Decision Models for Home Health Care

Assistant Professor Melissa O’Connor, PhD, MBA, RN, COS-C

When can an older patient move on from receiving skilled health care at home? In her groundbreaking study, Dr. Melissa O’Connor addresses that question as she targets a new area of decision support for home health discharge. “Today, there are no national, empirically derived decision-support tools regarding determining readiness for discharge,” she notes. “As evidence-based practice develops, research-based methods to support decision-making will become more common.”

The numbers are staggering. According to geriatric and home health care expert Dr. Melissa O’Connor, in 2011, 3.4 million Medicare beneficiaries received approximately 6.9 million skilled home health episodes, costing Medicare $18.4 billion. Medicare defines an episode as the care received during a 60-day period. Medicare relies on home health clinicians and physicians to evaluate the needs of beneficiaries and to decide whether to discharge patients from skilled home health. Appropriate decisions are critical for the patient, family and payers—especially if that patient is an older adult with chronic illness and multiple co-morbidities. If older adults are discharged from home health too soon, poor outcomes can result, among them hospitalization, emergency department visits, shorter time to death, decline in functional status and reduced quality of life.

Decision support in nursing is an understudied but emerging area of science that can have great impact. It can play a key role in improving the care and health of these vulnerable adults by “developing a systematic, evidence-based mechanism to identify patients who are ready for discharge from home health services versus those who require additional home health episodes,” explains Dr. O’Connor. Through an interdisciplinary perspective, her pilot study will garner data about the factors considered important to determine readiness for discharge among older adults vulnerable for poor outcomes. After synthesizing her data, she will build a decision model associated with experts’ recommendations for discharge that can be implemented in home health nursing practice.