Blood pressure young girl

Health System Research Priorities for Children and Youth With Special Health Care Needs

Health System Research Priorities for Children and Youth With Special Health Care Needs

Children and youth with special health care needs (CYSHCN) have, or are at an increased risk for, chronic physical, developmental, behavioral, or emotional conditions and also require health and related services of a type or amount beyond that required by children generally.

HIP Investigator, Dr. Ryan Coller et al. synthesized the opinions of a national multidisciplinary group of CYSHCN experts, including family caregivers, to prioritize research topics facing CYSHCN. Authors sought to take a foundational step toward developing a national research agenda for CYSHCN systems of care.

Read the article

Doctors in meeting room

Using Stakeholder Values to Promote Implementation of an Evidence-Based Mobile Health Intervention for Addiction Treatment in Primary Care Settings

Using Stakeholder Values to Promote Implementation of an Evidence-Based Mobile Health Intervention for Addiction Treatment in Primary Care Settings

Most evidence-based practices (EBPs) do not find their way into clinical use, including evidence-based mobile health (mHealth) technologies. The literature offers implementers little practical guidance for successfully integrating mHealth into health care systems. HIP Investigator, Dr. Andrew Quanbeck describes a novel decision-framing model that gives implementers a method of eliciting the considerations of different stakeholder groups when they decide whether to implement an EBP.

This paper presents a model implementers may use to elicit stakeholders' considerations when deciding to adopt a new technology, considerations that may then be used to adapt the intervention and tailor implementation, potentially increasing the likelihood of implementation success.

Read the article

View the toolkit

HIP Model

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.

Read the article

View the HIP Model and Tools for Research with Learning Health Systems

View the Case Management Case Management Benefit Scoring System Toolkit

Big Data Word Scramble

Evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet)

Evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet)

Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. A team of authors including HIP Investigators Dr. Maureen Smith and Dr. Menggang Yu sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients.

Read the article

View the toolkit

NNT vs number referred

Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits

Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits

Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes if they cannot be translated into efficacious intervention. HIP Investigator, Dr. Brian Patterson et al. examined the potential utility of automated risk stratification and referral intervention to screen older adults for fall risk after emergency department visits. This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record data and estimated the effects of a resultant intervention based on algorithm performance in test data.

Read the article

View the toolkit

Holding hands

Fused comparative intervention scoring for heterogeneity of longitudinal intervention effects

Fused comparative intervention scoring for heterogeneity of longitudinal intervention effects

With the growing cost of health care in the United States, the need to improve efficiency and efficacy has become increasingly urgent. There has been a keen interest in developing interventions to effectively coordinate the typically fragmented care of patients with many comorbidities. Evaluation of such interventions is often challenging given their long-term nature and their differential effectiveness among different patients. Furthermore, care coordination interventions are often highly resource-intensive. Hence there is pressing need to identify which patients would benefit the most from a care coordination program. In this work, Dr. Jared Huling and HIP Investigators Dr. Menggang Yu and Dr. Maureen Smith introduced a subgroup identification procedure for long-term interventions whose effects are expected to change smoothly over time.

Read the article

Research doctor team

Systems consultation for opioid prescribing in primary care: a qualitative study of adaptation

Systems consultation for opioid prescribing in primary care: a qualitative study of adaptation

In order to promote guideline-concordant opioid prescribing practices, a blended implementation strategy called systems consultation was pilot tested in four primary care clinics in one US health system. In this study, HIP Investigator, Dr. Andrew Quanbeck et al. described (1) how systems consultation worked during the pilot test and (2) the modifications necessary to adapt this implementation strategy to primary care.

Read the article

Health care meeting

Influence of Environmental Design on Team Interactions Across Three Family Medicine Clinics: Perceptions of Communication, Efficiency, and Privacy

Influence of Environmental Design on Team Interactions Across Three Family Medicine Clinics: Perceptions of Communication, Efficiency, and Privacy

Protocols encourage healthcare team communication before and after primary care visits to support better patient care. Physical clinic environments may influence these behaviors, but limited research has been performed. The UW PATH collaborative explored how two different primary care clinic physical layouts (onstage/offstage and pod-based [PB] designs) influenced pre- and postvisit team experiences and perceptions.

Read the article

young boy patient

Complex Care Hospital Use and Postdischarge Coaching: A Randomized Controlled Trial.

Complex Care Hospital Use and Postdischarge Coaching: A Randomized Controlled Trial.

Complex care programs seek to influence key health outcomes for children with medical complexity (CMC), and investment in program infrastructure is often justified by anticipating savings from lower health care use. HIP Investigator, Dr. Ryan Coller et al. sought to examine the effect of a caregiver coaching intervention, Plans for Action and Care Transitions (PACT), on hospital use among children with medical complexity (CMC) within a complex care medical home. Among CMC within a complex care program, a health coaching intervention designed to identify, prevent, and manage patient-specific crises and postdischarge transitions appears to lower hospitalizations and charges.

Read the article

Doctors working around table

Optimal treatment assignment to maximize expected outcome with multiple treatments.

Optimal treatment assignment to maximize expected outcome with multiple treatments.

When there is substantial heterogeneity of treatment effectiveness, it is crucial to identify individualized treatment assignment rules for comparative treatment selection. HIP Investigator, Dr. Menggang Yu et al. propose an outcome weighted learning method that extends estimating individualized treatment rules to multi‐treatment case by using a vector hinge loss as a target function. Consistency of the resulting estimator is shown in the article.

Read the article

Pages