Principles for Designing and Developing a Workflow Monitoring Tool to Enable and Enhance Clinical Workflow Automation

Cluster randomized trial of a team communication training implementation strategy for depression screening in a pediatric healthcare system: a study protocol

BACKGROUND

Pediatric depression is a global concern that has fueled efforts for enhanced detection and treatment engagement. As one example, the US Preventive Services Task Force recommends depression screening for adolescents ages 12-18 years. While many health systems have implemented components of depression screening protocols, there is limited evidence of effective follow-up for pediatric depression. A key barrier is timely team communication and coordination across clinicians and staff within and across service areas for prompt service linkage. However, team effectiveness interventions have been shown to improve team processes and outcomes and can be applied in healthcare settings.

METHODS

This project aims to refine and test a team communication training implementation strategy to improve implementation of an existing pediatric depression screening protocol in a large pediatric healthcare system. The team will be defined as part of the study but is expected to include medical assistants, nurses, physicians, and behavioral health clinicians within and across departments. The implementation strategy will target team mechanisms at the team-level (i.e., intra-organizational alignment and implementation climate) and team member-level (i.e., communication, coordination, psychological safety, and shared cognition). First, the project will use mixed methods to refine the team training strategy to fit the organizational context and workflows. Next, a hybrid type 3 implementation-effectiveness pilot trial will assess the initial effectiveness of the team communication training (implementation strategy) paired with the current universal depression screening protocol (clinical intervention) on implementation outcomes (i.e., feasibility, acceptability, appropriateness, workflow efficiency) and clinical/services outcomes (increased frequency of needed screening and reduced time to service linkage). Finally, the study will assess mechanisms at the team and team member levels that may affect implementation outcomes.

DISCUSSION

Team communication training is hypothesized to lead to improved, efficient, and effective decision-making to increase the compliance with depression screening and timely service linkage. Findings are expected to yield better understanding and examples of how to optimize team communication to improve efficiency and effectiveness in the pediatric depression screening-to-treatment cascade. This should also culminate in improved implementation outcomes including patient engagement critical to address the youth mental health crisis.

TRIAL REGISTRATION

NCT06527196. Trial Sponsor: University of California San Diego.

Optimization of Electronic Health Record Usability Through a Department-Led Quality Improvement Process

BACKGROUND

Electronic health records (EHR) have become commonplace in medicine. A disconnect between developers and users while creating the interface often fails to create a product that captures clinical workflow, and issues become apparent with implementation. Optimization allows collaboration of clinicians and informaticists after implementation, but documentation of success has only been at the institutional level.

METHODS

A 4-month, department-wide EHR optimization was conducted with information technology (IT). Optimizations were developed from an intensive quality improvement process involving all levels of clinicians and clinical staff. The optimizations were then categorized as accommodations (department adjusted workflow to EHR), creations (IT developed new workflows within EHR), discoveries (department found workflows within EHR), and modifications (IT changed workflows within EHR). Departmental productivity, defined as number of visits, charges, and payments, was standardized to ratios prior to the COVID-19 pandemic and evaluated by Taylor's change point analysis. Significant improvements were defined as shifts (change points), trends (5 or more consecutive values above/below the mean), and values outside 95% CIs.

RESULTS

The 124 optimizations were categorized as 43 accommodations, 13 creations, 54 discoveries, and 14 modifications. Productivity ratios of monthly charges (0.74 to 1.28) and payments (0.83 to 1.58) significantly improved with the optimization efforts. Monthly visit ratios increased (0.65 to 0.98) but did not change significantly.

CONCLUSION

Departmental collaboration with organizational IT for EHR optimization focused on detailed analysis of how workflows can impact productivity. Discovery optimization predominance indicates many solutions to EHR usability problems were already in the system. A large proportion of accommodation optimizations reinforced the need for better developer-user collaboration before implementation.Annals Early Access.

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

BACKGROUND

 Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

OBJECTIVES

 This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

METHODS

 The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

RESULTS

 Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

CONCLUSION

 The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.