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.

I outlined a strategy that was recently published in MedHealth Outlook titled, Closing the Gaps & Improving Patient Care: Why Patient-Centric Data Matters. Here are 3 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

In New York, The Alliance for Integrated Care of New York (AICNY) achieved $2.4 million in total cost reduction by incorporating clinical decision support tools in physician workflows. When COVID-19 paused critical follow-up care for many AICNY patients and higher infection and hospitalization and mortality rates climbed, great need for identifying at-risk patients and prompting care teams for follow-up and re-engage their patients to change management course that could mitigate the COVID-19 impact on this vulnerable population.

As the strategy that focused on a very narrow targeted population was deployed, AICNY witnessed inpatient expenditures saw a 6% reduction and ER visits dropped by 11%, and admissions were reduced by 7% with a similar increase in the utilization of urgent care reaching as high as 50% of the ER encounters.

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 VBC models.

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