Comparative workflow modeling across sites: Results for nursing home prescribing
Comparative workflow modeling across sites: Results for nursing home prescribing
Workflows associated with health care delivery vary between settings, and understanding similarities and dissimilarities can inform context-sensitive practice change. Clinical workflows are complex, dynamic, and context-dependent, and comparing workflow across multiple settings can support tailored implementation of practice-change interventions.
In this publication, HIP Investigator Dr. Edmond Ramly et al. propose a methodology for comparative workflow modeling and evaluate its use through application to antibiotic prescribing in six nursing homes. Authors describe the steps of the methodology in general and then demonstrate how to use them in a challenging application context to help equip others to adopt the methodology to study or improve other workflows in other settings.
Predictive Solutions in Learning Health Systems: The Critical Need to Systematize Implementation of Prediction to Action to Intervention
Predictive Solutions in Learning Health Systems: The Critical Need to Systematize Implementation of Prediction to Action to Intervention
The growth in the use of predictive models in health care continues as health systems adopt electronic health records and gain access to real-time digitized clinical data. Although health systems often have substantial experience in quality improvement related to care interventions, they have limited experience in implementing predictive models as part of the care process.
In this publication, authors, including HIP Investigators Dr. Maureen Smith and Dr. Brian Patterson, describe an approach to implementing predictive solutions that adapts the widely used Find-Organize-Clarify-Understand-Select–Plan-Do-Check-Act framework. This process can be used to bring together quality improvement teams and data analytics staff in leading a common process for organizational change and in supporting clinicians in adopting predictive solutions.
To guide health systems through the process of selecting and implementing a predictive model within their system, the UW Health Applied Data Science team and the Health Innovation Program developed Predictive Models: A Toolkit to Guide Implementation in Health Systems to support planning for and implementation of a predictive model. This toolkit should be used by health system quality improvement leaders, project managers, and analytics staff who are responsible for developing and implementing a predictive model within their health system.
Linking Parent Confidence and Hospitalization through Mobile Health: A Multisite Pilot Study
Linking Parent Confidence and Hospitalization through Mobile Health: A Multisite Pilot Study
In this publication, authors including HIP Investigator, Dr. Ryan Coller conducted a multisite pilot study of an mHealth platform with CMC caregivers (Assessing Confidence at Times of Increased Vulnerability [ACTIV]). ACTIV uses longitudinal text messaging to prospectively monitor parent confidence for their child to avoid hospitalization over the subsequent month. Their aim was to identify associations between ACTIV's repeated measures and CMC hospitalization, and to evaluate ACTIV's feasibility/acceptability when implemented within a complex care program.
Effect of Resident Physicians in a Supervisory Role on Efficiency in the Emergency Department
Effect of Resident Physicians in a Supervisory Role on Efficiency in the Emergency Department
Patient throughput and emergency department (ED) length of stay (LOS) are recognized as important metrics in the delivery of efficient care in emergency medicine. However, academic centers must balance expeditious care delivery with the educational mission of training the next generation of emergency physicians.
In this article, HIP Investigator, Dr. Brian Patterson et al. sought to examine the impact of a staffing model involving a supervisory resident “pre-attending” (PAT) on ED throughput and LOS, as this model offers a valuable educational experience for residents, but may do so at the expense of operational efficiency.
Comparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data
Comparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data
Emergency department (ED) visits for falls among older adults are often sentinel events for poor health trajectories; however, challenges exist in defining fall‐related visits in the ED. Authors including HIP Investigators Dr. Brian Patterson and Dr. Maureen Smith developed and validated a simple rules‐based Natural language processing system that accurately identified falls from the text of ED physician notes.
The goal of the study was to compare performance characteristics of several fall identification strategies using EHR data from ED visits using manual chart abstraction as a gold standard.
Codesign and Usability Testing of a Mobile Application to Support Family-Delivered Enteral Tube Care
Codesign and Usability Testing of a Mobile Application to Support Family-Delivered Enteral Tube Care
Enteral tubes are prevalent among children with medical complexity (CMC), and complications can lead to costly health care use. Using a human-centered codesign process, authors including HIP Investigator, Dr. Ryan Coller, created a highly usable mobile application to support enteral tube caregiving at home. Future work involves evaluating the feasibility of longitudinal use and effectiveness in improving self-efficacy and reduce device complications.
Growing Evidence for Successful Care Management in Children With Medical Complexity
Growing Evidence for Successful Care Management in Children With Medical Complexity
In this article, Dr. Mary Ehlenbach and HIP Investigator Dr. Ryan Coller reviewed the success of programs designed to better coordinate care for children with medical complexity. With respect to changes in acute care use, children likely respond to complex care models in different ways. Additionally, what complex care achieves and how it achieves it for any given child is likely not uniform in cross section or over time.
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department
Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. HIP Investigator, Dr. Brian Patterson described and validated a pragmatic, rules-based Natural Language Processing approach for identification of fall patients in the emergency department.
What Are We Measuring? Evaluating Physician-Specific Satisfaction Scores Between Emergency Departments
What Are We Measuring? Evaluating Physician-Specific Satisfaction Scores Between Emergency Departments
Most emergency departments (ED) use patient experience surveys (i.e., Press Ganey) that include specific physician assessment fields. A team of authors including HIP Investigator, Dr. Brian Patterson determined whether Press Ganey ED satisfaction scores for emergency physicians working at two different sites were consistent between sites, and to identify factors contributing to any variation.
The group found that Press Ganey satisfaction scores for the same group of emergency physicians varied significantly between sites suggesting that these scores are more dependent on site-specific factors, such as wait times, than a true representation of the quality of care provided by the physician.
A practical model for research with learning health systems: Building and implementing effective complex case management
A practical model for research with learning health systems: Building and implementing effective complex case management
For researchers to contribute meaningfully to the creation of learning health systems, practical tools are required to operationalize existing conceptual frameworks. A team of authors including HIP Investigators, Dr. Maureen Smith and Dr. Menggang Yu describe a model currently in use by the University of Wisconsin Health Innovation Program (HIP). The HIP model consolidates and enhances existing learning health system frameworks by defining specific steps needed to create sustainable change based on research conducted within the health system.
View the HIP Model and Tools for Research with Learning Health Systems
View the Case Management Case Management Benefit Scoring System Toolkit