A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization

Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis

PURPOSE

To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions.

METHODS

We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines.

RESULTS

Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data.

CONCLUSION

Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

Enhancing data practices for Whole Health: Strategies for a transformative future

We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.

Evaluating a learning health system initiative: Lessons learned during COVID-19 in Saskatchewan, Canada

Introduction

Evaluating a learning health system (LHS) encourages continuous system improvement and collaboration within the healthcare system. Although LHS is a widely accepted concept, there is little knowledge about evaluating an LHS. To explore the outputs and outcomes of an LHS model, we evaluated the COVID-19 Evidence Support Team (CEST) in Saskatchewan, Canada, an initiative to rapidly review scientific evidence about COVID-19 for decision-making. By evaluating this program during its formation, we explored how and to what extent the CEST initiative was used by stakeholders. An additional study aim was to understand how CEST could be applied as a functional LHS and the value of similar knowledge-to-action cycles.

Methods

Using a formative evaluation design, we conducted qualitative interviews with key informants (KIs) who were involved with COVID-19 response strategies in Saskatchewan. Transcripts were analyzed using reflexive thematic analysis to identify key themes. A program logic model was created to represent the inputs, activities, outputs, and outcomes of the CEST initiative.

Results

Interview data from 11 KIs were collated under three overarching categories: (1) outputs, (2) short-term outcomes, and (3) long-term outcomes from the CEST initiative. Overall, participants found the CEST initiative improved speed and access to reliable information, supported and influenced decision-making and public health strategies, leveraged partnerships, increased confidence and reassurance, and challenged misinformation. Themes relating to the long-term outcomes of the initiative included improving coordination, awareness, and using good judgment and planning to integrate CEST sustainably into the health system.

Conclusion

This formative evaluation demonstrated that CEST was a valued program and a promising LHS model for Saskatchewan. The future direction involves addressing program recommendations to implement this model as a functional LHS in Saskatchewan.

Advancing rapid adaptation for urgent public health crises: Using implementation science to facilitate effective and efficient responses

Responding rapidly to emerging public health crises is vital to reducing their escalation, spread, and impact on population health. These responses, however, are challenging and disparate processes for researchers and practitioners. Researchers often develop new interventions that take significant time and resources, with little exportability. In contrast, community-serving systems are often poorly equipped to properly adopt new interventions or adapt existing ones in a data-driven way during crises' onset and escalation. This results in significant delays in deploying evidence-based interventions (EBIs) with notable public health consequences. This prolonged timeline for EBI development and implementation results in significant morbidity and mortality that is costly and preventable. As public health emergencies have demonstrated (e.g., COVID-19 pandemic), the negative consequences often exacerbate existing health disparities. Implementation science has the potential to bridge the extant gap between research and practice, and enhance equity in rapid public health responses, but is underutilized. For the field to have a greater "real-world" impact, it needs to be more rapid, iterative, participatory, and work within the timeframes of community-serving systems. This paper focuses on rapid adaptation as a developing implementation science area to facilitate system responses during public health crises. We highlight frameworks to guide rapid adaptation for optimizing existing EBIs when responding to urgent public health issues. We also explore the economic implications of rapid adaptation. Resource limitations are frequently a central reason for implementation failure; thus, we consider the economic impacts of rapid adaptation. Finally, we provide examples and propose directions for future research and application.