processes are controlled by data experts who are intimately familiar with the registry data, goals, and stakeholders. Projects should have a dedicated team of data scientists, data engineers, and data integration specialists who provide long-term, end-to-end registry support through their extensive knowledge of all aspects of the project.

Achieve High Data Quality with Your Registry

Data quality is essential to registries because it establishes trust and value. When your clinicians, researchers, and other stakeholders are confident in the data, they can use it to advance research and improve outcomes.

Understanding Clinical Registry Data Types & Data Sources

Understanding Clinical Registry Data Types & Data Sources

Understanding Clinical Registry Data Types & Data Sources

Building a successful clinical registry is a three-step process that involves acquiring data, transforming it with advanced analytics that identify what matters, and informing clinicians and other stakeholders what and how to improve.

Let’s focus on the first step: acquiring data. For a high-achieving registry, you need to collect a large amount of quality data.

Data for your registry can come from a variety of sources, such as medical records and health insurance claims. Regardless of the source, you need to be sure that the data you’re collecting will both enhance your registry and help you meet your registry goals.

With so much data available, it’s easy to feel overwhelmed. As long as you keep your focus on your goals, you’ll be able to compile a healthy dataset that will serve your organization’s purposes successfully.

Let’s explore the various data sources you can use to build your registry.

In this post we will cover:

  • Clinical Data
  • Patient-Generated Data
  • Cost & Utilization Data
  • Public Health Data

For each, we’ll define what it is, where and how to acquire it, and address any common challenges or limitations of the data source.

Clinical Data

Clinical data is at the core of many registries. It has a powerful ability to summarize clinical experiences and capture information needed to inform healthcare progress.

What Is Clinical Data?

Clinical data helps establish who a patient is – their demographics, family history, comorbidities, procedure and treatment history, and outcomes. The breadth and depth of clinical data opens up opportunities to advance quality improvement initiatives, research, registry-based studies and virtual trials, and other stakeholder activities.

Sources of Clinical Data

The key source of clinical data resides in a patient’s medical record, which collects nearly all care delivery and treatment decision details, regardless of treatment setting. The record resides in outpatient and inpatient facilities and contains information from various ancillary services, such as chemistry labs, pathology or radiology departments, and pharmacies.

Because a patient’s medical record aims to show as complete a picture of the patient as possible, the data abstracted from those records is often more comprehensive than data from other sources. Additionally, medical records follow patients over time, allowing registries to collect and analyze longitudinal information. Medical record data is therefore extremely valuable to budding registries.

Collecting Electronic Health Record Data for Registries

Medical record data can be highly useful, but gaining effective access to that data isn’t always simple.

For clinical registries, successful medical record data acquisition relies on the combined effectiveness of trusted relationships with the record stewards and technologies used to transmit data, such as electronic health record (EHR) systems.

The vast majority of hospitals in the United States use electronic health records (EHRs). There are many different EHR vendors, which can cause difficulties with data sharing. Given the variety of EHR solutions and the varying policies at different sites, having a flexible data integration approach for your registry is key.

Here at ArborMetrix, we integrate with hospital and clinic EHRs and other data systems with minimal impact to IT and clinical resources. Whether healthcare organizations use cloud-based EHR systems, custom-defined layouts, or anything in between, our registry data platform seamlessly collects the necessary data.

We support industry standards for interoperability, as well as custom data formats. Industry standards such as HL7 FHIR® and C-CDA serve to bridge documentation from disparate EHRs in ways that facilitate data aggregation across populations.

Limitations of EHR Data for Registries

Sometimes, data collected in EHRs lacks the clinical specificity needed for quality improvement and research done through registries.

Many EHRs are not configured to collect the details on the high resource intensive conditions. This leaves the data with gaps that prevent it from being used to make impactful changes in care delivery. Consequently, there is the need to supplement the EHR data with specialized and targeted assets required for real clinical improvement.

Some registries use technology-enhanced clinical abstraction to address this and create a more complete dataset. For example, our electronic case report forms (eCRFs) allow registry participants to enter cases on-site or remotely. Once all the required data have been entered into the system, data analytics and dashboard reports are available immediately.

Leveraging eCRFs to fill any gaps in EHR data creates more complete data set.

Patient-Generated Data

Driving high-value healthcare often requires data that goes beyond an exam room or treatment facility. Your registry can generate many healthcare insights through patient-generated data.

What Is Patient-Generated Health Data?

Patient-generated health data (PGHD) “are health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern,” according to the National Learning Consortium at HealthIT.gov. [1]

A few examples of patient-generated data include a person’s health and treatment history, symptoms, biometric data, and patient-reported outcome measures.

Sources of Patient-Generated Data

A core source of patient-generated data comes from patient-reported outcomes (PRO). PRO are health data sourced directly from patients, without any interpretation or alteration by a clinician or other individual.

Because inpatient documentation in the hospital medical record stops when patients are discharged or transferred, and outpatient documentation occurs longitudinally but is sporadic and only captures new data clustered around patient encounters., Adequately capturing patient data outside of the walls of a hospital or outpatient clinic requires PRO.

Collecting PRO Data

A user-friendly PRO solution encourages patients and caregivers to share details of their experiences, quality of life, and outcomes in ways that extend the period of data acquisition following inpatient care. It also fills the gap in patient communication between outpatient encounters.

Important pieces of information that come from PRO include:

  • Type, frequency, and severity of symptoms
  • Nature and severity of disability
  • Impact of condition on daily life
  • Patients’ perceptions and feelings about their conditions and/or treatments

Including PRO in their registries can help healthcare organizations track outcomes, support shared decision-making, develop guidelines, inform best practices, and calculate predictive analytics.

Usually, organizations acquire PRO data through surveys and other technology-enabled digital platforms. Other sources of patient data include wearables, population health measures, and patient registries.

Challenges of Collecting PRO Data

A question that sometimes comes up with PRO data is whether it is accurate. Fortunately, research shows a high correlation between patient-reported data and clinically documented chart abstracted data. [2] This means data collected from patients is accurate and trustworthy.

A second potential challenge in collecting PRO data involves patient engagement. Patients who are engaged in their care can share information and communicate more easily with their caregivers, making PRO acquisition much more effective.

Organizations that have successfully engaged patients have employed a few helpful tactics, including:

  • Treating patients like consumers, by making surveys and feedback platforms responsive and user-friendly.
  • Recognizing the role of technology to help meet patients where they are at with a simple and effective survey experience.
  • Delivering a tailored experience so surveys can be customized to different patient populations and flexible and responsive to their unique needs.
  • Being creative and compelling by giving patients and their families access to other resources, such as a shared decision-making platform.

Collecting PRO Data from Patient Devices

Other patient-generated data sources include passive data collection from patient devices, such as wearables or smart healthcare electronics. These systems often have data connectivity tools, such as APIs, to security transfer patient data points to a registry platform. Real-time, streaming data can further augment information gathered from PRO surveys and clinical data systems, without burdening patients or providers.

Cost & Utilization Data

Many healthcare and clinical registry pursuits are driven by value-based care initiatives — improving outcomes while controlling for costs. These programs require measuring health outcomes against the cost of delivering those outcomes.

What Are Cost & Utilization Data in Healthcare?

Cost and utilization data helps to assess care value for preventing and treating health problems.

Healthcare cost has a specific meaning, but its “interpretation often depends on whose perspective is being considered.” According to the AMA Journal of Ethics, the definition of cost means:

  • To providers: the expense incurred to deliver health care services to patients.
  • To payers: the amount they pay to providers for services rendered.
  • To patients: the amount they pay out-of-pocket for health care services.” [3]

Healthcare utilization “refers to the use of healthcare services. People use healthcare for many reasons including preventing and curing health problems, promoting maintenance of health and well-being, or obtaining information about their health status and prognosis,” according to the Encyclopedia of Behavioral Medicine. [4]

Sources of Cost & Utilization Data

Key sources of cost and utilization data are collected by health insurers, governmental organizations, and public payers. They include claims data regarding patient treatments, as well as public datasets from organizations like the Centers for Medicare and Medicaid Services (CMS) and the Agency for Healthcare Research and Quality (AHRQ).

Payer and regulatory data are especially useful for registries examining the economic aspect of healthcare. Specifically, by including these types of data, registries can more easily perform cost and quality analyses due to the clear records of a patient’s treatment history. Also, especially for government data, the dataset may be larger-scale and include information from a broader patient population.

These data can be acquired from private and public health insurers, and many regulatory datasets are available for public use directly from the sponsoring governmental agencies. For example, we use the Healthcare Cost and Utilization Project (HCUP) dataset from AHRQ and the Limited Data Set from CMS to calibrate propensity-score-based risk adjustment models and to provide external benchmarks for our registry clients such as the Michigan Value Collaborative.

Challenges with Healthcare Cost & Utilization Data

While cost and utilization datasets can be extremely valuable, they also have their limitations.

Dealing with claims data, whether they come from commercial or public payers, can be subject to reversals and adjustments, completion lags, and coding variation and changes that can present considerable challenges when incorporating such data into a new registry.

Overcoming these challenges requires registries to use sophisticated analytic tools and have deep expertise with payer and regulatory data.

For example, we developed and use an elastic episode-based model that lends itself to more appropriate assessments of longitudinal cost. Our model also allows us to more fully assess the outcomes of medical treatments by looking at longitudinal episode cost across a variety of conditions, settings, and care types. This approach captures episodes of care, episodes of illness, and health maintenance episodes to illustrate cost effectiveness across a variety of components. Through this model, we can track care in systematic ways to make comparisons on cost and outcomes.

Public Health Data

There are a variety of factors that significantly influence healthcare outcomes, with clinical care and patient health behavior only representing a portion of the influences. Up to 50% of health outcomes are contributed to community and population health factors. [5]

Consequently, efforts to advance health care requires a full view of all factors that influence health outcomes. This view is critical to understanding the pathways to advance the direction of health care.

Registries can be powerful for providing data to understand the multitude of pathways influencing patient health and using that data to support the development of evidence-based clinical interventions, community guidelines, even advocacy for policies and regulations.

Sources For Public Health Data

There are numerous public health data assets for evaluating community and population health factors. Government data sources are easily available and can layer valuable information on the prevalence and severity of health conditions across different populations in a community. Some sources of public health data include:

  • Behavioral Risk Factor Surveillance System (BRFSS): Includes data at the county and census tract-level related to health outcomes, clinical care, and health behavior.
  • National Center for Chronic Disease Prevention and Health Promotion’s (NCCDPHP) Chronic Disease and Health Promotion Data and Indicators Open Data Porta: Includes numerous data access tools across a wide range of chronic disease data, risk factor indicators and policy measures.
  • National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention – NCHHSTP AtlasPlus: Includes more than 15 years of CDC’s surveillance data on HIV, viral hepatitis, STD, and TB. AtlasPlus also provides access to indicators on social determinants of health (SDOH) allowing users to view social and economic data in conjunction with surveillance data for each disease.
  • National Environmental Public Health Tracking Network: The National Environmental Public Health Tracking Network (Tracking Network) brings together health data and environment data from national, state, and city sources.
  • CDC National Vital Statistics System (NVSS): Includes health outcome data, such as mortality rates by cause of death at the county level.
  • CDC Wide-ranging Online Data for Epidemiologic Research (CDC WONDER): Includes nearly 20 collections of public-use data for U.S. births, death, cancer diagnoses, tuberculosis cases, vaccinations, environmental exposure, population estimates, among other topics.
  • USDA Economic Research Service (ERS) – Food Access Research Atlas: offers census-tract-level data on food access that can be downloaded for community planning or research purposes.

Public health data sources add critical information to registry systems to enable registry stakeholders to best serve the needs of the populations they serve. Public health and clinical information can be blended together within a single registry to have a comprehensive, multi-factorial view of health. This creates a platform that can best fulfill the meaningful and diverse healthcare objectives registries pursue.

Set Your Registry Up for Success

Whether you’re building a condition-specific registry for quality improvement or expanding your registry to address new research priorities, you need to be thoughtful about what data you include. Further, you need the tools, expertise, and technology to ensure your registry can achieve your goals.

Steps to Address Health Inequity: Awareness, Measurement, and Action

Steps to Address Health Inequity: Awareness, Measurement, and Action

Steps to Address Health Inequity: Awareness, Measurement, and Action

There are widespread, longstanding, systemic, and well-documented preventable health disparities among racial and ethnic minorities and amongst those in LGBTQIA+ communities. Minority populations experience a multiplicity of burden in regard to their health including poorer overall health statuses, worse outcomes, and disproportionate diagnosis of health conditions due to the intersection of structural inequity.

Barriers to access, differences in clinical need, and differences in patient treatment exacerbate these poorer health outcomes. Given this unfair burden, healthcare stakeholders have an ethical obligation to change healthcare delivery to address health inequity.

The Council of Medical Specialty Society (CMSS) provides a forum to catalyze healthcare improvements across U.S. medical specialty societies. They held the CMSS Leadership Summit on Equity and Antiracism in April 2021 to focus on methods to reduce health disparities, address issues perpetuated through dominant culture dynamics, and improve equity within medical specialties. Key objectives included:

  1. Increasing awareness of longstanding, health inequity and health disparities.
  1. Identifying and measuring disparities.
  1. Forming action to address disparities.

In this post, we share key learnings from the summit and expand on the role of clinical registries in this imperative work.

Increasing Awareness of Health Inequity and Disparities

The initial step to increasing awareness around inequity and the role of bias in health disparities is acknowledging the extent to which these systematic processes harm health outcomes.

These processes permeate healthcare at every level: from the language we use to talk about racism and bias, to algorithms built on racist data that are used to perpetuate myths about genetic and physiologic differences between patients of different races [1], to the exclusion of minorities in medical schools, to the lack of provider diversity, and ultimately in patient care.

This work begins with the way we speak about bias in general, and racism specifically. The language used to describe processes that exclude people who do not conform to dominant sociocultural norms indemnifies the very groups that perpetuate these processes. [2] This camouflages how detrimental bias is to both wealth-building and health outcomes. In changing how we talk about persons that do not hold power and influence in society, firstly we recognize that there is a difference in treatment based on power dynamics and we open the door to changing that dynamic.

Increasing awareness must be a systematic process occurring at every level of healthcare. This begins with recognizing race as a social construct that has physical implications [3] and treating social data with the same gravity as traditional medical data and enacting policy to support the collection and bidirectional transfer of these data. It also includes creating psychologically safe environments that encourage allyship amongst clinicians of dominant sociocultural groups and marginalized people. [4]

To dismantle racism and bias within healthcare, we must prioritize diverse perspectives amongst clinicians and the healthcare delivery system as a whole and normalize diversity, equity and inclusion initiatives. [5] To increase awareness surrounding health inequity, we are educating and training our team, focusing on ways to integrate the use of social data in registries, and supporting our clients in doing the same. We are also developing partnerships with and learning from experts who can help accelerate our work in measuring and improving health equity so that we can in turn support organizations like CMSS.

Identifying and Measuring Healthcare Disparities

Advancing health equity work in the pursuit of reducing and eliminating health disparities involves detection and measurement in addition to understanding the determinants. Clinical data registries can be instrumental in these tasks by assessing the causes and impact of health disparities on patient outcomes.

According to the Centers for Disease Control and Prevention (CDC), only 10 percent of factors affecting premature death are related to clinical care, and 30 percent of factors relate to genetics. This means that 60 percent of factors impacting premature death are based on a combination of social/environmental factors (20 percent) and behavior (40 percent). Ensuring the delivery of high-quality, patient-centered care requires understanding both the needs and circumstances of the populations served. Registries can collect and analyze patient, clinical, social, and geographic data to provide a more complete view of the type of advantages or disadvantages certain patients have in attempting to achieve optimal health. [6]

Fully identifying and measuring disparities and their root causes necessitates data that goes beyond clinical data elements. This includes the prioritization and comprehensive collection of social data such as:

  • ICD-10-CM codes (Z55-Z65) that detail patient social risk factors
  • Utilizing public health datasets such as the American Community Survey, Behavioral Risk Factor Surveillance System and CDC National Vital Statistics System
  • Patient-reported outcomes data

Integrating comprehensive social data elements in the United States Core Data for Interoperability (USCDI) and enforcing mandatory collection within electronic health records (EHRs) are two steps that make up a meaningful process in measuring the extent and impact of social determinants of health.

Forming Action to Address Healthcare Disparities

Data and measurement are only as valuable as it is usable and actionable. Advancing health equity begins with understanding the sources of disparities, but it can only be achieved through effective action.

Once disparities are assessed and the causes are understood, impactful interventions can be developed using data-driven approaches.

However, implementing informed interventions is only the first step. Next, interventions must be evaluated for effectiveness, changed to address problems as necessary, and finally, best practices must be disseminated to scale.

Leveraging Clinical Registries to Improve Health Equity

To promote optimal patient care, it is our ethical obligation to understand the prevalence and severity of social and health inequities, utilize comprehensive data collection and analysis, and take informed action to end them. We must evaluate and implement interventions that effectively improve outcomes for the most vulnerable populations, which in turn improves conditions for all populations.

Clinical data registries are valuable tools for pursuing these efforts. Registries can collect a high-volume of data from different sources and deliver high-quality, data-driven insights. Reduction and eventually elimination of health disparities require data to detect and measure impact, identify the complex and interconnected determinants, and inform interventions and best practices.

The Top Takeaways From the 2021 ONC Annual Meeting: Actionable Data as the Great Equalizer

The Top Takeaways From the 2021 ONC Annual Meeting: Actionable Data as the Great Equalizer

The Top Takeaways From the 2021 ONC Annual Meeting: Actionable Data as the Great Equalizer

The Office of the National Coordinator for Health IT (ONC) held its first fully virtual annual meeting last month with a focus on utilizing healthcare information technology and data to strengthen the United States healthcare system, address patient needs, and improve outcomes.

The U.S. Department of Health and Human Services (HHS) – the entity over both the Centers for Medicare and Medicaid Services (CMS) and the Centers for Disease Control (CDC) – and ONC have successfully aligned priorities according to their strategic plan for 2018-2022. [1] Included in this plan are the following goals:

  • Reform, strengthen, and modernize the nation’s healthcare system.
  • Protect the health of Americans where they live, learn, work, and play.
  • Strengthen the economic and social well-being of Americans across the lifespan.
  • Foster sound, sustained advances in the sciences.
  • Promote effective and efficient management and stewardship.

This year’s conference sought to fine tune national priorities, viewing them through the lens of COVID-19 and health inequality. The conference addressed HHS goals through the following six themes:

  1. Leveraging healthcare information technology to address inequities and social determinants of health.
  1. Propelling standards use in healthcare IT to improve interoperability of health data and address new data collection modalities.
  1. Modernizing healthcare data collection to drive research and innovation.
  1. The importance of using regulation and policy to support healthcare IT infrastructure.
  1. Using health IT in public health emergencies.
  1. Reducing clinician burden through the use of health IT.

Addressing some of the greatest challenges in health means using the most innovative technology to advance care and improve outcomes.

While the current health IT ecosystem is surrounded by complexity and compartmentalization, new standards and technologies are creating a solid backbone to unlock greater potential impact. Organizations such as medical specialty societies can bring together diverse healthcare stakeholders to use technology and data science to collect and analyze data and advance health.

This work becomes increasingly more efficient and valuable when it is supported by technology that is aligned with the innovative standards showcased by the ONC.

Leveraging Healthcare Information Technology and Using Social Determinants of Health Data to Address Inequities

The adage that “you can’t fix what you don’t measure,” is crucial to health information technology’s role in fixing social inequity in healthcare.

Social data is exceedingly responsible for health outcomes and healthfulness. Research has shown that genetics are accountable for approximately 20 to 30% of health outcomes and medical care attributable to about 10 to 20%. This leaves roughly 70% of health outcomes attributable to behavior and social determinants of health. [2]

A person’s ZIP code is the most dominant factor in determining health. It determines access to care, access to fresh and healthy food, environmental risk, and it ultimately determines life expectancy. Now, it also means having access to information through technological innovations such as broadband internet. This has been magnified during the COVID-19 pandemic, with at-risk populations unable to access appropriate information to get the care they need.

Currently, there is a divide between health data and community needs. Until now, health information technology has disregarded its role in perpetuating systemic inequality. Often social data such as class, race, rurality, and language are missing from the electronic health record (EHRs). These data have been either not measured correctly or ignored, contributing to adverse health outcomes in poor, rural, and minority communities.

Unreliable sources of social data coupled with the lack of centralization and standardization of data constrain accuracy. This leads to underserving vulnerable populations. However, the COVID-19 pandemic and the current social justice movement is creating a paradigm shift in the way race, class, and health are being examined and addressed.

Healthcare information technology has the ability to set the stage for creating an equitable health environment and can do so in four key focus areas:

  1. Incorporating standards through FHIR and nonproprietary open APIs.
  1. IT infrastructure through health information exchanges and widespread broadband access.
  1. Public health policy and funding for digital transformation and interoperability.
  1. Implementation of practices to promote health equity.

Promoting the collection and use of social data and using it to predict and rectify adverse health outcomes are necessary to drive change.

Historically, we have looked to electronic health records to be the primary information source for health data. These systems are transactional in nature and do not serve as the complex fusion network of data needed to address complex societal problems.

Moving forward, data must be pulled dynamically from different sources to adeptly address changing social needs. Healthcare IT can both challenge and change health inequity through thoughtful collection of data beyond the electronic health record. Data collection systems should incorporate “heath equity by design,” where mechanisms to achieve equity are engineered into systems for collecting and transforming health information.

Ultimately, the health consumer must be central to all innovation in health information technology. Social health information exchange can leverage existing infrastructure and integrate social determinants of health tools and applications to make social health data both available and interoperable. Funding for digital transformation, state block grants for interoperability, federal information consent privacy rules that are aligned with the needs of vulnerable populations are all necessary for achieving health equity by design.

Using Health IT in Public Health Emergencies

The COVID-19 pandemic has demonstrated the fragility of our healthcare system and has forced all aspects of healthcare, including health information technology, to revolutionize under duress. The past year has pointed out our lack of a standard, scalable case-based surveillance system that allows us to adapt to new widespread threats such as COVID-19.

Currently, electronic health records do not have the capability to incorporate data from alternate data sources such as pathogen diagnostics systems. This was a hindrance in surveillance for COVID-19.

Relying primarily on the EHR for health data limits our ability to respond to threats with agility. The pandemic has taught us that much of healthcare takes place in nontraditional spaces, and our information systems must adapt to this.

COVID-19 testing and vaccination occurs in a myriad of facilities outside of traditional healthcare spaces and we miss out on valuable data because we do not have the ability to collect information from these sources. Furthermore, people who are treated in these nontraditional spaces are typically people who suffer most from health inequities.

Propelling Standards Use in Healthcare IT to Improve Interoperability of Health Data and Address New Data Collection Modalities

The 21st Century Cures Act (Cures Act) was initially set to go in effect in 2020, but the COVID-19 pandemic took precedence and has delayed implementation until now. This groundbreaking regulation provides an opportunity to demystify myths surrounding interoperability and make data liquidity a priority within healthcare.

In a Stanford poll of primary care physicians, 67% indicated that they would like to see interoperability and system-wide information sharing be addressed as the most pressing healthcare issue we face in the next decade. [3]

Significant challenges lie in making health data completely interoperable. While we may focus on the creation of electronic data standards, the adoption of electronic modalities of collecting and transferring data have not reached complete saturation. Standards must incorporate all modalities and address settings where there is not wide adoption of EHRs such as in rural settings. While there is emphasis on electronic data collection, standards should be applicable to analog forms of data collection, as well.

Integration of social determinants of health data, device and mobile data, and traditional patient data from EHRs is fundamental to propelling our healthcare system into the future. Without integration, there is significant financial and human life cost to patients in the way of lack of access to care, needless repeated diagnostic care and inappropriate care. Standards will promote integration and bi-directional data exchange, allowing a deeper understanding of the continuum of patient care.

Modernizing Healthcare Data Collection to Drive Research and Innovation

Electronic health record systems primarily serve as a container for health data, rather than a tool for improving health and healthcare.

All levels of healthcare, including local and national levels, struggle to obtain timely, accurate, and actionable data from electronic health records. The current structure for data collection and exchange requires clinicians to spend less time with patients and more time interfacing with electronic systems that were not purpose-built, which results in omissions and errors. The structure is not scalable without significant resources and effort.

This mismatch between need and function fosters delays and a loss of data precision, a loss of trust amongst providers, and creates an unsustainable system at increased cost.  Current healthcare data infrastructure is reactive, siloed and inefficient.  To respond to future threats, the infrastructure must be transformed into a system that is predictive, agile, and focused on sharing and understanding timely data and resources.

To overcome these barriers, health agencies such as the CDC and other partners are looking to utilize API gateways to support streamlined, coordinated, and interoperable public health reporting. Ongoing modernization includes leveraging cutting-edge analytics and data visualization capabilities to seamlessly integrate data from disparate sources.

Health data is increasingly being generated by sources other than patient visits and electronic health records. With the advent of personal health devices such as continuous glucose monitors and fitness wearables, patients are increasingly creating and monitoring their own health data. By 2025, there will be over 180 billion gigabytes of sensor data generated by Internet of Things devices, much of this data related to health and fitness. [4, 5]

As the technology for these devices matures, this data has far-reaching implications for healthcare, including remotely monitored condition management, chronic condition self-management, faster diagnosis, earlier interventions and medication compliance. Incorporation of sensor data in healthcare can potentially streamline resource use, increase quality and lower cost.

The COVID-19 pandemic created an increase in telehealth visits. Having remote access to diagnostic sensor data will transform the utility of telehealth, creating the ability to easily provide care in rural and nontraditional settings.

Currently, most sensor data do not adhere to standards-based protocols and unless the data is unified and interoperable, it will continue to create vertical silos which creates superfluous complexity in data flow. [5] Sensor data will be of limited utility and is unlikely to reach the saturation needed to be viable unless standards-based interoperability is built in from the start.

While technological innovation is important to modernizing healthcare data collection, it cannot be done without a robust public health workforce. In addition to making data interoperable, we must focus on reskilling and retaining a vigorous data science workforce including public health informaticists to carry out 21st Century public health work. This requires significant funding and investment into public health human resources.

The Importance of Using Regulation and Policy to Support Healthcare IT Infrastructure

Policy and regulation can be drivers of public health change at the local, state, and national level.

The Cures Act lowers the barrier for data liquidity through information blocking provisions, availability of nonproprietary APIs, support of bi-directional data exchange for data flow amongst multiple entities, and transparency for patients through the Trusted Exchange Framework and Common Agreement (TEFCA). TEFCA outlines conditions to be met to promote secure exchange between providers, health information networks (HINs), and patients. This puts the patient at the center of healthcare IT and promotes ownership and patient management of data.

Required implementation of secure, standards-based application programming interfaces (APIs) by providers will make patient data readily available to aid in making timely decisions about patient care. This can be leveraged to power clinical decision support tools such as CDS Hooks that work in real-time to support clinician workflow.

The Information blocking provision in the 21st Century Cures Act prohibits health IT developers of certified health IT, health information networks, health information exchanges, and health care providers from engaging in practices that interfere with access, exchange or use of electronic health information. These provisions promote patient-centered care and allow patients greater access to their own health data and prohibits providers and health information technology developers from holding data ransom for financial or personal gain.

Lastly, new office and outpatient evaluation and management (E/M) coding and guideline changes improve clinician workflow, infrastructure and ultimately patient experience by reducing administrative burden of documentation. [6] The streamlining of coding and documentation requirements for evaluation and management codes and elimination of “documentation for the sake of documentation” will lead to payment for patient visits being resource-based rather than time-based.

Ultimately, policy is necessary to drive standard, widespread change across healthcare. Regulation has the power to drive data integration, a fundamental need to the health of this country. Proprietary systems have reason to remain siloed to protect economic interests and are unlikely to change unless forced.

While there have been significant improvements with the advent of the Cures Act and other guideline changes, additional work is needed to create national frameworks and best practices for choosing and implementing healthcare IT. Currently, the process for selecting IT is highly fragmented, and organizations rely on data that does not meet public health needs. This leads to increased cost and decreased functionality at the expense of patient care.

Reducing Clinician Burden Through the Use of Health IT

According to the same Stanford poll previously referenced, roughly 70% of primary care clinicians state that despair and burnout are at an all-time high and the current healthcare IT infrastructure contributes to this. [3]

Clinician burden is realized in the amount of time spent on activities that are not direct patient care. Clinicians regularly voice their concerns about administrative burden by describing laborious overdocumentation “note bloat” and poorly structured electronic health record systems, nicknamed “five clicks to crazy.” These inefficient systems harm clinicians and patients alike as more time is spent managing the process of healthcare rather than providing healthcare.

When the technology revolution finally reached healthcare, it was intended to streamline processes and allow clinicians to spend more time with patients. While electronic health records have been demonized as the root of all evil in clinician burnout and medical errors due to missing or erroneous information [7], it is well documented that the same administrative burden occurred in paper-based healthcare administration [8].  Instead of utilizing healthcare IT to transform healthcare, we simply traded the fragmented, broken system of paper for a fragmented, broken system of mouse clicks.

Clinician experience can be improved through the new E/M guidelines that reduce the documentation needed for a patient visit. But there are technological innovations that can increase efficiency, reduce the amount of time spent documenting patient information, and improve health outcomes such as using note dictation and workflow automation principles such as natural language processing, robotics, and, machine learning.

Healthcare presents a complex management task, and workflow automation can streamline repetitive processes, enabling high-quality, safe, secure, and effective patient-centered care. Automation of rote tasks can reduce errors and decision-making burden. This allows clinicians to devote their time to specialized patient care and allows greater engagement for decision support. This requires standards adherence since automation requires repetition, clearly defined variables for modeling and clear roles and responsibilities for actors.

ONC is preparing its policy and development agenda on modern computing and is committed to supporting clinicians through standards and processes, such as workflow automation. To reduce burden for all and produce high-quality healthcare, these processes must preserve the following:

  • Measurable goals
  • Involvement of stakeholders in design
  • Preservation of human judgment
  • Avoidance of adverse outcomes
  • Scalable approaches across settings
  • Incorporation systems for reducing health inequity
  • Improved efficiency, savings, and quality

Actionable Data as the Great Equalizer

We need comprehensive, interoperable data to strengthen the nation’s healthcare system, to reduce inequity, and improve health outcomes. This is only possible through a complex fusion network of data and meaningful analytics.

As a healthcare analytics organization, we embrace and drive adoption of standards and technologies that make research and quality improvement accessible and successful. We support the vision of making actionable data the great equalizer through creating fusion networks of registry data that identify improvement opportunities, investigate what is driving outcomes, and promote future forecasting through advanced predictive modeling​.

Together, we can create the healthcare system of the future by creating interoperable networks of actionable data.

How to Define Success for Your Clinical Registry

How to Define Success for Your Clinical Registry

How to Define Success for Your Clinical RegistryMedical specialty societies and patient foundations aim to improve healthcare. Clinical registries are one of the greatest instruments that guide how best to do that.
Clinical data registries are also called patient registries and disease registries. Professional medical specialty societies tend to use the term clinical data registry, while research and patient foundations and government organizations lean toward patient registry. Because disease registries are condition-specific, that term is often used among industry organizations.

A registry transforms real-world healthcare data into real-world evidence. The purpose is to evaluate and improve outcomes for a patient population defined by a particular condition, disease, or exposure.

Specifically, registries track information about the health status of patients and the care they receive. They bring together large data sets and analyze trends or patterns in treatments and outcomes to help inform best practices, guidelines, and treatment decisions.
Over the past 40 years, registries have become a central resource for medical societies, physicians, and, increasingly, for patients too. The majority of the 46 medical society members of the Council for Medical Specialty Societies (CMSS) manage patient registries. These groups make an aggregate investment of approximately $500 million over five years, according to CMSS. [1]

So what impact do these registries have on healthcare? How do you know if your registry is measuring up to what a modern registry is capable of achieving?

What Is a Successful Registry?

To put it simply, a successful registry measurably improves patient care.

It could be met through quality improvement programs, clinical research, regulatory reporting, or informing health policy decisions. It could be done using huge volumes of data from a large set of physicians, health systems, and practices — or from a small, yet statistically significant, group of targeted participants. Either way, the first step to measuring registry ROI is knowing what you want to achieve and identifying how, where, and when you will measure your own results.

What Are Key Metrics for Measuring Registry Success?

There are several key metrics you can use to determine your registry’s performance and value.

We separate these key metrics into performance metrics and results. Together, these tell a data-driven story of how well your registry is doing at achieving its objectives. They also provide useful data to inform future registry direction, enhancements, and changes.

Registry Success: Performance Metrics

  • Number of participants
  • Rate of participation growth
  • Volume of quality data received
  • Rate of data growth
  • Valuable feedback from participants

These performance metrics help determine if your registry is off to the right start. Importantly, they are not an endpoint. Focusing on them alone will not guarantee you achieve your desired results.

For example, some registries have hundreds of sites participating and data for millions of patients or cases, but little to show in the way of results. They are focused on enrolling sites and getting data first, and the purpose value of the registry second.

You need to see the outputs — the results — of all of the inputs to evaluate your success.

Registry Success: Measurable Results

  • Level of registry engagement (sessions, report views, dashboard interactions)
  • Number of published research articles using registry data
  • Rate of published research using registry data
  • Development of evidence-based care guidelines
  • Trackable improved outcomes from compliance with evidence-based guidelines
  • Using registry data to shape healthcare policy and advance health equity
  • Volume and types of data that can be repackaged and commercialized for industry

These results fall into five primary categories: Engagement, Research, Quality Improvement, Population Health, and Business Outcomes.


User engagement metrics are a primary output of your registry. They signal to you whether those who have signed up for your registry are finding it useful.

You can use engagement metrics to understand how physicians and administrators are using your registry.

  • Are they interacting with clinical outcome reports to identify best practices?
  • In what ways are they drilling into reports to uncover trends and establish guidelines?
  • When are they using predictive analytics and outcomes calculators for point-of-care and shared decision-making?
  • How are they leveraging patient-reported outcomes (PROs) data?

When data demonstrate methods for improving patient care, physicians, researchers, and end users are easily engaged. They actively use the registry to define new and well-supported best practice guidelines and to support high-quality research.

Administrative dashboards are useful tools for tracking registry engagement by presenting live data across a variety of metrics – from report utilization to end user support requests. The dashboards provide critical information to identify best practices and drive further registry enhancements.

Patient Engagement

Engagement metrics do not only apply to physicians and administrators. They also apply to patients and family members who participate in patient-reported outcomes surveys and other patient engagement programs.

  • What percentage of patients have responded to PRO surveys?
  • How are patients and physicians using information to support shared decision-making?
  • What resources are patients accessing through their engagement dashboard?


Many registries leverage high-quality data and analytics to support research.

The results to measure here include:

  • The frequency and volume of impactful publications using data-driven evidence from your registry.
  • The speed to value your registry delivers in supporting active research. For example, ArborMetrix customers publish research in academic journals within 12 months of implementation go-live.
  • The number of clinicians, researchers, or industry partners using your registry data as a foundation for registry-enhanced or registry-based clinical trials or post-market surveillance studies.

As soon as your registry becomes productive, spread the news. Disseminate findings early and often. When physicians, researchers, industry, and patients hear the latest, they will engage, participate, and contribute.

Here are a few ideas for sharing research among your community:

  • Make a library of research publications using registry data available on your registry website and call out highlights on your home page. These are examples of research using data from our customers’ registries.
  • Include registry-based research highlights in your member or community newsletter.
  • Educate your member community and stakeholders about the findings of registry-based research during annual conferences, scientific meetings, and webinars.
  • Share registry-based research publications on your social media profiles.

Quality Improvement

Typically, measurable quality improvement is achieved through a combination of the registry and collaborative efforts across participating sites and physicians. Many of our medical specialty society registry customers adopt the Learning Health System model to achieve lasting quality improvement.

Quality improvement results include:

  • Development of evidence-based care guidelines.
  • Adherence to evidence-based guidelines and best practices.
  • Trackable (and often published) improved outcomes from compliance with evidence-based guidelines.

Population Health

Population health initiatives allow you to improve health outcomes across all populations. This includes using registry data to identify needs, act to address outcome gaps, and evaluate population health initiatives to inform best practices. There are many key outputs that indicate how well your registry is advancing population health.

Evidence of success with population health initiatives includes:

  • Increase in engagement with initiatives aimed at addressing health disparities.
  • Collaborative sharing of best practices to advance health equity.
  • Increase in use of patient social risk factor data to improve care decisions.
  • Growth of population health insights used to shape healthcare policy.


The reality of running a registry is that it also affects your business operations. Whether you are a medical specialty society, patient foundation, or other healthcare association, your registry requires a strategic investment and a sustainable model to make it successful over the long term.

Business results for clinical and patient registries include:

  • Amount of fees received from participating sites or physicians
  • Amount of funding received from public and private grants
  • Amount of revenue received from other sources, like industry partners who either sponsor part of your registry program or purchase data and reports

Successful clinical registries are designed with all of this in mind.

  • How will your registry provide enough value and day-to-day utility to justify member or site fees, and/or how will you design a program that provides value to other industry stakeholders?
  • How will your registry enable you to leverage partnerships that remove the financial burden for ongoing maintenance and innovation?
  • What role does or could your registry play in supporting industry-driven research and development to track, document, and improve the safety and effectiveness of medical devices and therapies?

How Do I Achieve Success With My Registry?

There are a number of decisions—often made early on—that have a direct effect on the long-term success and sustainability of a registry. The technology you use to design and build the registry, your participation model, and data quality all affect your registry’s trajectory.

Before diving into it all, know this:

The single most important factor to registry success is knowing where you want to go. As the adage goes, “Begin with the end in mind.” Start there. Let your goals determine who participates, when and how you measure performance, and what data you use.

We take this approach with our customers by asking what you want to achieve and guiding you through a step-by-step process to design a registry with your goals in mind.

3 Technology Essentials for Clinical Registries that Improve Care

3 Technology Essentials for Clinical Registries that Improve Care

3 Technology Essentials for Clinical Registries that Improve Care

Improving care seems like a monumental undertaking. It sounds expensive. It sounds overwhelming. But the good news is, if you have a clinical data registry, improving healthcare delivery and advancing research has never been more achievable.

Here are three technology essentials to consider when designing, building, and managing a clinical registry that will get you moving closer to measuring and improving care.

Using Healthcare Analytics for Clinical Registries

Running a successful clinical registry, also called a patient registry or disease registry, is a significant undertaking that requires the right technology and the right approach. Today’s most valued and most effective registries use healthcare analytics that go beyond data hoarding and warehousing, and set the stage to play a crucial role in advancing care and research.

Specifically, registry technology should cover three essentials:

  1. Integrate: Efficiently and securely acquiring and validating healthcare data from various sources.
  2. Identify: Advanced analytics are used for building trust in the data and uncovering drivers of outcome and variation.
  3. Inform: Delivering insights via interactive reports and tools that inspire data-driven action.

3 Keys of Registry Technology

1. Integrate: Collecting and Validating Data with Clinical Registry Software

Successful data acquisition creates a complete (and sometimes large) data set. Key aspects of this step include data sources, data integration technologies, and validating data for quality.

Registry Data Sources

Registries that achieve measurable results rely on a diverse and complete data set. They harmonize data from many sources, including:

  • Clinical data from electronic health records (EHRs) and case report forms (eCRFs).
  • Patient-generated data from patient-reported outcome (PRO) surveys.
  • Cost and utilization data from claims and public datasets.
  • Public health data from various government data sources.

Data Integration Technologies

In order to drive participation and value, registries must use efficient electronic data collection methods that ingest, validate, and transform data into a high-quality asset.

  • Ingest: Deploy standards-based, vendor-specific, and custom connectors to ensure interoperability across EHR and other systems and sources.
  • Validate: Ensure data completeness, correctness, and accuracy.
  • Transform: Assemble and organize data into a single consistent representation.

Importantly, successful registries should base their data acquisition strategy on their goals and purpose – not just the data most readily or easily available to them. Focusing on what you need rather than on what you can get will point your registry in the most successful direction.

2. Identify: Analyzing Healthcare Data to Uncover Opportunities

Applying advanced analytics ensures data and reports are credible and trusted. When it comes to building engagement in trusted data, analytics make the difference. Here are a few specific methodological and statistical tools your registry should use.

Risk Adjustment

Risk adjustment is a process that corrects for the severity of a patient’s illness. Adjusting for risk levels the playing field and ensures that comparisons of hospitals and clinicians are fair and accurate. For example, our registries use scientifically and clinically validated statistical risk models to evaluate – in real-time – all factors related to each outcome. This builds trust.

Reliability Adjustment

In healthcare quality measurement, when sample sizes for a hospital or clinician are small, the observed rates or rare outcomes may be due to chance and should be considered less precise than rates based on larger sample sizes. Reliability adjustment is a statistical technique that is designed to isolate the signal and reduce the noise in your dataset. Specifically, our methods will determine whether outcomes are due to chance or true differences in quality.

If you want to learn more about risk and reliability adjustment, check out this post: What Are Risk and Reliability Adjustment and Why Do They Matter?


Healthcare benchmarking means comparing a hospital or practice, or clinician, to others. Your registry should provide benchmarking based on patient characteristics, condition, geography, facility type, or other meaningful groups. It’s smart to also blind these benchmarks to protect privacy while still supporting effective measurement.

Peer Group Definition

Peer groups are an important part of benchmarking. Look for reporting and peer benchmarking capabilities at the clinician, practice, multi-practice, multi-facility, and organization levels.

If you want to learn more about benchmarking in healthcare, check out this post: What Is Healthcare Benchmarking?

Sample Peer Group Benchmarking Report

3. Inform: Delivering Insights and Leveraging Evidence

While leveraging analytics and data science unleashes the value of your registry data, making those insights and reports accessible, engaging, and easily understood unlocks exponential value for your organization.

To make those insights as valuable as possible, aim to prioritize the user experience. You’ll want to think of your registry participants and end users as people who have a wide exposure to a variety of digital experiences. That means that when it comes to engaging with your registry, participants:

  • Expect the same technological experience as their favorite apps or websites.
  • Share and/or use information when it is convenient and compelling.
  • Need their experience to be valuable.

When you focus on user experience, it ensures that every person can get value from the data, whether their needs are simple or complex. We’ve designed reporting features in our registry platform to drive engagement and support data-driven decision making. A few of the specific reporting features we use in our registry platform to drive patient engagement and support analysis include:

  • Intuitive Views: Analyze data at the patient, provider, or site level, either individually or in aggregate.
  • Flexible Interaction: Explore data at a deep clinical level by creating custom patient cohorts, analyzing case-level details, and drawing correlations within and across diagnoses.
  • Personalized Visualizations: Perform complex analyses and create powerful visualizations using embedded Tableau.
  • Custom Queries: Extract raw or analyzed data for special research projects or reports.

If you want to learn more about our user-centered approach to healthcare analytics software design, check out this post: Designing Healthcare Analytics to Engage Clinicians.

Registry Analytics Checklist

How do you use analytics to ensure measurement is fair, trusted, and engaging?

Download this checklist to help you evaluate your registry analytics.

Other Interactive Experiences for Registries

Beyond reporting and data visualizations, registries can harness emerging tools and technologies to add utility and value to their programs.

Patient Engagement and Patient-Reported Outcomes

Patient data informs a more complete view of quality, smoothing the gaps between clinical visits documented in the EHR, and helps you achieve even greater results. Modern patient-reported outcomes surveys and engagement tools allow you to collect the right data from the right patient at the right time.

Clinical Decision Support

Bringing together clinical data and patient-reported outcomes creates a powerful asset. Outcomes calculators use predictive analytics to intelligently combine registry data to predict outcomes, risks, and results of procedures, therapies, and treatments. This enables physicians to make data-driven decisions and empowers patients to engage in informed conversations about their outcomes.

Putting It All Together

These three essential steps to a successful registry are complex but achievable. ArborMetrix has built strong partnerships with our clients that put acquiring, organizing, analyzing, and acting on data at the center of their registry strategy.

Our clinical registry platform and our strategy and partnership, put these goals in their grasp. We bring our proven strengths in delivering clinical data registries that lead to scientific findings and actionable insights that have a real impact on real people.

The Basics of Clinical Data Registries

The Basics of Clinical Data Registries

The Basics of Clinical Data Registries

Healthcare’s digital transformation is long underway, yet still lagging. Calls for better and more efficient care, therapies, and outcomes have never been louder and more insistent. The role of clinical data registries has never been more significant.

It’s necessary to understand the importance of registries and how they make sense out of large volumes of disparate healthcare data to measurably improve care and advance research.

Let’s cover the basics:

  • What Is a Clinical Data Registry?
  • Types of Clinical Registries
  • Purpose and Uses of Clinical Data Registries
  • The Value of Clinical Data Registries
  • Examples of Quality Improvement with Clinical Data Registries
  • Example of a Medical Device Registry
  • Getting Started with Clinical Data Registry Software Solutions

What Is a Clinical Data Registry?

A clinical data registry is an interactive database that collects, organizes, and displays healthcare information.

Clinical data registries are also sometimes called patient registries and disease registries. Professional medical associations and specialty societies tend to use the term clinical data registry, while research and patient foundations and government organizations lean toward patient registry. Because disease registries sound condition-specific, that term is often more popular with industry.

Regardless of the name, the purpose of a data registry is the same: to evaluate and improve outcomes for a population defined by a particular condition, disease, or exposure.

Specifically, registries use observational study methods to collect and harmonize data about the treatment, outcomes, and well-being of patients who receive care over time. They aggregate large data sets and analyze trends or patterns in treatments and outcomes.‍

Registries can serve many purposes and provide value for a variety of healthcare stakeholders. For example:

  • Physicians and other healthcare professionals use registries to evaluate available treatments, procedures, and therapies, and to understand how patients with different characteristics respond to various treatments.
  • Medical device manufacturers and pharmaceutical developers use registries to track and understand the effectiveness, safety, and value of medical devices or therapies and drugs entering or on the market.

The number of registries has grown over the past several decades as healthcare information has become digitized. Yet despite their increase in use and significance, registries face real challenges in establishing the participation, engagement, and utility needed to drive their sustainability.

Modern clinical data registries address these limitations by going beyond data collection and data warehousing. They rely on advanced analytics and data science to transform data into meaningful insights that are useful, usable, and used by a variety of stakeholders to achieve a desired outcome.

Types of Clinical Registries

Clinical registries come in many different forms. The type of registry depends on the organization managing or sponsoring it, and the patient population, disease, condition, or treatment it examines.

Although registry goals and purposes vary, when designed with the right approach and built with the right analytics technology, they can measurably improve care.

What Is a Patient Registry?

A patient registry, also called a disease registry, tracks information about the health status of patients and the care they receive for a specific disease or condition. Patient registries bring together data to evaluate longitudinal outcomes, best practices, treatment guidelines, and to support research and therapeutic development.

A growing number of patient foundations and pharmaceutical organizations are establishing patient registries to study the treatment of rare diseases and conditions, such as hemophilia and other genetic diseases.

What Is a Specialty Registry?

Specialty registries are clinical registries focused on advancing care and outcomes across a medical specialty or subspecialty, such as pathology, sleep medicine, surgery, and trauma medicine. These registries often aim to develop guidelines and decision support tools, accelerate research, and advance care through collaborative quality improvement.

What Is a Population Registry?

A population registry is more broadly focused across entire patient populations and spans both specialty care and specific diseases and conditions. These registries aim to capture the health, well-being, diagnostic, treatment, and outcome data for every patient within a population defined by demographics (age, gender, or other social determinants), geography (state, region, country and including like Health Information Exchanges and within Health Departments), or disease or condition (diabetes, cancer).

What Is a Medical Device Registry?

A medical device registry is focused on tracking the effectiveness, safety, and value of medical devices. Device registries come in several forms. Medical specialty organizations may collect data on various devices used for procedures or conditions, as part of their clinical data registries. Medical device companies establish registries and use registry data to support post-market surveillance.

What Is a Payer Registry?

A payer registry is established by a healthcare payer focused on measuring and improving value by advancing outcomes and reducing costs. Payer-sponsored registries are often organized across a specific geography or region, and by specialty – surgery, urology, emergency medicine, etc.

Purpose and Uses of Clinical Data Registries

Healthcare organizations such as medical specialty societies, patient foundations, pharmaceutical companies, and medical device manufacturers establish registries for many purposes and uses.

Clinical Data Registry Purposes

  • Quality improvement
  • Benchmarking
  • Clinical research
  • Clinical effectiveness
  • Cost effectiveness
  • Device surveillance
  • Treatment surveillance
  • Population surveillance

Clinical Data Registry Uses

  • Decision support
  • Guideline development
  • Measure development
  • Regulatory and public reporting
  • Value-based reimbursement and payment
  • Patient engagement
  • Post-market surveillance
  • Registry-based clinical trials
  • Education development
  • Certification and accreditation

The Value of Clinical Data Registries

Clinical data registries are valuable when they measurably improve care and achieve results. Examples of this in action are advancing research, establishing and evaluating guidelines, or managing and reducing costs.

Achieving value with a registry happens when:

  • Physicians and providers use high-quality, data-driven insights to better understand expected outcomes, make evidence-based decisions, and share best practices.
  • Patients share timely and personal data about their condition and outcomes and gain a greater understanding of their care that leads to informed shared decision-making.
  • Researchers and developers use registry data as the foundation for registry-enhanced or registry-based research, clinical trials, or post-market surveillance studies.

Examples of Quality Improvement with Clinical Data Registries

In 2019, the Michigan Surgical Quality Collaborative (MSQC) used registry data to generate knowledge in the form of procedure‐specific opioid prescribing guidelines.

The result?

Post-surgical opioid prescribing dropped by 30%, and post-surgical patient opioid consumption dropped by 50%, according to a paper published in the New England Journal of Medicine. There also was no change in patient-reported satisfaction with care and pain in the week after surgery.

The Pediatric Cardiac Critical Care Consortium (PC4) aims to improve the quality of care for pediatric heart patients through a clinical data registry that allows hospitals to evaluate their own outcomes and learn best practices.

Their efforts are paying off.

Eighteen hospitals significantly reduced mortality and improved care for children with critical heart conditions, according to a paper published in the December 2019 edition of the Journal of the American College of Cardiology. Specifically, they achieved a 24% decrease in postoperative mortality among participating sites between 2014 and 2018.

If you want to know more, read this post about how MSQC and PC4 are top examples of quality improvement in healthcare.

Getting Started with Clinical Data Registry Software Solutions

Today’s most successful clinical data registries use healthcare analytics technology that goes beyond data collection and data warehousing and plays a crucial role in advancing care and research.

Specifically, a clinical data registry platform should:

  1. Acquire various data using industry-leading technology and standards.
  2. Assemble the data into real-world evidence using advanced analytics and data science.
  3. Enable various users to act on the evidence using dashboards, reports, surveys, and other unique decision-support tools.

Leverage the Power of Clinical Data Registries

The National Quality Registry Network outlines some key considerations when approaching a clinical data registry and deciding what to outsource to a vendor. This includes your in-house availability and expertise, budget impact, convenience, and the many responsibilities that can be outsourced or kept in-house.

At ArborMetrix, we help healthcare organizations and companies demonstrate real and measurable results through robust analytics and intuitive reporting. Through our comprehensive partnerships and clinical expertise, we enable our clients to leverage their real-world evidence for real-world results.