By Emad Rizk, MD

Lack of data interoperability in healthcare, with clinical and financial data residing across different systems, has caused extensive challenges for many years. Disparate data has prevented both payers and providers from achieving a comprehensive view of the patient and slowed the shift to value-based care.

It has also severely hampered the industry’s ability to proactively respond to the COVID-19 pandemic, limiting comorbidity risk identification and personalized treatment therapies.

In the first months of the pandemic, interoperability issues forced some physicians to become “medical detectives,” piecing together the right interventions without a complete view of the patient’s medical history. This problem was compounded at times by limited ability to connect with family members due to social distancing restrictions. Public health agencies struggled to cobble together data from disparate systems to help identify major risks associated with the disease, and breakdowns in continuity of care occurred when primary care physicians were left out of the notification process.

Data from a Duke-Margolis study underscores the challenges key healthcare stakeholders face when issues with data interoperability permeate the public health response. Consider, for example, that up to 50 percent of COVID-19 laboratory reports did not contain the patient’s address or ZIP code.

As a result, some patients never received their results—and neither did their primary care physicians, who may never even know their patients were tested. Without key demographic data like patient ZIP codes, public health agencies struggle to identify infection clusters, localize disease hotspots, and perform contact tracing to complete an investigation. The chances that patient information will be matched to the right medical records also are diminished, preventing healthcare providers from gaining a comprehensive view of the patient’s health history further down the line.

Further, efforts to predict outbreaks of disease before confirmed diagnoses are made—tracking increases in emergency department (ED) visits for specific symptoms, for example—depend on the ability to capture and share surveillance data.

According to the Duke-Margolis study, New York City recorded an uptick in ED visits for influenza-like illness and respiratory complaints as early as March 4, when there were just two laboratory-confirmed cases of COVID-19 in the city. In hindsight, it is easy to see how this information could have bolstered COVID-19 response efforts both in the city and regionally. Now, as states examine whether schools should reopen, access to data by local, state, and federal health agencies is essential to visualizing the trajectory of COVID-19 and making the right decisions for the health of the population.

These examples demonstrate that while the timeline for implementation of the 21st Century Cures Act has been delayed until 2021, now is not the time to put the brakes on data interoperability. Further, interoperability on its own will not provide the level of value the healthcare industry needs. Advancements in data sharing must be paired with robust privacy protections as well as analytics that enhance clinical decision-making at the point of care, help eliminate barriers in care for vulnerable populations, and increase efficiency and reduce costs.

As the industry makes the transition toward widespread interoperability next year, amid a continued focus on COVID-19 emergency response, there are three ways an analytics-fueled approach to data sharing will help achieve next-level value.

1. Establishing a Single Source of Truth

A key component of healthcare value is the ability to deliver highly targeted clinical interventions efficiently and effectively. To do this, physicians must have access to comprehensive, timely patient data, including information around existing chronic conditions that may affect treatment decisions. This is especially true when patients are unable to communicate their medical history, a scenario that has often unfolded during the COVID-19 pandemic.

A fully interoperable healthcare system is key to ensuring the right care is provided to the right patient at the right time. It’s also crucial to evolving the healthcare industry by establishing a foundation for value, with data that is easily available and accessible by patients, providers, and health plans.

Efforts to achieve data integrity—such as through use of a master patient index (MPI) to match the right record to the right patient—also will pave the way for applying artificial intelligence (AI) to support more comprehensive analysis of patient records.

For example, integrating standardized address data with patient medical records could boost patient matching accuracy. This strengthens the ability of healthcare organizations to drive highly personalized clinical interventions that take into account comorbidities such as diabetes, hypertension, and obesity—conditions that were found in a significant percentage of patients hospitalized with COVID-19. It also unites stakeholders around shared insights.

During COVID-19, one effort to establish a single source of truth is an initiative by the National Syndromic Surveillance Program (NSSP) to collect data daily from 71 percent of the nation’s emergency departments (EDs). According to the Duke-Margolis study, this initiative enables healthcare’s key stakeholders to “rapidly align on a consensus set of protocols” regarding which data can be used for nationwide surveillance of COVID-19 and how it can be used appropriately.

2. Reducing Disparities in Care

Breaking down barriers to care—including social determinants of health—is one of the biggest challenges healthcare leaders face, and the pandemic has exacerbated these challenges. Nationwide, disparities in care are worsening. In rural areas alone, Medicare beneficiaries with one or more chronic conditions have a 40 percent higher rate of preventable hospitalizations with lack of access to specialty care accounting for 55 percent of this difference. Among African-Americans, deaths from COVID-19 are disproportionately high. In Michigan, 14 percent of the state’s population are African-American but 41 percent of coronavirus deaths are in that community. In New York, African-American and Latino patients are twice as likely to die from COVID-19.

Data interoperability paired with predictive analytics improves health outcomes for vulnerable populations by more effectively and proactively identifying these groups at the point of care. It also positions healthcare organizations to more tightly coordinate healthcare resources—including assistance from social services—before the patient leaves the care environment.

Use of AI-driven analytics can help healthcare professionals identify social determinants of health that increase the risk of exposure among vulnerable populations, such as the elderly and those who lack access to stable housing.

A 360-degree view could empower healthcare teams to strengthen COVID-19 discharge protocols for patients who are housing insecure. With this information in hand, clinicians can work with community social service agencies to assist patients in their recovery while limiting further exposure to vulnerable populations.

Similarly, AI-driven analytics can aid in the design of personalized clinical interventions for the elderly, such as the use of remote technologies to maximize mobility in seniors who are isolated at home during recovery—significantly reducing the risk of frailty—or to combat feelings of loneliness.

3. Strengthening Care Collaboration

Telehealth visits will soar to 1 billion this year amid the COVID-19 pandemic. But unless patients’ medical records are easily accessible and available from multiple settings—including retail settings—virtual providers will be unable to gain a comprehensive view of the patient during the encounter or provide information concerning treatment back to care teams. This will result in disjointed care, preventing care collaboration.

This problem also heightens patient safety risks. Consider a patient who is experiencing a headache during the telehealth encounter. What if that patient were hypertensive, had a history of migraines, or had previous results of a CT scan? This information would be crucial in determining what follow-up questions to pose to the patient; which diagnostic tests to consider, if any; and the treatments to consider or avoid. When telehealth is accessed outside a health system, such as via a retail pharmacy or national telehealth provider, unless the record from that visit is made available to all who provide medical care for the individual, including their primary care provider, critical insights that could inform the individual’s care further down the line may never be shared.

Data interoperability fills in the missing pieces for physicians who are put in the position of “medical detective” on telehealth’s front lines. It also enables physicians to dig deeper, addressing sensitive topics that patients may be reluctant to bring up during a video call with a medical professional they have never met, especially if the patient has privacy concerns. Interoperability also supports effective follow-up by primary care physicians after a telehealth appointment, ensuring detailed data is available to support better engagement and adherence to the care or treatment plan.

Data integrity and analytics-fueled collaboration offer tremendous potential to strengthen the quality of telehealth visits and improve outcomes while reducing costs. For example, access to robust patient data prior to the virtual encounter allows physicians to pre-chart telehealth visits—a practice that enhances care by determining whether advance outreach by a nurse to capture additional information is required; validate the reason for the visit; and pair the patient with remote monitoring devices in advance, if necessary, to aid in the evaluation. When pre-visit outreach is performed, clinicians also can verify that the patient knows what to expect during the visit, increasing the patient’s comfort level and potentially the quality of information provided.

Moving Beyond ‘Medical Detective’

The interoperability and patient access provisions of the 21st Century Cures Act mark “the most extensive healthcare data sharing policies the federal government has implemented.” But achieving the vision of this act means providers and payers must accelerate work toward making data interoperable and actionable in the months before Cures Act implementation.

Continuing to seek opportunities to integrate clinical, demographic, and financial data and pair it with robust analytics—even amid the coronavirus pandemic—is the best path toward a single source of truth that strengthens quality of care. It also positions providers and payers to respond to care needs with agility during and after the pandemic. It is a move that not only will bolster COVID-19 recovery efforts, but also hasten the pace toward value-based care and personalized medicine in the years ahead.

 

Emad Rizk (ceo@cotiviti.com) is chairman, president, and CEO, Cotiviti.

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