Using Data to Optimize Care Transitions 

How data analytics can break the cycle of readmissions

Only for emergencies?
The National Institutes of Health reminds us that the American healthcare system is mainly set up to cope with acute health problems. People with long-term illnesses or other conditions must often deal with different providers and conflicting advice. It’s easy for patients to get confused in this kind of system, to miss or misunderstand important information.
Lapses in data sharing between providers mean that patient histories, diagnoses, medication regimens, and care plans are often inadequately communicated. Patients receive duplicate tests or conflicting care plans, suffer from harmful medication errors, or have a harder time than they should managing their conditions at home. This information void in the continuum of care can have far-reaching consequences.
Fortunately, with the right data and analytics, providers can improve coordination and optimize transitions, which ultimately reduces ED readmissions. By drawing on data from across the care continuum, you can obtain a more complete view of the medical history and needs of each patient, identify patterns and gaps, and facilitate handoffs.
Then patients will benefit from greater continuity of care and better outcomes — and will be less likely to be readmitted.

How Data Analytics Can Optimize Transitions

Data analytics extracts and integrates data from electronic health records, claims, and patient surveys so that you can identify gaps in care, high-risk patients, and strategies to facilitate handoffs between healthcare settings.
Using electronic health records and health information exchanges (HIEs), you can securely share health data with other teams or organizations. Dashboards and alerts provide a quick overview of a patient’s status, risks, and requirements for coordinating the next steps of care.
Predictive analytics also identifies patients at high risk of being readmitted or of suffering adverse events so that staff can intervene promptly when they need to.
Remote monitoring devices track the health status of patients when they’re at home, alerting care teams to problems before they escalate.

Health Information Exchanges

Health information exchanges are secure platforms that make it easier for healthcare providers to share electronic health records and other patient information in real time. This real-time sharing fosters coherent care by reducing duplicate testing and enhancing coordination.
To implement HIEs, organizations must assess their technological infrastructure, make the security of data a priority, and work with providers and established HIE networks.

Identifying At-Risk Patients Fast

Crucial to effective care transition is identifying patients at high risk of readmission. Using data analytics and machine-learning models, healthcare providers can scrutinize patient data, including diagnosis codes, medication lists, and demographic information, in order to discern patterns and risk factors. Once high-risk patients have been identified, providers can intervene with personalized care plans or other post-discharge support.

Post-Discharge Monitoring

Whether done in person or remotely, post-discharge monitoring helps ensure that the transition from hospital care to home care is a smooth one. Making follow-up calls within two or three days of discharge enables providers to review discharge instructions, address concerns, and check that patients understand and are following their care plans. By using devices like pulse oximeters and blood pressure cuffs to remotely monitor the health of patients, care teams are able to intervene promptly when there’s a problem.

Measuring and Improving

Healthcare organizations should establish key performance indicators to measure the effectiveness of efforts to improve care transition. One critical metric is the readmission rate within 30 days of discharge. Lower rates indicate better coordination of care, while higher rates indicate that there is room for improvement. Other metrics monitored by the value-based purchasing plan of the Centers for Medicare and Medicaid Services include excess readmission ratio.
Because data analytics provides real-time insights into the effectiveness of interventions, providers can regularly reassess KPIs and adjust their strategies accordingly.

A Data-Powered Future

The future of healthcare is being shaped by the power of data. Data analytics and other technologies are helping organizations to both improve care and cut costs, in large part by streamlining handoffs and curbing readmissions.
In this era of unprecedented technological advancements, the role of data analytics becomes paramount in optimizing the complex landscape of care transitions. By harnessing the wealth of information derived from electronic health records, claims, and patient satisfaction surveys, healthcare organizations can orchestrate a more cohesive and informed approach to patient care. The holistic view obtained from data analytics enables the identification of critical gaps in care, high-risk patient populations, and the formulation of targeted strategies to ensure seamless transitions between different healthcare settings.