Dynamic Capacity Management for Healthcare ─ Advanced Methods and Tools for Optimization

While hospitals can learn from other industries, they cannot be improved or run like factories. With work that is more individualized than standardized, and limited control over volume and arrivals, even the leanest-minded hospital must recognize that healthcare systems are more dynamic than nearly any work environment.

Written with the creativity needed to navigate the rapidly changing landscape of healthcare, Dynamic Capacity Management for Healthcare: Advanced Methods and Tools for Optimization presents the unique new tools, methodologies, and thinking required of healthcare systems that want to survive and thrive in a reduced reimbursement, higher-cost world. Demonstrating his approaches and recommendations through case studies specific to the complex issues of healthcare delivery, Pierce Story, a long-time and passionate healthcare operations expert, shows how hospitals and health systems can make leaps in performance in an environment in which both financial and human resources are shrinking as expectations for clinical perfection continue to rise.

Through its unique approach to the dynamic management of complex care systems, this volume raises the bar for what is possible. This text presents an excellent opportunity for healthcare’s change agents to meet the challenges and responsibilities of our day.

During his 20+ year healthcare career, Pierce Story has dealt with complex systems redesign, operations improvement, and performance analysis throughout hospitals and health systems. Pierce brings years of experience, unique perspectives, and new concepts to chronic disease management, capacity management, patient care, and health system redesign. Having developed several new applications and toolsets for the analysis and redesign of key clinical operations and patient care capacity strategies, Pierce understands the needs of the industry and the failings of traditional solutions. His vision is a new way of managing the provision of healthcare in the United States.
Pierce has a Masters Degree in Health Policy and Management from the Muskie School of Public Policy in Portland, Maine, and is trained in both Six Sigma and Lean methodologies. Pierce is also a Diplomate, Past President, and active member of the Society for Health Systems, a volunteer organization of over 900 healthcare performance improvement specialists and engineers. He is a member of the Leadership Council of the American Society for Quality’s Healthcare Division.

Prologue: Kenji’s StoryPreface: BlasphemyAcknowledgments1. IntroductionA Child of Our OwnDynamic Capacity Analysis, Matching, and Management (DCAMM): Concept OverviewA Caveat to the TextNote2. Variability: Killer of CapacityThe Look and Feel of VarianceThe Deception of the AverageSystem Demand, Patterns, and VariabilityPatterns in Demand VariabilityThe Importance of RangesProbability of OutcomesVariability, Ranges and Patterns, and Predictive AnalyticsOutliers and Break PointsPatterns, Demand, and HODDOW WOMSOYAttribute VariationVariability and EvolutionSummary: Variability and DemandNotes3. InterdependenciesInterdependenciesInterdependencies in the Current PI MethodologiesThe Missing ElementsThe Biggest Missing Element: VariabilityInterdependencies and Variability: The Origins of DynamismDynamism and Systems AnalysisDynamism and EvolutionWhy Not a "Live" Test, PDCA, or Kaizen Event?Dynamism in Systems Thinking: An IOM/NAE PerspectiveTools for Interdependency AnalysisSummaryNotes4. DCAMM IntroductionCapellini: The Better SpaghettiCapellini and Hospital-Wide FlowDynamic Capacity Analysis, Matching, and Management (DCAMM): Introduction and RefresherCapacity Entitlements and Acceptance PatternsOptimized versus Excess CapacitySummary: Why DCAMM Is NecessaryNote5. Predictive Analytics"Managing To"Simulation Models: The Tools of DCAMM and DPAA Word about Modeling AssumptionsResourcesSo What?What-If’s and Model OutputsEffective Model Use and Learning from DPATime FramesSimulation and the Creation of CreativityStrategic Analysis Using DCAMMModel ScaleThe Community DemandA Word on Real-Time Data and Patient Tracking SystemsSummaryNotes6. Demand Components: The Emergency DepartmentCommunal Demand Recipients: ED as a Source of Downstream DemandDiving into the PatternsArrivals and the Debates on PredictabilityThe ED and DCAMM: Using Patterns to Manage the SystemOther Demand PatternsCase Studies and Sample Outputs: Possible Solutions for ED Flow IssuesEyeball DispositionImpact on DCAMM AnalyticsPhysician on the Front EndResult Waiting AreaUse of Cardiac MarkersSummaryNotes7. Surgical Services and DCAMM AnalyticsSurgical Services and Downstream Demand AnalysisOR TAT’s and First-Case StartsChasing the Rabbit in the ORSurgical Smoothing and Systems ThinkingCase Length Variation and AnalysisSchedule Analytics, the DCAMM WayCase-Fit ScoringSo What? We Get by Just Fine Doing What We Are DoingDownstream Demand and SchedulingCapacity Entitlement and Surgical ServicesSurgical Services Demand and Workload AnalyticsSummaryNotes8. Up–Down–Up: Creating a Systems View from a Component PerspectiveUDU, Processes, and Design ParametersDesign and Component OptimizationFacilities, Communities of Care, ACOs, and CapellinisSummary9. Capacity Patterns and Analytics for DCAMMHow Much Is Enough?Capacity as a Single NumberTips on Making Capacity AvailableAcceptance Patterns and Capacity EntitlementThe Highly Constrained EnvironmentDischarge by XOutliers within OutliersBed Huddles, Acceptance, and EntitlementBed Huddles and the Occasional Outlier (Demand) DaySummaryNotes10. Dynamic Resource Allocations, Dynamic Standardization, and Workload AnalyticsThe Old Way of Creating Unit CapacityThe New WayWorkload Analysis: Two Activity BolusesFrom Admit and Discharge to CensusWorkload and WorkflowA Word on VariabilityTask AllocationDynamic StandardizationDynamic Resource AllocationBreak Points and Task AllocationsSummaryNotes11. A Word on Mandated Nurse–Patient RatiosDynamic StaffingCurrent Legislative EffortsSummary12. Outlier Management and System BalanceOutlier ManagementOutlier ManagementDynamic Systems BalancingPossibilitiesChallengesSummaryConclusion.Epilogue: Kenji’s Story (Continued)