ArborAdmin

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.

‍

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.

2. Identifying and measuring disparities.

3. 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 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.

2. Propelling standards use in healthcare IT to improve interoperability of health data and address new data collection modalities.

3. Modernizing healthcare data collection to drive research and innovation.

4. The importance of using regulation and policy to support healthcare IT infrastructure.

5. Using health IT in public health emergencies.

6. 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.

2. IT infrastructure through health information exchanges and widespread broadband access.

3. Public health policy and funding for digital transformation and interoperability.

4. 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.