Patient Centeredness Transfers to Data, Too

Patient Centeredness Transfers to Data, Too

We know data from population health technology can assist the provider organizations to make management strategy decisions that make a significant impact in disease management, for instance, with diabetic populations thanks to sophisticated technology and tracking tools that add real-time data from remote monitoring devices to influence the drug regime changes for optimizing patient outcomes, however, how about pattern recognition when race and racial information is added to the clinical data that highlights disparities that have been documented across the healthcare industry including  COVID-19 morbidity and mortality rates?

How can data-driven technology help physicians and care teams improve care quality collaboratively for all patients?

A patient-centric data model is the foundation for value-based care and can take into consideration high-risk factors, medication adherence rates, frequent ER users and more. Assembling this information into a complete picture for every patient means care quality outcome or improvement activities are implemented equally applied across patient populations and across individual providers in the organization.

An article published recently in MedHealth Outlook titled, “Closing the Gaps & Improving Patient Care: Why Patient-Centric Data Matters,” recommended three components your organization and provider partners should tackle to accomplish equalized value-based care:

  1. Develop a patient-centric data model that includes assessing social determinants of health (SDoH).
  2. Identify the greatest risk through stratification methods to pinpoint those patients that need immediate attention or adjustment to their care plan.
  3. Hold patients accountable through collaborative care that includes goal setting, community support, and targeted disease management.

The Alliance for Integrated Care of New York (AICNY), which cares for approximately 6,200 dual eligible Medicare and Medicaid beneficiaries with developmental disabilities, achieved $4.1 million in total cost reduction in 2020 by incorporating clinical decision support tools in physician workflows. When the COVID-19 pandemic began, a great need emerged for identifying at-risk patients, alerting care teams for follow-up, and re-engaging patients to mitigate the impact of COVID-19 on this vulnerable population.

AICNY’s strategy for 2019/2020 involved using HealthEC’s solution to:

  • Analyze comprehensive patient data from internal and outside medical doctors, a regional HIE, and a pharmacy benefits manager.
  • Identify high-risk patients and focus on chronic care management and transitions of care.
  • Improve interdisciplinary clinical care and care coordination with long-term care providers.

AICNY also installed tele-triage kiosks in group homes connected to local ER or urgent care providers with access to beneficiaries’ longitudinal records. As a result of deploying their strategy — which focused on a very narrowly targeted population — In 2020, AICNY saw a 37 percent drop in ER visits and a decrease in both hospital admissions and readmissions.

A deeper dive into community-wide data can drive higher quality care decisions by taking into account a patient’s ability to access care based on housing, employment, transportation, and other indicators. We have seen great success with healthcare organizations that integrate patient-centered data into their value-based care models.