Professor, Department of Anesthesiology From “Solution Shop” to “Focused Factory ” in Cardiothoracic Surgery David J. Cook, M.D. Professor, Department of Anesthesiology Mayo Clinic College of Medicine Center for the Science of Healthcare Delivery Has no relevant financial relationships to disclose. Will not be discussing off-label/investigative use(s) of commercial devices.

Case Study: The Medicare Gap in CVS Reduce cost per case by 20% Operational analysis: Stakeholder analysis 2

CV Surgical Care Model: “Who’s patient is this?”

CV Surgical Care Model: “Who’s patient is this?”

Unwarranted variation (total variable costs): Daily matching demand and capacity ICU length of stay (routine) Surgical Hours Duration UCI

Christensen HBS: Typology of Business Models: example Payment model Solution Shop Unstructured problems, undifferentiated complexity, expertise and intuition Consulting, architecture, law, medicine Fee for service Value-adding Process (VAP) (via Focused Factory) Process application increases value, manage product (output) Refining, Manufacturing, Healthcare (surgicenter, imaging, lab facilities) Pay for outcome FFS Facilitated network Manages exchanges: services, data or information Cable services, insurance, banking Membership, subscription, transaction fees

Solution Shop Model: Unstructured problem: Decision making by ‘expert” Unique knowledge, training Experience and intuition

Value-Adding Process Model: Solution Shop Model: Value-Adding Process Model: Create a standardized, protocol-based pathway across care geographies (Focused Factory) Support delivery via communication, information-systems, geography Empower bedside providers and shift decision-making

Product and Process Specifications Informed/empowered bedside provider UCO Ambulation Chest tubes out Pacer wires out Anesthetic management Hemodynamic Drips Blood Component Therapy Antibiotics Wean mechanical ventilation Fluid management Wean hemodynamic support Remove “lines”

Process: Urinary Catheter Out (UCO) Cook, Thompson et al., Am Journal of Medical Quality Aug, 2013

Resource Utilization: ICU Length of Stay Vertical histograms showing distribution for duration of ICU length of stay control (2008-upper panel) and intervention (2012- lower panel) groups (n = 769 for each group). Median and mean values as well as 95% confidence intervals are shown below respective panels. Cook, Pulido et al. Annals of Surgery Dec. 2014

UHC: Total Variance from Predicted LOS (DRG’s 216 – 221) 2008 2012

In-Hospital Safety: Variable§ Pre (2008) (N=769) Post (2012) P-value* Initial ventilation duration (h) †   Re-intubated Initial ICU length of stay (h) Readmission to the ICU 9.3 (6.2, 14.5) 5 (1%) 26.3 (23.6, 44.3) 19 (3%) 6.3 (4.7, 9.2) 1 (0%) 22.5 (20.0, 25.8) 18 (2%) <.001 0.22 1.0 * P-values are from rank-sum tests for continuous variables and Fisher’s exact tests for categorical variables. †There were 14 missing values for initial ventilation duration. § Values are n (%) for categorical variables and median (q1, q2) for continuous variables. Cook, Pulido et al. Ann Surg December 2014

* * * * 14-15% reduction In cost Cook, et al. Health Affairs: May2014 * * 14-15% reduction In cost * * Distribution of costs pre- and post-implementation of focus factory model is shown. The red line of in each box indicates the median cost; the lower and upper border of the boxes show the 25th and 75th percentiles of the cost distribution, while the stars (*) indicates the outliers. (n=769 per group).

Process metrics → Outcome Metrics Cook, et al. Health Affairs: May2014 Process metrics → Outcome Metrics

Cook, et al. Health Affairs: May2014 COST 2012 data

Observations/Conclusions: Health care delivery provides a product as a result of a process (business model) Complexity and unwarranted variation drive poorer outcomes and higher cost Specifications can be established for both process and product → Predictability

Cost/Complexity Distribution in Populations: Very different practice (business) models Implications for: decision model (expert or algorithm) work model practice improvement normative data on process, outcome and cost

Learning Objectives: By the end of this session, participants should be able to: Understand the origins of unwarranted variations in surgical care Understand means to reduce unwarranted variations Understand the implications of the distribution of cost and care complexity for populations of surgical patients