Framework for Evaluating and Implementing Inpatient Portals: a Multi-stakeholder Perspective.
Daniel M WalkerJennifer L HefnerCynthia J SieckTimothy R HuertaAnn Scheck McAlearneyPublished in: Journal of medical systems (2018)
Inpatient portals are emerging as an important tool to support patient care and are increasingly being adopted in hospitals. However, best practices concerning the implementation, use, and impact of these portals are poorly understood. To improve evaluation and implementation efforts, this paper develops a logic model that can help researchers and hospital managers in deploying and assessing the impact of inpatient portals. Guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework, we held a series of two focus groups (n = 12 and n = 8, respectively) and an online forum (n = 14) including hospital administrators, clinicians, patients, and information technology team members to learn from these stakeholders about the system-wide implementation and evaluation of an inpatient portal at an academic medical center in the United States. These sessions were supplemented with a Nominal Group process to assess the relative importance and feasibility of evaluation areas. Our Logic Model highlights that patients are at the center of the multi-stakeholder context within which inpatient portals are being implemented, and that collaborative work is necessary for successful implementation and evaluation of the tool. The Model also identifies priority areas for evaluation, and it suggests measures and data sources applicable for quality improvement and research. Applying the SEIPS 2.0 framework, this Logic Model captures the multiple relevant stakeholder perspectives by describing the organizational structures, processes, and outcomes that pertain to inpatient portals. This Model provides specific evaluation suggestions for hospital managers seeking to implement inpatient portals as well as for researchers seeking to evaluate this new technology.
Keyphrases
- quality improvement
- patient safety
- acute care
- palliative care
- mental health
- healthcare
- primary care
- end stage renal disease
- ejection fraction
- newly diagnosed
- type diabetes
- metabolic syndrome
- machine learning
- high resolution
- gene expression
- deep learning
- drinking water
- social media
- big data
- adipose tissue
- patient reported
- skeletal muscle