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The American Journal of Managed Care September 2018
Food Insecurity, Healthcare Utilization, and High Cost: A Longitudinal Cohort Study
Seth A. Berkowitz, MD, MPH; Hilary K. Seligman, MD, MAS; James B. Meigs, MD, MPH; and Sanjay Basu, MD, PhD
Language Barriers and LDL-C/SBP Control Among Latinos With Diabetes
Alicia Fernandez, MD; E. Margaret Warton, MPH; Dean Schillinger, MD; Howard H. Moffet, MPH; Jenna Kruger, MPH; Nancy Adler, PhD; and Andrew J. Karter, PhD
Hepatitis C Care Cascade Among Persons Born 1945-1965: 3 Medical Centers
Joanne E. Brady, PhD; Claudia Vellozzi, MD, MPH; Susan Hariri, PhD; Danielle L. Kruger, BA; David R. Nerenz, PhD; Kimberly Ann Brown, MD; Alex D. Federman, MD, MPH; Katherine Krauskopf, MD, MPH; Natalie Kil, MPH; Omar I. Massoud, MD; Jenni M. Wise, RN, MSN; Toni Ann Seay, MPH, MA; Bryce D. Smith, PhD; Anthony K. Yartel, MPH; and David B. Rein, PhD
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“Precision Health” for High-Need, High-Cost Patients
Dhruv Khullar, MD, MPP, and Rainu Kaushal, MD, MPH
Health Literacy, Preventive Health Screening, and Medication Adherence Behaviors of Older African Americans at a PCMH
Anil N.F. Aranha, PhD, and Pragnesh J. Patel, MD
Early Experiences With the Acute Community Care Program in Eastern Massachusetts
Lisa I. Iezzoni, MD, MSc; Amy J. Wint, MSc; W. Scott Cluett III; Toyin Ajayi, MD, MPhil; Matthew Goudreau, BS; Bonnie B. Blanchfield, CPA, SM, ScD; Joseph Palmisano, MA, MPH; and Yorghos Tripodis, PhD
Economic Evaluation of Patient-Centered Care Among Long-Term Cancer Survivors
JaeJin An, BPharm, PhD, and Adrian Lau, PharmD
Fragmented Ambulatory Care and Subsequent Healthcare Utilization Among Medicare Beneficiaries
Lisa M. Kern, MD, MPH; Joanna K. Seirup, MPH; Mangala Rajan, MBA; Rachel Jawahar, PhD, MPH; and Susan S. Stuard, MBA
High-Touch Care Leads to Better Outcomes and Lower Costs in a Senior Population
Reyan Ghany, MD; Leonardo Tamariz, MD, MPH; Gordon Chen, MD; Elissa Dawkins, MS; Alina Ghany, MD; Emancia Forbes, RDCS; Thiago Tajiri, MBA; and Ana Palacio, MD, MPH
Adjusting Medicare Advantage Star Ratings for Socioeconomic Status and Disability
Melony E. Sorbero, PhD, MS, MPH; Susan M. Paddock, PhD; Cheryl L. Damberg, PhD; Ann Haas, MS, MPH; Mallika Kommareddi, MPH; Anagha Tolpadi, MS; Megan Mathews, MA; and Marc N. Elliott, PhD

“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.
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It is increasingly clear that high-need, high-cost patients are not a homogenous group, but rather a diverse set of patients with varied circumstances and needs. Acting on this insight requires comprehensive data networks we have not traditionally had, and most analyses to date have focused primarily on claims data. We argue that making clinical and financial gains will require data-sharing networks that integrate clinical factors, genomic information, and social determinants from multiple health systems. Investing in these networks may allow us to better anticipate the unique needs of patients, conceptualize care models to meet those needs, and put targeted interventions into action.

Am J Manag Care. 2018;24(9):396-398
Takeaway Points
  • It is increasingly recognized that high-need, high-cost patients make up a heterogeneous group of patients with varied medical, functional, and socioeconomic circumstances.
  • Designing and implementing targeted care delivery models will require comprehensive data networks with information on medical claims, clinical factors (including genomics), and social determinants of health.
  • Creating and maintaining such data networks may require public investment and greater coordination among health systems. A number of challenges remain, including privacy and cost concerns.
What would it take for health systems to better care for high-need, high-cost (HNHC) patients? Countries across the world are contending with growth in healthcare expenditures, and a disproportionate share of spending is concentrated on small groups of patients with high healthcare utilization.1 In the United States, 1% of the population accounts for 21% of healthcare expenditures and the costliest 5% account for more than half of all health spending.2

HNHC patients have traditionally been identified simply as those with high healthcare utilization. But recent work, including a report from the National Academy of Medicine, aims to stratify patients into subgroups, recognizing their varied circumstances and needs.3 These needs often extend beyond those caused by their medical conditions and include behavioral, functional, and social challenges.

“Precision medicine,” customizing diagnostics and treatments based on a patient’s genetic profile, is generally thought of as essential for the future of personalized care. But for HNHC patients today, “precision health” may be just as important: developing care models based on patients’ unique medical and sociocultural needs and matching those models with the patients most likely to benefit. Creating meaningful taxonomies and translating them into clinical and financial gains will require comprehensive databases with information on medical claims, clinical factors (including genomics), and social determinants of health.

Some health systems have begun collecting and analyzing data on HNHC patients, but current efforts are limited in important ways. First, they do not always include data from multiple hospitals and health systems. Healthcare utilization is often distributed across several systems, particularly in urban settings, and many HNHC patients are not identified by individual hospital analyses in the absence of robust data-sharing arrangements. For example, a study of HNHC patients in Maryland found that only 30% visited 1 hospital, whereas 38% visited 3 or more hospitals.4 Yet hospitals in more competitive markets are less likely to engage in health information exchange.

Second, many analyses are limited by payer type, often with a focus on Medicare claims. But differently insured patients have different drivers of healthcare utilization.5 Healthcare spending on Medicare patients is driven largely by end-organ sequelae of multiple chronic conditions, whereas mental health disorders predominate in Medicaid patients. Catastrophic injuries and specialty drugs are important drivers of high costs in commercially insured populations.

Third, most analyses rely primarily on claims data. Using claims data alone may underestimate the prevalence and severity of chronic disease and mental illness. It also fails to capture important clinical factors, such as functional status, or genomic information, which is increasingly recognized as contributing to variability in drug response, dosing, and adverse effects and may allow for safer and more targeted medication choices. Clopidogrel (Plavix), for example, requires biotransformation in the liver, and CYP2C19 homozygosity is associated with reduced response. Finally, claims-based analyses do not incorporate social determinants of health, which are widely understood to be important, sometimes modifiable, contributors to healthcare costs.

Addressing these concerns will require large structured databases that include inputs from multiple health systems and incorporate claims, clinical, and social information. Such databases would allow better prediction of the low- or medium-utilizer patients who will progress to become high utilizers, which high utilizers will remain high utilizers, which care delivery models will be effective for particular subgroups, and how to allocate limited health system resources in targeted ways to improve outcomes and curb costs.

One such effort is under way at the New York City Clinical Data Research Network (NYC-CDRN). Funded by the Patient-Centered Outcomes Research Institute (PCORI), the NYC-CDRN brings together 22 institutions—health systems, medical colleges, research groups, and government agencies—to share data, develop common tools, and collaborate on research. The network includes comprehensive longitudinal data on millions of patients and uses open-source code to develop high-utilizer “phenotypes” and focus interventions. Similar data-sharing initiatives are being developed in 12 other regions around the country, although they do not currently focus on HNHC patients.

These data-sharing networks allow for both a more complete identification of HNHC patients and a deeper understanding of how clinical and social factors interact at the population level to produce unique challenges for particular subgroups. For example, how does lack of transportation or unstable housing differentially influence care, costs, and outcomes for older, frailer patients compared with disabled patients or mentally ill patients? Transportation may be critical for HNHC patients from particular zip codes or with functional limitations but less relevant for a young patient with sickle cell disease. Integrated behavioral health services may be the most important intervention for patients with coexisting mental illness, but they are unlikely to affect utilization for patients with advanced illness and robust social support.

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