Case Study “Clinical Transformation: Experience of One System”


Published on

Founded in 1852 and with over 53,000 employees, Providence Health & Services is a 27 hospital, plus 100 other kinds of healthcare facilities, organization. In this session, you will learn how Dr. Marton lead Providence’s clinical transformation by learning from and applying the best available practices faster, and more effectively to achieve truly safe, high quality care with the least waste and expense in ALL parts of the system.

Published in: Education, Technology, Business
  • Be the first to comment

  • Be the first to like this

Case Study “Clinical Transformation: Experience of One System”

  1. 1. Keith Marton, MD 1/18/12
  2. 2.  Founded in 1852 Catholic, non-profit Fairly highly integrated system across 5 western states 27 hospitals from critical access to large quaternary facilities Plus over 100 other kinds of facilities ~53,000 employees Annual revenue: ~$8.5 billion NOI typically 4.5-5% ~1500 employed physicians (out of 13,000 medical staff members)
  3. 3. An uncertain future that didn’tlook great
  4. 4. Net Revenue Impact 2010-2012 (millions) ($29) ($86) ($59) ($160) ($109) ($340) ($283) ($428) ($530) ($1,458) AK WAMT OR CA Total Best Worst Best: 1.5% NSR Reduction, Worst: 6.5% NSR Reduction Pa ge 4
  5. 5.  People and Culture ◦ The ability to instill a culture of collaboration, creativity, and accountability. (i.e. a learning organization that embraces a just culture) Business intelligence ◦ The ability to collect, analyze, and connect accurate quality and financial data to support organizational decision making. (More on this later)
  6. 6.  Performance improvement ◦ The ability to use data to reduce variability in clinical processes and improve the delivery , cost- effectiveness, and outcomes of clinical care. (More on this later ) Contract and risk management ◦ The ability to develop and manage effective care networks and predict and manage different forms of patient-related risk. (i.e. integrated ACO’s with good data)
  7. 7. And do it in many areas
  8. 8. Categories of Focus1.Clinical Transformation2.MD Partnership Transformation3.Administrative Transformation4.Balance Sheet Maximization5.Contiguous Market Growth Pa ge 8
  9. 9. Targeted ImpactCategories of Focus (millions)Clinical Transformation $ 784MD Partnership Transformation $ 87Administrative Transformation $ 257Balance Sheet Maximization $ 150Contiguous Market Growth $ 180Total $ 1,458 Pa ge 9
  10. 10.  Data Re-Use, AKA business intelligence, Unified Intelligence, Comprehensive data Warehouse ◦ Relatively new concept for health care ◦ Uses still being defined and explored ◦ Quantification of costs are pretty clear ◦ Quantification of benefits: still emerging
  11. 11.  Reduce the cost of collecting/analyzing data Speed the decision making process and faster spread of innovation, based on near real-time access to information Preclude the need for many, future small database acquisitions Anticipate having data to answer questions that we didn’t know we’d want to ask Identify which data (among many) really need to be standardized Reduced waste and injury Data backup Pa ge 12
  12. 12.  Selected after a look at the options: ◦ review by outside consultants (First, Gartner) ◦ site visits to other users (SJHS) Initial Implementation: ◦ 2 of 4 regions ◦ 7+ “use cases” ◦ Evaluation of technical deployment, user friendliness, future use, cost of ownership Enterprise agreement to support Providence deployment Pa ge 13
  13. 13.  Initial Goal: identify the top 10 uses for initial implementation ◦ Actually, we stopped after 47 potential uses Create the supporting infrastructure ◦ For managing the tool ◦ For spreading and implementing knowledge across the system Connect as many data sources as possible. Pa ge 14
  14. 14.  System implemented in all 4 regions—107 different data inputs Governance/communications structure created Support staff hired--~26 FTE’s (mostly internal staff) Continued training of key users Initial focus on 8 key uses: ◦ Catheter-Associated UTI’s ◦ Modified Early warning system (MEWS) ◦ Sepsis risk ◦ Central line blood stream infection ◦ Readmission tracking Pa ge 15
  15. 15.  Use cases, cont’d ◦ Core measure—CHF D/C ◦ Patient transfer activity ◦ Glycemic monitoring. Key strategic concept: use system to identify patients requiring standardized interventions (but allow staff to also do ad hoc inquiries) Pa ge 16
  16. 16.  In one (smaller) Providence region, it costs $7 million per year to collect and report core measure data ◦ Due to “brute force” data collection: clinicians go on the wards to find core measure candidates and hand tally results. Amalga solution: ◦ Replace brute force method by using electronic data to find those patients and alert ward staff. Expansion of core measures will only increase the cost problem if a data warehouse does not exist.
  17. 17.  A pharmacy alert system ◦ Multiple electronic inputs (lab, pharmacy, ADT) ◦ Locally developed rules scan the inputs and alert pharmacist to intervene with at-risk patients. Impact (at 20 hospitals in 1 system): ◦ $4 million pharmacy savings/ month ◦ 70 serious events averted in 4 months Amalga could have done this, had it been purchased earlier (and will eventually replace that system) Caveat: system worked best where pharmacists worked on the wards.
  18. 18.  Improving sepsis outcomes ◦ Early detection and treatment of sepsis: up to 50% mortality improvement; 30% improvement in LOS and 30% improvement in cost of care. ◦ So, why stop at treating patients who already have sepsis? ◦ Next step: Use Amalga to ID patients at greatest risk for sepsis for intensified monitoring and prevention of sepsis.
  19. 19.  Catheter-associated UTI’s (CA-UTI’s) ◦ 50% of HAI’s ◦ In Providence, HAI’s cost ~$45 million/yr ◦ Equivalent to 247 nurses, who could be put to better use ◦ Prior to Amalga it was impossible to even know who had urinary catheters ◦ Now, catheter patients can be identified and evidence- based standards applied ◦ Expected outcome: 50% reduction in CA-UTI’s
  20. 20. Post MEWS75.465.455.4 Pre MEWS45.435.4 UCL25.4 CL15.4 5.4 LCL-4.6 1/23/2012
  21. 21. 14 Total Pages for Code Team from "PEAT" areas12 UCL10 Post MEWS & PEAT rounds 8 6 CL 4 2 0 1/23/2012
  22. 22. $2,700 $453,600 14 Potential annual savings using MEWS Average Admissions reimbursement 35 that could shortfall for an be avoided ICU admission (HFMA July 2006) (McQuillan 1998) Monthly escalations of care to ICU More importantly… MEWS at PAMC saved lives1/23/2012
  23. 23.  Basic concept: use data mining to detect best practices within one’s own system ◦ Internal best practices more likely to be adopted ◦ e.g. Most cost effective approach to stroke, pneumonia, hip replacement Also, need appropriate communications system and infrastructure to support spread and adoption Ultimately, more rapid adoption of innovation means faster savings, improvements.
  24. 24.  New clinical registries—for relating inputs to outcomes ◦ Orthopedics ◦ Thoracic surgery Real-time ICU dashboards More active data mining by quality department. Incorporation of cost data into the system.
  25. 25.  This is a new tool—not intuitive to many folks ◦ Communicate, communicate, communicate! ◦ Educate, educate, educate! ◦ Involve all stakeholders in the process. This is a pluri-potential tool ◦ Know which strategic goals are key This is an expensive tool ◦ Know (roughly) how it’s going to pay for itself—tell stories to illustrate, know the costs that can be reduced, even though the actual results are not in yet; have an idea why real-time data are important. Pa ge 27
  26. 26.  This is not an expensive tool –compared to the various alternatives Even with a state of the art EMR, this kind of tool makes sense. This is not a magical tool ◦ Understand that it needs to be supported by a skilled staff and effective infrastructure We have probably underestimated its potential uses and value It is primarily limited by the amount of electronic data available ◦ So, we expect that we’ll want to generate even more sources of electronic data Pa ge 28
  27. 27.  Access to real-time data reveals multiple opportunities to improve clinical outcomes AND financial returns In general, the actual benefits have turned out to be greater than the estimated benefits Data alone are not sufficient; also required are: ◦ Skilled data mining and presentation ◦ Supporting infrastructure to act on the data
  28. 28. Questions? Pa ge 30