Closing the Loop by Operationalizing Systems Engineering and Design (CLOSED)
Specific Aims :
Aim 1:Use systems engineering and patient engagement to design, develop, and refine a highly reliable “closed loop” system for diagnostic tests and referrals that ensures diagnostic orders and follow-up occur reliably within clinically- and patient-important time-frames.
Aim 2: Use systems engineering and patient engagement to design, develop, and refine a highly reliable “closed loop” system for symptoms that ensures clinicians receive and act on feedback about evolving symptoms and physical findings of concern to patients or clinicians.
Aim 3: Design for generalizability across health systems more broadly so that the processes created in Aims 1 and 2 are effective in (1) a practice in an underserved community, (2) a large tele-medicine system, and (3) a representative range of simulated other health system settings and populations.
Sunday, June 2, 2019
Sunday, June 2, 2019
Sunday, June 2, 2019
Results to Date:
Center for Healthcare
Clinician burnout is a major problem affecting more than 80% of healthcare systems, with significant implications on mental health, patient safety, and costs (estimated at $4.6 billion annually). This project responds to National Academy of Medicine recommendations to apply systems engineering methods to understand and reduce burnout, including model-based analysis of burden dynamics and interventions, workflow simplification, and process redesign.
Society continues to suffer epidemics of enormous consequence, ranging from COVID-19, opioid and substance abuse, violence, and mental health. This research has been developing and applying novel methods for modeling epidemic spread across regions and populations, combining differential equation, agent-based, and curve fitting models. Results to-date have been used to develop etiology insights, evaluate interventions, investigate disparities, and conduct model-based surveillance.
Surges in care demand can significantly tax a health system’s ability to provide adequate care, with COVID-19 illustrating extreme cases (makeshift hospitals, overflowing ICUs, equipment stock-outs, staff shortages). The project has developed several models for accurately estimating facility-specific bed, staff, and equipment needs and shortages on a 1-30 day ahead rolling basis and currently is seeking additional test beds to help evaluate accuracy, impact/value, and further modeling needs.
Detecting changes in baseline rates and patterns is important to control hospital infections, epidemics, and substance abuse patterns, among others. This is an ongoing CHER focus area to develop and apply novel detection methods, including change point, condition-achieving, likelihood-based SCAN statistic, signal processing, network spanning tree, model-based (mechanistic and stochastic), and prediction volatility methods. We are seeking new test beds and problems to continue this overall work.
While standard statistical process control (SPC) charts have become common for detecting changes in key processes, they do not always fit some important healthcare applications. This is an ongoing CHER focus area to develop and disseminate special-purpose SPC methods for such cases, including rare events, risk-adjusted data, start-up applications, time-lagged data (e.g. weekly infections), epidemic data, and others. We are seeking systems interested in using these methods or with new needs.
Primary care is an important but under-researched focus area for industrial and systems engineering (ISE), which historically has been applied much more to in-hospital problems. This project is conducting a literature review and environmental scan of how ISE has been applied in primary care (types of problems, types of methods, important gaps) to inform future work for greater ISE impact relative to key and emerging problems faced by primary care.
While unit-based rooming of admitted inpatients can facilitate improved care, efficiency, safety, and satisfaction, policies to best cohort patients is less clear. This project conducted mixed methods research (data analysis, qualitative survey, literature review, computer simulation modeling) to investigate and compare approaches for optimally cohorting inpatients. We are seeking additional health systems interested in using the developed models and/or results of this project.
Despite significant patient safety improvements over the past two decades (primarily using basic improvement methods (“safety-1”)), iatrogenic harm remains a major concern in most health systems and in other industries new safety methods continually are being developed. This project therefore is investigating the application and utility of “safety-2” and other new safety methods to a range of problems within CHER members, directly improving their processes as a result.
Disparities are a problem across healthcare, including access, treatment, safety, and outcome inequities, many involving fundamental systems problems. This project responds to NIH, AHRQ, and NSF calls for systems engineering and modeling research to address care disparities. Pilot work to-date has developed several models and adapted systems engineering methods to help study disparities. We are seeking systems interested in informing further directions for this work and piloting results on key problems.
Equitable access to care is ubiquitous problem across healthcare, particularly affecting rural, minority, and underserved populations, with clear links to poorer outcomes. This is an ongoing CHER project focus area to apply systems engineering methods, computer simulation, and optimization models to study and improve access equity and overall, with applications to-date spanning cancer screening, substance abuse treatment, dermatology, and diagnostic testing.
Below is an overview of current annual projects. Additional information on each can be found by following the links. New projects are defined quarterly to enable input from incoming members on their interests, needs, and priorities.