Decision Tree

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.

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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.

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Quit smoking

Impact of a Rheumatology Clinic Protocol on Tobacco Cessation Quit Line Referrals

Impact of a Rheumatology Clinic Protocol on Tobacco Cessation Quit Line Referrals

Although smoking is a risk factor for patients with rheumatic conditions and contributes to greater symptom severity and cardiovascular disease risk, standard smoking cessation intervention is rare in rheumatology clinics. Primary care uses brief staff protocols to connect patients to free, state-run tobacco cessation quit line resources, but this approach had not been previously tested in rheumatology.

In this article, authors including HIP Investigators Dr. Christie Bartels and Dr. Edmond Ramly implemented a rheumatology staff-driven protocol, Quit Connect, to increase the rate of electronic referrals (e-referrals) to free, state-run tobacco quit lines (TQL). The group found that implementing Quit Connect in rheumatology clinics was feasible and improved referrals to a state-run TQL.

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WMJ: Impact of Race and Racism on Health

Identifying Substantial Racial and Ethnic Disparities in Health Outcomes and Care in Wisconsin Using Electronic Health Record Data

Identifying Substantial Racial and Ethnic Disparities in Health Outcomes and Care in Wisconsin Using Electronic Health Record Data

Although Wisconsin ranks highly in overall health care quality, the state performs poorly with respect to health disparities. To eliminate health disparities in Wisconsin, it is critical to understand where disparities exist.

Measuring disparities in health outcomes and care allows for benchmarking of current performance and monitoring changes over time. Measurement also allows stakeholders to prioritize efforts and develop and implement programs for the populations that are most impacted by disparities. Authors including HIP Investigator, Dr. Maureen Smith identified racial and ethnic disparities in health outcome and care measures in Wisconsin.

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Woman getting vaccinated in hospital

A prioritization algorithm for healthcare personnel for the CDC’s COVID-19 Phase 1a vaccine distribution plan

A prioritization algorithm for healthcare personnel for the CDC’s COVID-19 Phase 1a vaccine distribution plan

The National Academies of Sciences, Engineering, and Medicine, in its Framework for Equitable Allocation of COVID-19 Vaccine, suggests using an index such as the Social Vulnerability Index (SVI) to prioritize individuals living in locations identified as vulnerable in order to incorporate variables that are most linked to the disproportionate impact of COVID-19 on people of color.

In this paper, authors including HIP Investigator Dr. Maureen Smith, developed an algorithm that can be used to equitably distribute COVID-19 vaccinations to healthcare personnel (HCP) during Phase 1a of the Center for Disease Control and Prevention’s (CDC) coronavirus vaccine distribution plan, if not enough vaccine is available to immunize an entire group of employees with similar job-related risk exposure. The algorithm prioritizes individuals with the highest risk of mortality using Social Vulnerability Index (SVI) and age.

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Choice thumbnail

Novel Decision Support Interventions for Low-risk Thyroid Cancer

Novel Decision Support Interventions for Low-risk Thyroid Cancer

The treatment paradigm for low-risk thyroid cancer increased in complexity following release of the 2015 American Thyroid Association Guidelines for Adults with Differentiated Thyroid Cancer. Although the intention of the guidelines were to “complement informed, shared patient-healthcare provider deliberation” when making treatment decisions, patient-clinician deliberation can fail to meet patients’ needs and may exclude available treatment.

In this research letter, Dr. Susan Pitt and Megan Saucke examined a patient-oriented intervention to support decision-making about low-risk thyroid cancer treatment. The duo held 16 stakeholder meetings between July 2016 and December 2017 to develop a 1-page treatment comparison chart and a trifold pamphlet containing a question prompt list (available at https://www.hipxchange.org/ThyroidCancerTreatmentChoice).

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Skilled Nursing Facility Differences in Readmission Rates by the Diagnosis-Related Group Category of the Initial Hospitalization

Skilled Nursing Facility Differences in Readmission Rates by the Diagnosis-Related Group Category of the Initial Hospitalization

The US Health and Human Services Office of the Inspector General report on nursing homes found that skilled nursing facilities (SNFs) exhibit wide differences in overall hospital readmission rates. Because evidence has been found that hospital readmission rates can be lowered by discharging to specific SNFs, policy makers have started using hospital readmission rates by SNFs as a measure of quality of care patients receive at these facilities.

In a letter to the editor, a group of authors featuring HIP Investigator Dr. Maureen Smith, examined whether SNFs differed substantially in their readmission rates by the DRG category of the initial hospitalization (ie, medically uncomplicated, surgically uncomplicated, medically complicated, or surgically complicated). The authors’ results suggest considerable differences in readmission rates across SNFs by DRG category of the initial hospitalization, despite similar overall readmission rates.

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Girl with Diabetes

Factors associated with health psychology use in pediatric type 1 diabetes

Factors associated with health psychology use in pediatric type 1 diabetes

For children with type 1 diabetes (T1D), self-management is complex and requires coordination between a child and his or her family multiple times every day to test blood sugars, determine and administer insulin doses, and adjust as needed. The consequences of suboptimal self-management are a combination of short- and long-term complications, some of which are life-threatening.

In this publication, HIP Investigator Dr. Elizabeth Cox et al. evaluated the prevalence of health psychology use in children with T1D and the association of this use with individual and contextual characteristics.

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Doctor checking sugar level

Impact of family‐centered tailoring of pediatric diabetes self‐management resources

Impact of family‐centered tailoring of pediatric diabetes self‐management resources

Behavioral interventions to improve self‐management, glycemic control, or quality of life (QOL) for children with type 1 diabetes have small to moderate positive effects, but a host of challenges diminishes their effectiveness in practice. A group of authors including HIP Investigator, Dr. Elizabeth Cox evaluated the hypothesis that delivering self‐management resources in a family‐centered manner, using PRISM to guide resource selection, will result in better glycemic control and quality of life for children and their parents.

Authors found that tailored self‐management resources may improve outcomes among specific populations, suggesting the need to consider families' self‐management barriers and patient characteristics before implementing self‐management resources.

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FCR

System Factors Influencing the Use of a Family-Centered Rounds Checklist

System Factors Influencing the Use of a Family-Centered Rounds Checklist

Checklists are used to operationalize care processes and enhance patient safety; however, checklist implementation is difficult within complex health systems. A family-centered rounds (FCR) checklist increased physician performance of key rounding activities, which were associated with improved parent engagement, safety perceptions, and behaviors. To inform FCR checklist implementation and dissemination, a team of authors including HIP Investigator, Dr. Elizabeth Cox assessed physician compliance with this checklist and factors influencing its use.

Authors found that multiple factors within hospital systems may influence FCR checklist use. Strategies, such as providing rounding schedules and mobile computers, may promote optimal engagement of families during rounds and promote pediatric patient safety.

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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.

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