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“Precision Health” for High-Need, High-Cost Patients
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“Precision Health” for High-Need, High-Cost Patients

Dhruv Khullar, MD, MPP, and Rainu Kaushal, MD, MPH
Better care for high-need, high-cost patients will require targeted care delivery models based on integrated data networks that include clinical, genomic, and social information.
Check out our website’s new table/figure pop-up feature! Click on the name of a table or figure in the text to see it in your browser.
Consider a HNHC patient with multiple admissions for heart failure exacerbations (Table). How might merging claims, clinical, and social data in a multisystem registry help detect the patient's needs and customize care? Integrated claims data would allow us to correctly identify the patient, especially if they are seeking care at multiple hospitals, and provide an estimate of disease severity based on International Classification of Diseases, Tenth Revision codes; number of comorbidities; and frequency of admissions. Clinical data would enhance our ability to risk stratify the patient using, for example, brain natriuretic peptide levels, recent weight fluctuations, and frequency of diuretic dose changes. Genomic information may ultimately inform warfarin or β-blocker dosing. Finally, social data would provide additional avenues for intervention: Does the patient have an air conditioner? Does the patient live in a neighborhood with limited access to healthy food?

Widespread application of integrated data networks does not yet exist, but recent efforts offer a sense of how multidimensional assessments of patient needs could focus care delivery. Parkland Health and Hospital System in Texas, for example, has developed a risk model to predict the likelihood of readmission for patients with heart failure based on 29 clinical, social, behavioral, and utilization factors. The safety-net health system concentrates an intensive set of interventions—counseling, monitoring, specialty follow-up care, pharmacy, and care management services—on patients identified as highest risk, allowing more efficient use of limited resources. The program has resulted in a 26% relative reduction in the odds of readmission.6

Using a grant from the Center for Medicare & Medicaid Innovation, Denver Health, a vertically integrated health system in Colorado, developed a predictive algorithm to divide patients into 4 risk tiers and personalize care based on their medical and social needs.7 The Department of Veterans Affairs has developed hot-spotter algorithms using population-level data to identify homeless veterans at risk for high healthcare utilization and then target services such as housing, food assistance, care management, and substance use support.8 Comprehensive data networks would enrich these efforts and allow health systems to tailor and scale programs for patients with similar profiles.

Moving toward precision health for HNHC patients will require greater public investment in data-sharing infrastructure. The NYC­-CDRN, for example, is supported by PCORI, and HHS could allocate more funding to promote interoperability of electronic health records and facilitate research across data-sharing networks. Payment models that create incentives for institutions to share data may help allay health systems’ financial and competitive concerns. Dedicated training of a multidisciplinary informatics workforce, including health services researchers, computer scientists, and systems engineers, will also be necessary to compile, organize, and clean data.

Serious challenges remain. Data integration and transparency raise privacy concerns, and health systems must work to relieve patient fears about data security. Managing expansive data networks is expensive and will likely require additional or reallocated funding. It is also not clear who should “control” such data networks and how competitors should share potential costs or savings. Finally, the extent to which care models based on integrated data networks can improve health outcomes will require rigorous and prolonged study.

Nonetheless, better health, especially for HNHC patients, will likely require more precise population health. It is increasingly clear that HNHC patients are not a homogenous group, but acting on this insight requires integrated multifaceted data networks that we have not traditionally had. Investing in these networks would allow us to better anticipate the needs of patients, conceptualize care models to meet those needs, and put targeted interventions into action.

Author Affiliations: Department of Healthcare Policy & Research (DK, RK), and Department of Medicine (DK), Weill Cornell Medical College, New York, NY.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DK, RK); acquisition of data (RK); analysis and interpretation of data (RK); drafting of the manuscript (DK, RK); critical revision of the manuscript for important intellectual content (DK, RK); statistical analysis (RK); provision of patients or study materials (RK); obtaining funding (RK); administrative, technical, or logistic support (RK); and supervision (RK).

Address Correspondence to: Dhruv Khullar, MD, MPP, Department of Healthcare Policy & Research, Department of Medicine, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. Email: khd9010@med.cornell.edu.
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5. Powers BW, Chaguturu SK. ACOs and high-cost patients. N Engl J Med. 2016;374(3):203-205. doi: 10.1056/NEJMp1511131.

6. Amarasingham R, Patel PC, Toto K, et al. Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study. BMJ Qual Saf. 2013;22(12):998-1005. doi: 10.1136/bmjqs-2013-001901.

7. Johnson TL, Brewer D, Estacio R, et al. Augmenting predictive modeling tools with clinical insights for care coordination program design and implementation. EGEMS (Wash DC). 2015;3(1):1181. doi: 10.13063/2327-9214.1181.

8. O’Toole TP, Pape L. Innovative efforts to address homelessness among veterans. N C Med J. 2015;76(5):311-314.
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