Guiding the Lean Design of a Hospital's New Laboratory: A Case Study.

PURPOSE: To present the process used by a clinical laboratory to guide the design of their new facility, part of a major campus expansion at a pediatric hospital. OBJECTIVE: The primary objective was to generate information about arrangement and allocation of laboratory space and functional flows in the early phases of the expansion project. BACKGROUND: Seattle Children's Hospital has increased the capacity of their primary campus in response to growing demand for specialized services. The clinical laboratory was identified as one of the functional groups that would move their operations to the new building. METHODS: The clinical laboratory team agreed on a three-stage process to guide the development of their initial design specifications: formulate the concept, develop design alternatives, and evaluate those alternatives. CASE STUDY: The different tasks were executed successfully. The process defined-based on Plan, Do, Check, Act (PDCA)-provided a context for collaboration among sections of the clinical laboratory, reducing silo thinking, so common in the specialized sections within a large clinical laboratory. DISCUSSION: The process was developed to guide the lean design efforts, even when initial space and location parameters had not yet been decided and was sufficiently flexible to accommodate changes in the project constraints and emergent information generated by the concurrent design activities of other hospital functional groups to be included in the campus expansion. The three-stage process allowed the team to review each laboratory section's complexity, create multiple conceptual designs for review, and ultimately increase the likelihood of an efficient physical configuration that would satisfy a diverse group of stakeholders.

Augmenting systems-level implementation of patient-reported outcomes for depression care through the use of structured analysis and design technique.

Background: Health systems increasingly need to implement complex practice changes such as the routine capture of patient-reported outcome (PRO) measures. Yet, health systems have met challenges when trying to bring practice change to scale across systems at large. While implementation science can guide the evaluation of implementation determinants, teams first need tools to systematically understand and compare workflow activities across practice sites. Structured analysis and design technique (SADT), a system engineering method of workflow modeling, may offer an opportunity to enhance the scalability of implementation evaluation for complex practice change like PROs. Method: We utilized SADT to identify the core workflow activities needed to implement PROs across diverse settings and goals for use, establishing a generalizable PRO workflow diagram. We then used the PRO workflow diagram to guide implementation monitoring and evaluation for a 1-year pilot implementation of the electronic Patient Health Questionnaire-9 (ePHQ). The pilot occurred across multiple clinical settings and for two clinical use cases: depression screening and depression management. Results: SADT identified five activities central to the use of PROs in clinical care: deploying PRO measures, collecting PRO data, tracking PRO completion, reviewing PRO results, and documenting PRO data for future use. During the 1-year pilot, 8,596 patients received the ePHQ for depression screening via the patient portal, of which 1,719 (21%) submitted the ePHQ; 367 patients received the ePHQ for depression management, of which 174 (47%) submitted the ePHQ. We present three case examples of how the SADT PRO workflow diagram augmented implementation monitoring, tailoring, and evaluation activities. Conclusions: Use of a generalizable PRO workflow diagram aided the ability to systematically assess barriers and facilitators to fidelity and identify needed adaptations. The use of SADT offers an opportunity to align systems science and implementation science approaches, augmenting the capacity for health systems to advance system-level implementation. Plain Language Summary: Health systems increasingly need to implement complex practice changes such as the routine capture of patient-reported outcome (PRO) measures. Yet these system-level changes can be challenging to manage given the variability in practice sites and implementation context across the system at large. We utilized a systems engineering method-structured analysis and design technique-to develop a generalizable diagram of PRO workflow that captures five common workflow activities: deploying PRO measures, collecting PRO data, tracking PRO completion, reviewing PRO results, and documenting PRO data for future use. Next, we used the PRO workflow diagram to guide our implementation of PROs for depression care in multiple clinics. Our experience showed that use of a standard workflow diagram supported our implementation evaluation activities in a systematic way. The use of structured analysis and design technique may enhance future implementation efforts in complex health settings.

A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization.

INTRODUCTION: Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient-centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient-reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework. METHODS: Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders. RESULTS: Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action. CONCLUSIONS: The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice.

Predicting the Future: Using Simulation Modeling to Forecast Patient Flow on General Medicine Units.

BACKGROUND: Hospitals are complex adaptive systems within which multiple components such as patients, practitioners, facilities, and technology interact. A careful approach to optimization of this complex system is needed because any change can result in unexpected deleterious effects. One such approach is discrete event simulation, in which what-if scenarios allow researchers to predict the impact of a proposed change on the system. However, studies illustrating the application of simulation in optimization of general internal medicine (GIM) team inpatient operations are lacking. METHODS: Administrative data about admissions and discharges, data from a time-motion study, and expert opinion on workflow were used to construct the simulation model. Then, the impact of four changes: aligning medical teams with nursing units, adding a hospitalist team, adding a nursing unit, and adding both a nursing unit and hospitalist team with higher admission volume were modeled on key hospital operational metrics. RESULTS: Aligning medical teams with nursing units improved team metrics for aligned teams but shifted patients to unaligned teams. Adding a hospitalist team had little benefit, but adding a nursing unit improved system metrics. Both adding a hospitalist team and a nursing unit would be required to maintain operational metrics with increased patient volume. CONCLUSION: Using simulation modeling, we provided data on the implications of four possible strategic changes on GIM inpatient units, providers, and patient throughput. Such analyses may be a worthwhile investment to study strategic decisions and make better choices with fewer unintended consequences.

Design and implementation of a combined influenza immunization and tuberculosis screening campaign with simulation modelling.

RATIONALE, AIMS AND OBJECTIVES: Design and implement a concurrent campaign of influenza immunization and tuberculosis (TB) screening for health care workers (HCWs) that can reduce the number of clinic visits for each HCW. METHOD: A discrete-event simulation model was developed to support issues of resource allocation decisions in planning and operations phases. RESULTS: The campaign was compressed to100 days in 2010 and further compressed to 75 days in 2012 and 2013. With more than 5000 HCW arrivals in 2011, 2012 and 2013, the 14-day goal of TB results was achieved for each year and reduced to about 4 days in 2012 and 2013. CONCLUSION: Implementing a concurrent campaign allows less number of visiting clinics and the compressing of campaign length allows earlier immunization. The support of simulation modelling can provide useful evaluations of different configurations.

Neither snow nor rain: contingency planning by a clinical reference laboratory courier service for weather related emergencies.

To optimize transportation processes, we present herein a contingency plan that coordinates interim measures used to ensure continued and timely services when climate based events might cause an interruption of the usual specimen transportation processes. As an example, we outline the implementation and effectiveness of a contingency plan for network laboratory courier automobile transportation during times of mountain pass highway closure. Data available from an approximately 3-year period from October 10, 2010 through August 29, 2013 revealed a total of 690 complete closures in the eastbound or westbound lanes of the Interstate-90 highway in the Snoqualmie Pass area in the state of Washington. Despite the frequency of closures, the Washington State Department of Transportation was effective in limiting the duration of closures. Road closures of less than 1 hour accounted for 58.7% of the total closures. No recorded closures prevented dispatched couriers from completing a prescheduled Snoqualmie Pass route. We identified no delays as being clinically significant, despite that there were 5 instances of delays greater than 4 hours. We implemented a contingency plan of aiding courier logistics during all times of pass closure. The plan includes an easy to interpret Condition Dashboard as a status indicator and a Decision Tree that references and summarizes information. Overall, the contingency plan allows for an objective, robust, proactive decision support system that has enabled operational flexibility and has contributed to continued safe, on-time specimen transportation; clients and courier and reference laboratory staff have appreciated these features and associated outcomes.