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Observational study using the tools of lean six sigma to improve the efficiency of the resident rounding process.

ABSTRACT: BACKGROUND: Recent focus on resident work hours has challenged residency programs to modify their curricula to meet established duty hour restrictions and fulfill their mission to develop the next generation of clinicians. Simultaneously, health care systems strive to deliver efficient, high-quality care to patients and families. The primary goal of this observational study was to use a data-driven approach to eliminate examples of waste and variation identified in resident rounding using Lean Six Sigma methodology. A secondary goal was to improve the efficiency of the rounding process, as measured by the reduction in nonvalue-added time. METHODS: We used the "DMAIC" methodology: define, measure, analyze, improve, and control. Pediatric and family medicine residents rotating on the pediatric hospitalist team participated in the observation phase. Residents, nurses, hospitalists, and parents of patients completed surveys to gauge their attitudes toward rounds. The Mann-Whitney test was used to test for differences in the median times measured during the preimprovement and postimprovement phases, and the Student t test was used for comparison of survey data. RESULTS AND DISCUSSION: Collaborative, family-centered rounding with elimination of the "prerounding" process, as well as standard work instructions and pacing the process to meet customer demand (takt time), were implemented. Nonvalue-added time per patient was reduced by 64% (P = .005). Survey data suggested that team members preferred the collaborative, family-centered approach to the traditional model of rounding. CONCLUSIONS: Lean Six Sigma provides tools, a philosophy, and a structured, data-driven approach to address a problem. In our case this facilitated an effort to adhere to duty hour restrictions while promoting education and quality care. Such approaches will become increasingly useful as health care delivery and education continue to transform.

SUBMITTER: Chand DV

PROVIDER: S-EPMC3184924 | biostudies-other | 2011 Jun

REPOSITORIES: biostudies-other

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Project description:BackgroundThe Veterans Health Administration (VHA) developed a comprehensive mobile screening technology (eScreening) that provides customized and automated self-report health screening via mobile tablet for veterans seen in VHA settings. There is agreement about the value of health technology, but limited knowledge of how best to broadly implement and scale up health technologies. Quality improvement (QI) methods may offer solutions to overcome barriers related to broad scale implementation of technology in health systems. We aimed to develop a process guide for eScreening implementation in VHA clinics to automate self-report screening of mental health symptoms and psychosocial challenges.MethodsThis was a two-phase, mixed methods implementation project building on an adapted quality improvement method. In phase one, we adapted and conducted an RPIW to develop a generalizable process guide for eScreening implementation (eScreening Playbook). In phase two, we integrated the eScreening Playbook and RPIW with additional strategies of training and facilitation to create a multicomponent implementation strategy (MCIS) for eScreening. We then piloted the MCIS in two VHA sites. Quantitative eScreening pre-implementation survey data and qualitative implementation process "mini interviews" were collected from individuals at each of the two sites who participated in the implementation process. Survey data were characterized using descriptive statistics, and interview data were independently coded using a rapid qualitative analytic approach.ResultsPilot data showed overall satisfaction and usefulness of our MCIS approach and identified some challenges, solutions, and potential adaptations across sites. Both sites used the components of the MCIS, but site 2 elected not to include the RPIW. Survey data revealed positive responses related to eScreening from staff at both sites. Interview data exposed implementation challenges related to the technology, support, and education at both sites. Workflow and staffing resource challenges were only reported by site 2.ConclusionsOur use of RPIW and other QI methods to both develop a playbook and an implementation strategy for eScreening has created a testable implementation process to employ automated, patient-facing assessment. The efficient collection and communication of patient information have the potential to greatly improve access to and quality of healthcare.

Project description:BackgroundIn Australia, aged care and disability service providers are legally required to maintain comprehensive and accurate clinical documentation to meet regulatory and funding requirements and support safe and high-quality care provision. However, evidence suggests that poor-quality clinical data and documentation are widespread across the sector and can substantially affect clinical decision-making and care delivery and increase business costs.ObjectiveIn the Optimizing the Quality of Clinical Data in an Australian Aged Care and Disability Service to Improve Care Delivery and Clinical Outcomes (OPTIMISE) study, we aim to use an Agile Lean Six Sigma framework to identify opportunities for the optimization of clinical documentation processes and clinical information systems, implement and test optimization solutions, and evaluate postoptimization outcomes in a large postacute community-based health service providing aged care and disability services in Western Australia.MethodsA 3-stage prospective optimization study will be conducted. Stage 1 (baseline [T0]) will measure existing clinical data quality, identify root causes of data quality issues across services, and generate optimization solutions. Stage 2 (optimization) will implement and test changes to clinical documentation processes and information systems using incremental Agile sprints. Stage 3 (evaluation) will evaluate changes in primary and secondary outcomes from T0 to 12 months after optimization. The primary outcome is the data quality measured in terms of defects per unit, defects per million opportunities, and Sigma level. The secondary outcomes are care delivery (direct care time), clinical incidents, business outcomes (cost of quality and workforce productivity), and user satisfaction. Case studies will be analyzed to understand the impact of optimization on clinical outcomes and business processes.ResultsAs of June 1, 2022, stage 1 commenced with T0 data quality audits conducted to measure current data quality. T0 data quality audits will be followed by user consultations to identify root causes of data quality issues. Optimization solutions will be developed by May 2023 to inform optimization (stage 2) and evaluation (stage 3). Results are expected to be published in June 2023.ConclusionsThe study findings will be of interest to individuals and organizations in the health care sector seeking novel solutions to improve the quality of clinical data, support high-quality care delivery, and reduce business costs.International registered report identifier (irrid)DERR1-10.2196/39967.

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Observational study using the tools of lean six sigma to improve the efficiency of the resident rounding process.

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