Recognizing Frailties In How We Measure Health and Health Care—And Charting A Pandemic ... .

Preventive medicine

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COVID-19 has exposed at least three critical frailties in how our data systems are used to reflect on (and try to improve) the nation’s health and health care annually. First, social, demographic, and economic disparities impose unevenness in access to and use of health and preventive services. Second, false dichotomies between infectious and non-infectious diseases fragment how we deliver and measure care and health. This persists even as the pandemic unsympathetically reminds us that in fact, seemingly unrelated diseases such as obesity, diabetes, and hypertension are potent risk factors for COVID-19-related hospitalization and mortality. Third, the approaches we use to define and collect health and health care data are themselves fragmented. Professional society and other national quality guidelines vary to the point of confusing clinicians and patients, and data collection approaches consistently underrepresent groups that are most vulnerable—those who are uninsured and lack access to preventive services.

These frailties are woven into the fabric of America’s health enterprise. So, what do they mean for measuring the nation’s health and health care? And, importantly, given that COVID-19 revealed these frailties—is there a pandemic-resistant path forward to measuring health and health care quality?

The Current Paradigm Of Measurement

Health and health care quality surveillance in the US rely on a mix of actively and passively collected data. Actively collected data are sourced from national surveys. Passive data originate from single- and multipayer data, single- and multisystem health records, vital statistics, and other sources. Additionally, guidelines, preventive services recommendations, and quality metrics are developed via systematic compilation of the most relevant and recent randomized controlled trials, meta-analyses, and modelling studies. Then, recommending agencies disseminate these guidelines and metrics—a process facilitated by public and private health insurance coverage, clinician and patient education, and features of the electronic medical record that make comprehensive care delivery more efficient. The reach and adoption of these evidence-based recommendations and achievement of quality goals are, once again, measured with active and passive data sources.

The Problem(s) With 2020

COVID-19 has undermined the demand for care and also how that care is supplied. New state and federal public health mandates further complicate matters on both fronts. Together, these factors (exhibit 1) have led to fewer opportunities for patients to receive evidence-based health and preventive services—and fewer opportunities for researchers and policy makers to track and measure them. For example, from January to April 2020, approximately three million fewer childhood vaccines for preventable illnesses were ordered compared to the same months in 2019. Meanwhile, out of 51 US Preventive Services Task Force (USPSTF) Grade A and B recommendations, 22 require—and six likely benefit from—in-person, laboratory (for example, sexually transmitted infection testing), imaging (for example, mammograms), or procedures (for example, cervical cancer screening).

Exhibit 1: The influence of supply, demand, and public health on preventive services and health care quality—during COVID-19 and beyond

Source: Authors’ analysis. Note: (*) Supply of health care and preventive service visits include those that occur at traditional clinical practices, non-traditional care locations such as community pharmacies and retail clinics, and federally qualified health centers.

While some screening and counseling can occur via telehealth, adoption by clinicians as well as uptake by users has varied due to both patient and provider preferences, access, acceptability, and capabilities. By overcoming distance, transportation, and other physical barriers, telehealth could improve engagement in care and reduce missed appointments, enhancing continuity of clinician-patient interactions. Yet, it is equally possible that telehealth won’t close access gaps.

Research published by the Commonwealth Fund reported that ambulatory visits declined by 60 percent of pre-pandemic levels by early April 2020. At its maximum, the number of telehealth visits was only 14 percent higher than pre-pandemic levels and now appear to have plateaued at just 7 percent higher than pre-pandemic visits. There are also concerns whether telehealth deepens disparities, weakens provider-patient relationships, or allows low-value, non-evidence-based services—such as unnecessary antibiotic prescriptions—to creep higher. Furthermore, in some cases processes of care designed to monitor for end organ damage such as eye or foot exams for diabetes, and guide the management of cardiovascular risk factors such as blood pressure measurement may have been replaced by home-monitoring tools without established reliability and validity.

There has been wide heterogeneity in the extent to which supply, demand, and public health factors have affected individuals, systems, and states. Deep-seated disparities and fragmentation underlie and can exacerbate this variation and influence the validity, accuracy, and precision of measuring health and health care use. For example, definitions and policies governing “essential” workers and businesses have varied widely, and socioeconomic disparities have forced some to return to work prematurely, increasing risk of SARS-CoV-2 exposure and widening health disparities. There is also variation in the timing of these impacts; for example, lack of access to health care may become evident later when people lose employer-linked insurance or when state revenues drop to the point that Medicaid coverage is reduced. Unfortunately, those who have lost their insurance are the most in need of coverage.

Mortality data are also not immune to these influences. During the pandemic, death rates have been higher than for seasonally adjusted data from previous years due to increases in both COVID-19-specific and non-COVID-19 mortality—the latter likely due to inadequate or missed care during the pandemic shutdowns. Perversely, higher mortality among those with chronic conditions may lead to a scenario of “lower prevalence” or “better controlled” estimates of these conditions post-pandemic.

A Path For The “New Normal”

The combination of systematic and random variation in policies, preferences, and behaviors—only some of which are measurable—will bias and confound 2020’s health and health care quality data. Regardless, documenting the “gaps” in high-value service use should guide the design of focused strategies (for example, catch-up vaccinations). Generating estimates on a regular, repeated basis and stratifying these data to explore which groups—defined by where they live, their race/ethnic background, socioeconomic circumstances, or clinical characteristics—are worst affected will help identify the most vulnerable groups that have greatest needs. Estimates like this from active data sources will likely continue to suffer from recall biases. Also, the “new normal” will likely be a hybrid of in-person and telehealth care. As such, could we plan health and health care measurement beyond 2020 that prevents the creation of uninterpretable data?

We believe that such a plan requires investments and rigorous research in three areas. First, health equity should pervade the planning and evaluation of all policies, delivery, and research. The USPSTF’s recent statement on its commitment to incorporating considerations of social determinants of health in its recommendations is a worthy step in the right direction.

Second, to address fragmentation, interoperability of electronic clinical data is imperative. Disparate and proprietary technologies that cannot be linked worsen fragmentation of care, erode trust, and likely contribute to clinician burnout and waste. It is high time data are centered on the individual and portable, instead of being controlled by the payer and delivery systems that serve individuals. As outlined by the National Academy of Medicine, end-to-end interoperability from individuals’ devices to their electronic health records holds great potential to empower patients in self-care; link care settings, community pharmacies, laboratories, and long-term care facilities that can provide continuity of information (for example, a national immunization registry could help close gaps and avoid unnecessary re-vaccinations); and anonymously and privately contribute to health and health care surveillance. Recent congressional action toward unique patient identifiers is an important step.

Third, technology requires trustworthy ecosystems that are patient-centered, privacy-preserving, effectively designed to convey visual cues, and support health literacy. Investing in these ecosystems implicitly includes prioritizing quality metrics that convey value and are most strongly correlated with health. The pandemic also underscores the importance of convenient, non-invasive screening (for example, reliable and accurate home kits for health screenings such as home HIV kits) and prevention.

Conclusions

The pandemic has accentuated disparities and fragmentation and laid bare the inadequacies of health and health care measurement in the US. This realization should catalyze investments in equity, interoperability, and patient-centered metrics and ecosystems that are pandemic resistant and support integrated delivery, measurement, and quality that aligns with the quadruple aim.