How can you use data analysis to prevent hospital readmissions?

Care Coordination and Follow-Up are critical components in preventing hospital readmissions by ensuring timely and appropriate post-discharge care. Analyzing data related to post-discharge care coordination and follow-up appointments helps identify gaps and areas for improvement. Effective care coordination, coupled with targeted interventions for those at higher risk, enhances patient outcomes and reduces the likelihood of readmission.

Como Trabajadora Social, se?alo que las readmisiones pueden estar soportadas por causas sociales relacionadas con temas de pobreza, desigualdades étnicas, falta de acceso a servicios educativos, discapacidades, viviendas no seguras, inseguridad alimentaria, violencia, entre otros factores sociales. Este tipo de causas sociales es vital incorporarlas en el concepto mismo de readmisiones, ya que al hacerlo se fortalece también la vinculación de profesionales sociales que apoyen en la atención de casos, así como la evaluación de los contextos sociales y culturales en los cuales se genera la readmisión, para analizarlos y promover acciones de mejora.

Real-Time Monitoring plays a crucial role in preventing hospital readmissions by enabling continuous tracking of patients' health data post-discharge. This approach, facilitated by technologies like wearables and remote monitoring tools, allows healthcare providers to promptly identify potential issues or deterioration, intervening early to prevent complications and reduce the likelihood of readmission.

Medication Adherence Monitoring is essential in ensuring patients follow prescribed treatment plans, thereby decreasing the risk of exacerbation of their conditions. By employing tools such as medication adherence apps, smart pill dispensers, or electronic health records, healthcare providers can monitor and analyze whether patients are taking their medications as prescribed. Improved medication adherence contributes to better symptom management and overall health, ultimately reducing the chances of hospital readmissions.

30 days seems to be the standard definition for readmission (this excludes traumatic/emergent non-related admissions). However, that doesn’t paint the full picture. Greater success can be benchmarked at various intervals- 30, 60, 90 days, 6 month, 1 year, etc. Population-level metadata helps to identify high-risk populations and provides a baseline for risk stratification. On an individual level, claims data on medication refills, appointment follow up, and psychosocial/socio-economics, resource availability are some of the key indicators for readmission. Strong transitional care between one care level to the other can often prevent readmission.

Data about readmissions and emergency department presentations should be linked with primary care and ambulance data to identify at-risk patients. This allows co-design of hospital avoidance programs by strengthening prevention strategies, early detection and chronic disease management in primary care and addressing social determinants of health in communities.

Well said, Dr Edwin Kruys. We have started to do this via the Lumos data linkage project, supported by the PenCS risk stratification tool. The technology is (increasingly) available. The next step is awareness, engagement, education and training. Primary Health Networks play a vital role in the allocation of human, financial, technological and social resources needed to turn data into actionable insights, as well as in providing practice facilitation and change management support. The Strengthening Medicare program has started to shift funding levers to support proactive health care delivery. The problem we need to collaborate on is that of workforce constraint (including supply, distribution and funding).

Data between care settings and healthcare entities needs to be as seamless as possible and checked for accuracy at every step. EMR solutions that communicate with each other are ideal, but it comes down to taking the time to ensure the data is accurate - med reconciliation starting at ED/admission, accurate discharge summaries, community-based case management follow up is essential, clear follow up plans. Without good data throughout the care spectrum, it’s difficult to analyze the true efficacy of individual and population health management.

Como Trabajadora Social, se?alo que la recopilación y organización de datos en las readmisiones debe incluir variables sociales de análisis. Los registros médicos de las readmisiones pueden identificar tasas de inequidad entre grupos poblacionales, como hombres y mujeres, grupos étnicos, adultos y ni?os, y situaciones económicas. Estos análisis permiten identificar grupos poblacionales con mayores barreras en la atención médica, que deben ser trabajados para prevenir readmisiones. También es importante conocer datos demográficos, niveles de pobreza, educación, violencia, etc. La información de datos es vital para lograr análisis profundos y mejorar el servicio.

Data driven insights are crucial to identify opportunities of improvement. We often spend more time on debating the definition of readmissions or its inclusion/exclusion criteria. I believe diving deeper into this population of patients to better understand the key drivers for readmission will be more beneficial. We need to leverage Data insights from sources like Vizient Clinical database, post-discharge patient calls and closing care gaps utilizing telemedicine as well technology.

In a city hospital, a data analytics team tackles the challenge of reducing readmission rates. Through rigorous analysis, they uncover demographic patterns and critical post-discharge periods using regression analysis and predictive modeling. Creating intuitive dashboards, they empower frontline staff to visualize key insights for targeted interventions. To refine their strategy, the team benchmarks the hospital against national standards and peers, revealing actionable opportunities for improvement. This real scenario showcases how data analysis and visualization serve as a strategic compass, guiding the hospital toward more effective and patient-centric healthcare practices.

Para analizar y visualizar datos, me gusta utilizar técnicas de análisis de género e interseccionalidad. Este tipo de análisis permite identificar oportunidades de mejora en el dise?o de programas sociales y de salud. El análisis se puede realizar comparando grupos poblacionales para calcular probabilidades de reincidencia. Estas probabilidades se pueden analizar críticamente para identificar factores sociales que influyen en la reincidencia. Los datos se pueden visualizar en subconjuntos de pruebas con datos desglosados por hipótesis de atención y grupos poblacionales. El análisis también se puede nutrir de la recopilación de información documental de la zona, así como de información cualitativa de grupos infrarrepresentados.

Para implementar y monitorear intervenciones para evitar reingresos, el análisis de datos puede ayudarnos a identificar barreras en el servicio que se pueden presentar en grupos poblacionales específicos. Estas barreras pueden ser sociales, culturales o económicas. Identificar estas barreras es importante para mejorar la atención y reducir los reingresos. El enfoque tradicional, que se centra en modificar el comportamiento del paciente, no siempre es suficiente. En cambio, es necesario reconocer las barreras sociales y culturales existentes en el territorio y trabajar con ellas a favor. Esto significa, por ejemplo, brindar apoyo social y económico a los pacientes, así como adaptar los servicios a sus necesidades específicas.

Having a continuous improvement mindset is essential. One needs to continuously monitor the metrics of success of a program (process and/or outcome measures) and iteratively adjust the program or processes based on performance and new learnings.

?? Continuous monitoring in disease management programs is key! ?? Tracking parameters like weight and blood pressure is vital. ?? Strong patient engagement is a must! Educate them on how to respond to changes—be it weight gain or blood pressure fluctuations. ?? Clearly outline the steps! Whether it's visiting a general practitioner or a specialist, minimizing decompensation is the goal. ?? Keep individuals in their comfort zone! Even for those with dementia, leveraging sensory tech helps respond swiftly to glucose level changes, avoiding complications and hospital stays.

Para aprender y mejorar continuamente, es necesario que los sistemas de salud estén fuertemente conectados con el contexto social del que forman parte. Esto permite tener el contexto necesario para analizar los datos de manera acertada, promover acciones de manera articulada con grupos de interés, en especial con grupos de posiciones infravaloradas, y promover acciones innovadoras que permitan que el sistema de salud pueda romper estigmas e incorporar formas de vida diferentes e identidades que también juegan un papel importante en la prestación del servicio y en la atención en salud.

We have to commit to being life-long learners. Additionally, constantly reviewing, monitoring, and documenting changes proves to be very beneficial in the long run. We have to expand our knowledge base by networking and being innovative.