October 02 2019 Michelle Chiu

Shifting from “sick” care to healthcare

What will it take to shift the American health system from “sick care” to health care? Health services today primarily focus on disease treatment in the acute care setting, rather than on prevention.  Primary care remains strapped with too few physicians and too little time. In fact, 24% of Americans live in Primary Care Health Professional Shortage Areas,[1] putting additional strain on the 79% of primary care physicians reporting symptoms of burnout.[2]

This structure is fundamentally ill-equipped to manage the increasing burden of chronic disease. The 28% of Americans with 3+ chronic conditions account for 67% of health spending.[3] Diseases caused by obesity and being overweight are responsible for $480B in direct healthcare costs.[4] These conditions are largely preventable, but often require support beyond the four walls of a hospital or clinic. Thus, shifting from “sick care” to healthcare necessitates a better understanding of Americans’ social context, as well as care interventions targeted at modifying behaviors before they lead to chronic disease.

Common Approaches to Understanding Social Determinants

Current efforts to understand social risk factors typically utilize social determinant screenings or zip code level data. Surveys such as PRAPARE are growing in popularity, but without the scale to address a majority of members. Only 24% of hospitals and 16% of physician practices have a system in place to screen for all 5 social needs under CMS’s Accountable Health Communities model,[5] and likely only a small proportion of their patients are screened. With limited time and competing priorities, physicians typically prioritize the sickest patients for these risk assessments—once again focusing on “sick care,” not prevention. Meanwhile, zip code and block-level data are being incorporated into data-savvy organizations, but with limits on their ability to assess individual member needs. Zipcodes can exceed 100K Americans and block groups are designed to range from 600–3K individuals. This methodology operates blindly to the sensitivity and tact needed to address many social risk factors, and may stereotype individuals rather than deliver personalized care.

Clarify’s Approach via Individually-Attributed Consumer Data

At Clarify, we’ve explored a different approach. With access to information from credit risk agencies, consumer databases, and public and private records that provide as many as 1K+ attributes for each individual American—we asked ourselves, is this information reliable and high quality enough for use in member care? Or in other words, can the data Big Tech and major retailers use to power personalized marketing be used to better manage Americans’ health, instead of their wallets?

The answer is yes, but it takes work. Our team of epidemiologists and data scientists developed a scalable methodology for assessing data quality—the Clarify Data Quality Index—which quantitatively evaluates data sets across seven dimensions, including missingness, information lag, and predictive power among others. They broke ground in SBDoH data validation, comparing consumer-sourced data against Census and aggregate statistics, as well as within and across our individually-attributed data sources. The end result was a dataset, available for almost all Americans, with 40+ social and behavioral determinant (SBDoH) attributes spanning all 5 social needs—and ongoing additions as our list of validated attributes grows. This asset enables us to understand individuals’ social risk factors at scale, without relying on resource- and time-intensive screenings. Additionally, it captures individuals who avoid interacting with the healthcare system and monitors changing needs over time.

Next-Gen Risk Stratification: Engaging Risky Members at the Right Time

How can this promote the shift to actual “healthcare”? Traditional approaches to risk stratification, such as LACE and historical claims utilization, skew towards members with recent inpatient visits and often identify members after their most costly events. These approaches fail to surface rising risk, which requires an understanding of disease burden and how SBDoH may accelerate disease progression or promote member health. Our prospective risk ratings incorporate clinical, utilization, and SBDoH factors to drive a more holistic view of risk—for example, our palliative care model outperforms Elixhauser by 34%, identifies at-risk members an average of 5 months earlier than common clinical decision trees, and surfaces members with an average of 2.2x more utilization than our customers’ models.

Moreover, bringing key information on individuals’ disease burden, care utilization, social context, and risk factors into one place—and translating that information into insights and action, dramatically impacts provider efficiency and intervention efficacy. Creating transparency into our risk ratings improves clinician buy-in and trust of the data, and enables providers to ask the right questions at the right time, making the most of their limited time with members.

If you would like to learn more about this topic, join our upcoming webinar: Improved Data, Improved Risk Stratification, Better Care.

[1] Bureau of Health Workforce, Health Resources and Services Administration (HRSA), U.S. Department of Health & Human Services. Designated Health Professional Shortage Areas Statistics: Designated HPSA Quarterly Summary, as of December 31, 2018.

[2] InCrowd. Physician Burnout 2019.

[3] Butotorff, Christine et al. RAND Corporation. Multiple Chronic Conditions in the United States. 2017.

[4] Waters, Hugh and Marlon Graf. Milken Institute. The Cost of Chronic Diseases in the U.S. May 2018.

[5] Fraze, Taressa et al. JAMA Network Open. Prevalence of Screening for Food Insecurity, Housing Instability, Utility Needs, Transportation Needs, and Interpersonal Violence by US Physician Practices and Hospitals. September 18, 2019.

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