Clinical Quality Improvement                            Data Driven Decision Support              Guest Lecture for Health...
Overview•   Data Warehousing & Analytics•   Clinical Quality Programs•   Clinical Decision Support•   Case Study Examples ...
Acknowledgements•   Any success and knowledge that I enjoy on this topic is largely a    reflection of their association a...
Northwestern University    Medical School Campus                    • Feinberg School of                        Medicine  ...
Key Facts•   Feinberg School of Medicine     – 360 tenured faculty     – 370 graduates     – 3,000 full-time + contributed...
Issues Related to…DATA WAREHOUSING &ANALYTICS                     6
Data Warehousing:                   The Library Metaphor• Stores all of the books and other reference material you need to...
EDW: The Targeted Value AreasThe data required for…Measurement, Trends, and Patterns              Business              Pe...
The Healthcare Process       Simplified                                                                   Billing &       ...
Multiple, Collaborative OrganizationsHospital X                                                                         Bi...
Why Should You Care           About A Data Warehouse?        Pay for            Consumer         Genetic medicine   Perfor...
Intangible Value of the Data Warehouse•   Increased grant funding     – The EDW tools and data will help attract grants   ...
Data Warehousing in Healthcare•   1980s: Isolated research databases     – Not much electronic clinical data available    ...
Issues Related to…ANALYTIC CONCEPTS &STRATEGY                      14
Books to Read• Books to read which capture the vision   –   “Competing on Analytics: the New Science of Winning”         –...
Sanders’ Hierarchy of Analytic Maturity•   Basic business reporting                                                    Inc...
Mean Time To Improvement• On average, how long does it take an organization to Improve?   –   What is your cultural MTTI?•...
Mean Time To Improvement                      COMPUTERIZED      WORKFLOW-BASED,   ANALYTICS-BASED     RECOGNITION:   BUSIN...
Examples of Clinical Goals•   Decrease the total number of            •   100% compliance to post-surgery    nulliparous e...
DOQ-IT/PQRI Examples                       20
Vertical and Horizontal Strategy                                                                              Neurology   ...
The Advisory Board
The Advisory Board
The Advisory Board
The Advisory Board
The Advisory Board
Measuring Data Quality•   Data Quality = Completeness x Validity    – Can it be measured objectively?•   Measuring “Comple...
Structured vs. Unstructured Data                             • Structured,                             discrete dataComput...
Lessons Learned and…KEY MESSAGES ONANALYTICS                       29
Key Messages• No matter what they tell you, a vendor cannot provide an   “enterprise” data warehouse solution out of the b...
Key Messages• Your data warehouse will only be as good as the  source systems which supply it   –   Don‟t put the cart bef...
Cultural Lessons• Overprotecting access to the data    –   The most secure, least accessible data is also the most useless...
Cultural Lessons• Trying to justify an EDW with a traditional ROI mindset    –   What‟s the ROI of a library in an academi...
Data Content & Structure Lessons•   Blindly believing that star schemas are the solution to everything     – Star schemas ...
Data Content & Structure Lessons•   Failing to recognize that changes will be required in your source    systems to suppor...
Data Content & Structure Lessons•   The importance of master data management     – For front-end data collection     – For...
Data Content & Structure Lessons• Clinical vocabulary standards   –   Failing to balance something better against the purs...
The Case For Timely UpdatesGenerally, to minimize Total Cost of Ownership (TCO), your update frequencyshould be no greater...
In Summary• In the absence of culturally-driven process improvement, data   warehouses are simply costly IT investments wi...
Role of the Enterprise Data Warehouse in…CLINICAL DECISIONSUPPORT                                            40
Medical Evidence Overload?• Medline, alone…    –   4,500 journals in 30 languages    –   11.7 million citations    –   Gro...
Decision Support Interventions                                From an acute                               To a health     ...
What is Clinical Decision Support?  A workflow view  • Synchronous     – Real-time pop-ups, dialog boxes & advisories     ...
CDS Intervention Types   •   Forms and templates   •   Relevant data presentation   •   Proactive order suggestions and or...
Decision Support Interventions                                    A Workflow View                                  All Dec...
ExamplesChanges in quality measures during the first 3 months of the studyMEASURE                                      Sat...
Changes in quality measures during the first 3 months of the studyMEASURE                               Satisfied   Satisf...
Physician Performance                   (most recent 3 months)          Aspirin for Primary Prevention in Diabetes    100 ...
Anticoagulation for Heart Failure with Atrial                          Fibrillation    100    90    80    70    60    50% ...
Cervical Cancer Screening    100     90     80     70     60     50     40%     30     20     10     0    -10    -20
Why Didn’t the Patient            Follow the Protocol?• 167 patient reasons for not following advice for  preventive servi...
Why Didn’t the Physician           Follow the Protocol?• 147 cases in which medical exceptions or modifiers  were given   ...
The Future EHR User Interface•   Patient specific data     – Much like current EHRs     – “Tell me about this patient.”•  ...
Case Study ExamplesINTERMOUNTAINHEALTHCARE                      54
Case Study• Primary Care: Diabetes   – Motive: Improved long-term management of diabetes patients       – RAND Study 2002:...
Big Picture•   Two forms of data driven quality improvement    – Point of care clinical decision support    – Population a...
Point of Care Decision SupportExamples and anecdotes• Antibiotic Assistant• ICU Glucose Manager• MRSA/VRE Alerting System•...
The Antibiotic Assistant• Balancing quality and cost at the point of  careAntibiotic   Dosage   Route   Interval   Predict...
The Antibiotic Assistant Impact• Outcomes improved 47%• Avg # doses declined from 19 -> 5.3• The replicable and bigger sto...
General Lessons• These specific examples of decision support are not    extensible or possible for smaller, less IT capabl...
Diabetes CPM:                                               Key Indicators                                                ...
Case Study: Diabetes Management                                  62
Case Study: Diabetes Management                                  63
Diabetes Management Peer Comparison Chart                                            64
Case Study: Asthma• Primary Care: Asthma   – Motive: Increase controller medication use      – Reduce Asthma related ER vi...
Case Study: Asthma                     66
Case Study: Asthma                     67
Case Study: Asthma                     68
Asthma Peer Comparison Report                                69
Case Study• CV Discharge Medications   – Motive: Basic protocol adherence      – Appropriate discharge meds ordered follow...
Case Study: CV Discharge Meds                                71
Case Study: CV Discharge Meds                                72
The Tangible Benefits                   From Intermountain’s                  Cardiovascular Clinical                     ...
Case Study• Labor and Delivery - Elective Inductions   – Current Care Process Goals: Continued Clinical     Program Focus ...
Percent <39 Weeks                                                  19                                                     ...
Elective Inductions                                                 Estimated Variable Cost Savings From Elective Inductio...
So far, so good…NORTHWESTERN’S DATAWAREHOUSE                      77
Northwestern               • 2.1 billion clinical data points               • 1.9 million patients            Northwestern...
Data Loaded to DateMetric                          ValueNumber of Rows                  3,173,632,200Terabytes            ...
Early Adopters of the EDWCustomer                  Analytic UseNUgene                    Relating genomic data and clinica...
Specific ExampleRapid turnaround (<2 days) to meet a grant submission deadline… For the last year for the women who delive...
Other Examples•   How many patients were prescribed an NSAID and who also had a lab    value which indicated reduced renal...
The High Clinical and Research Value of…DISEASE REGISTRIES                                           85
Disease RegistryA database designed to collect information about the occurrence   and incidence of a particular disease, a...
History of Disease Registries• Historically, the term implies stand-alone, specialized    products and clinical databases ...
Use Cases for Disease RegistriesDisease registries can drive...•   Consistent profiling for prospective, predictive interv...
Disease Registries Data                                • How do we define a particular disease?  SCHEDULING               ...
For Example: Heart Failure•   Inclusion codes based entirely on ICD9, which is a good place to start,    but not specific ...
Disease Registry Exclusions•   The industry will need standard vocabularies for excluding patients    – Removing patients ...
Large n Disease Registries•   Asthma                            •   HIV•   Breast cancer                     •   Hypertens...
Small n Disease Registries• More and more, rare diseases will attract emphasis    from research, pharmas, payers, and fami...
Thank You!• Questions?Dale Sandersdsanders@northwestern.edu312.695.8618                                   94
Data Driven Clinical Quality and Decision Support
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Data Driven Clinical Quality and Decision Support

  1. 1. Clinical Quality Improvement Data Driven Decision Support Guest Lecture for Health Information Science HINF 551 University of VictoriaDale Sanders312-695-8618dsanders@northwestern.edu 1
  2. 2. Overview• Data Warehousing & Analytics• Clinical Quality Programs• Clinical Decision Support• Case Study Examples – Intermountain Healthcare – Northwestern University• Lessons Learned
  3. 3. Acknowledgements• Any success and knowledge that I enjoy on this topic is largely a reflection of their association and support – Much of the material contained herein is their labor, not mine• My current informatics team and clinical champions – Darren Kaiser, Mike Doyle, Andrew Winter, Rex Chisholm, Warren Kibbe – Drs. Jim Schroeder, Abel Kho, David Baker, Steve Persell, David Liebovitz, Gary Martin• Colleagues and teammates from Intermountain Healthcare – Steadfast clinician champions – David Burton, Brent James – My former data warehousing team – Steve Barlow, Dan Lidgard, Kristine Mitchell, Chuck Lyon, Jonathan Despain• Colleagues from the Healthcare Data Warehousing Association – Jack Bates, Deb Alzner, Pat Taylor, Craig Own, Jonathan Einbinder, and others• Professional colleagues from past professional lives – Tom Robison, Mary Carter, Rob Carpenter, Rick Sorensen, Stan Smith, Terri Parkinson, Ron Gault, Bob Bloss• Specific slides from Dr. John Haugom & The Advisory Board Company 3
  4. 4. Northwestern University Medical School Campus • Feinberg School of Medicine • Northwestern Memorial Hospital • Northwestern Medical Faculty Foundation • Children‟s MemorialChicago, Illinois Hospital 4
  5. 5. Key Facts• Feinberg School of Medicine – 360 tenured faculty – 370 graduates – 3,000 full-time + contributed services faculty – 290 National Institutes of Health Principle Investigators – 65 grants in excess of $1M – $852M annual revenue• Northwestern Memorial Hospital – Sole winner of National Quality Health Care Award in 2005 – $1.1B annual revenue – 43,000 admissions – 71,000 ER visits – 744 beds• Northwestern Medical Faculty Foundation – 600 physicians, specialty focused – 1100 employees – 575,000 ambulatory clinic visits/year – $500M annual revenue 5
  6. 6. Issues Related to…DATA WAREHOUSING &ANALYTICS 6
  7. 7. Data Warehousing: The Library Metaphor• Stores all of the books and other reference material you need to conduct your research – The Enterprise data warehouse• A single place to visit – One database environment• Contents are kept current and refreshed – Timely, well choreographed data loads• Staffed with friendly, knowledgeable people that can help you find your way around – Data architects and analysts• Organized for easy navigation and use – Metadata – Data models – “User friendly” naming conventions 7
  8. 8. EDW: The Targeted Value AreasThe data required for…Measurement, Trends, and Patterns Business Performance • Minimize the cost of operations • Maximize the quality of care • In the Minimum time required Clinical Quality &Research Safety Compliance & Accreditation 8
  9. 9. The Healthcare Process Simplified Billing & Billing and AR Claims Claims Processing System Accounts System Processing Receivable Registration & Orders & Encounter Results & Patient Diagnosis Scheduling Procedures Documentation Outcomes Perception •Diagnostics Surveys•ADT System Diagnostic systems Pharmacy Electronic •Pharmacy•Master Patient Index •Lab System Medical Record •Radiology •Imaging •Pathology •Cardiology •Others 9
  10. 10. Multiple, Collaborative OrganizationsHospital X Billing & Billing and AR Claims Claims Processing System Accounts System Processing Receivable Registration & Scheduling Diagnosis Orders & Procedures Encounter Documentation Results & Outcomes Patient Perception EDW A single data perspective on the patient care process •Diagnostics Surveys•ADT System Diagnostic systems Pharmacy Electronic •Pharmacy•Master Patient Index •Lab System Medical Record •Radiology •Imaging •Pathology •Cardiology •Others Billing & Billing and AR Claims Claims Processing System Accounts System Processing Receivable Billing & Billing and AR Claims Claims Processing System Accounts System Processing Receivable Registration & Orders & Encounter Results & Patient Diagnosis Scheduling Procedures Documentation Outcomes Perception Registration & Orders & Encounter Results & Patient Diagnosis Scheduling Procedures Documentation Outcomes Perception •Diagnostics Surveys •ADT System Diagnostic systems Pharmacy Electronic •Pharmacy •Master Patient Index •Lab System Medical Record •Radiology •Diagnostics Surveys •Imaging •ADT System Diagnostic systems Pharmacy Electronic •Pharmacy •Pathology •Master Patient Index •Lab System Medical Record •Cardiology •Radiology •Imaging Hospital Y •Others •Pathology Physician Office Z •Cardiology •Others 10
  11. 11. Why Should You Care About A Data Warehouse? Pay for Consumer Genetic medicine Performance: No pressure on vs. clinical data, no money “safe medicine” outcomes Greaterunderstanding of IRS: Proof of outcomes vs. non-profit status medications Influences driving healthcare towards “measurement” and analytics Payer emphasisConsumer driven to drive downchoice for quality costs Malpractice Greater Sarbanes-Oxley litigation: emphasis on and non-profit Where’s the data driven versions of same proof? clinical research 11
  12. 12. Intangible Value of the Data Warehouse• Increased grant funding – The EDW tools and data will help attract grants – Already seeing the effects of same in recent grants – CTSA – NUgene Genome Wide Association – Electronic Notifications at Care Transitions – Disease Ontology• The best clinical faculty are attracted to good clinical data – Good data = Good research opportunities• Commercial value of clinical data – Funding will be available from pharmas and commercial genomics companies – As a tool to speed their drug trials and genomic discoveries• Preferential negotiations with payers and employers – Transparency of lower costs with higher clinical quality – Negotiations will move faster towards conclusion, too• Greater national recognition – More grants = More published papers• Philanthropic and/or commercial branding – Of the EDW in total, or portions of it 12
  13. 13. Data Warehousing in Healthcare• 1980s: Isolated research databases – Not much electronic clinical data available – Some text based clinical data (natural language processing) – Heavily dependent on ICD9, CPT case mix and financial data• 1990s: Coded, structured clinical data emerges – The value of standardized clinical vocabularies to analytics, reporting, and decision support becomes apparent – Top tier academic and integrated delivery systems build first version data warehouses based on coded clinical data (LOINC, SNOMED, et al)• 2000s: Electronic Medical Records – Electronic clinical data is now becoming more available – “Now that we have this data, let‟s analyze it.”• Current state: Quality, Cost, and Translational Research – Cultural economic emphasis on faster, better, cheaper healthcare – Data warehousing now “the second highest IT priority” (Gartner) among medium-to- large healthcare organizations 13
  14. 14. Issues Related to…ANALYTIC CONCEPTS &STRATEGY 14
  15. 15. Books to Read• Books to read which capture the vision – “Competing on Analytics: the New Science of Winning” – Tom Davenport, Harvard Business School – “Super Crunchers: Why Thinking By Numbers is the New Way to be Smart” – Ian Ayres, Yale Law School – “The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations” – James Surowiecki – “Nudge: Improving Decisions About Health, Wealth, and Happiness” – Richard H. Thaler – “Programming Collective Intelligence” – Toby Segaran, MIT 15
  16. 16. Sanders’ Hierarchy of Analytic Maturity• Basic business reporting Increasing Maturity – Financial and Human Resources• Legal compliance reporting – As required by state and federal law – Cancer Registry, mortality rates, et al• Professional accreditation reporting – Joint Commission, Society of Thoracic Surgeons, et al• Quality of care reporting – Physician Quality Reporting Initiative, Leap Frog, et al• Patient Relationship Management (PRM) – Borrowing from Customer Relationship Management in retail – Tailored to the entire context of the patient – Simpler, faster patient satisfaction and outcomes feedback – Clinical “Loose Ends”• Real-time analytic fusion – Blending patient specific data with general patient type data – “Other physicians who saw patients like this, ordered these medications and tests.” 16
  17. 17. Mean Time To Improvement• On average, how long does it take an organization to Improve? – What is your cultural MTTI?• Healthcare – MTTI is measured in years, sometimes decades – 17 years passed before the “standard” clinical protocol for CAP to be commonly practiced• Examples of low MTTI cultures – Amazon.com; Intel; WalMart; GE; Black & Decker – MTTI measured in weeks and days – Dramatic change in recent years: Microsoft• What drives down MTTI? – Evidence of a better way – Cultural commitment to act – Constant discontent with the status quo 17
  18. 18. Mean Time To Improvement COMPUTERIZED WORKFLOW-BASED, ANALYTICS-BASED RECOGNITION: BUSINESS OR WORKFLOW ALERTS TRANSACTION INFORMATION OPPORTUNITY FORCLINICAL PROCESS AND EMBEDDED INFORMATION SYSTEMS QUALITY DECISION SUPPORT SYSTEMS (BI AND DW) IMPROVEMENT ACTION TAKEN: PROCESS AND QUALITY IMPROVEMENT Mean Time To “Influence” (MTTI) Goal: Squeeze MTTI as close to zero as possible 18
  19. 19. Examples of Clinical Goals• Decrease the total number of • 100% compliance to post-surgery nulliparous elective inductions with radiation therapy protocols for a Bishop Score <10 by 50% breast cancer cases with >4 positive nodes and tumor size• Keep the variable cost increase of >=5cm deliveries without complications resulting in normal newborns to • Compliance with the timing of 5.73% for 2003 administration of Pre-surgical Prophylactic Antibiotic Usage will• For all adult patients with diabetes, exceed 91% increase the percent of patients with LDL less than 100 to >=45.5%. • For patients being treated for (Currently 44.5%) depression, increase the percentage continuing on• Measured glucose values will be prescribed antidepressant for 6 between 60 and 155 mg/dl 80% of months after filling first prescription the time for all ICU patients to >=44.6% 19
  20. 20. DOQ-IT/PQRI Examples 20
  21. 21. Vertical and Horizontal Strategy Neurology Women’s Health Step One: Intensive MedicineClinical Excellence Programs Cardiology Oncology Materials Mgt Registration Admissions Radiology Pharmacy Nursing AR/AP Lab Step Two: Operational Excellence Programs 21
  22. 22. The Advisory Board
  23. 23. The Advisory Board
  24. 24. The Advisory Board
  25. 25. The Advisory Board
  26. 26. The Advisory Board
  27. 27. Measuring Data Quality• Data Quality = Completeness x Validity – Can it be measured objectively?• Measuring “Completeness” – Number of null values in a column• Measuring “Validity” – Cardinality is a simple way to measure validity – “We only have four standard regions in the business, but we have 18 distinct values in the region column.” 27
  28. 28. Structured vs. Unstructured Data • Structured, discrete dataComputer Analytic Value Frustrated here… Comfortable here… • Text • Recorded Audio • Face-to-Face Audio • Video Representation of Human Experience & Knowledge 28
  29. 29. Lessons Learned and…KEY MESSAGES ONANALYTICS 29
  30. 30. Key Messages• No matter what they tell you, a vendor cannot provide an “enterprise” data warehouse solution out of the box – They must be custom built, but they don‟t have to be built from scratch• Mistakes are costly and the root causes are subtle – Mistakes emerge insidiously and late in the lifecycle of data warehouses, when they are the most costly to repair• It‟s easy to avoid the common causes of failure in data warehousing, if you have made them before – Unlike EMR/EHR deployments, where making the same mistake over and over is sometimes hard to avoid – Collaborate with the growing membership of the Healthcare Data Warehousing Association (www.hdwa.org) – No membership fees 30
  31. 31. Key Messages• Your data warehouse will only be as good as the source systems which supply it – Don‟t put the cart before the horse• Technology is only half of the equation – Culture is the other half – The ROI from an EDW comes from a cultural willingness to use the tool – To drive down costs and improve quality – Your organization must be committed to continuous quality improvement – Otherwise, the IT of EDW is a lost investment 31
  32. 32. Cultural Lessons• Overprotecting access to the data – The most secure, least accessible data is also the most useless – Trust your data analysts who have access, but verify with audits• Data warehouse staff that can‟t work with customers – You need a librarian‟s personality and skill set – But they also need to be technical• Assuming an EDW is going to solve your business problems – It‟s only a tool – Must be deployed with a process improvement culture• Failing to hire adequate numbers of skilled data analysts – Like building a library in an illiterate community 32
  33. 33. Cultural Lessons• Trying to justify an EDW with a traditional ROI mindset – What‟s the ROI of a library in an academic medical center? – What‟s the ROI of your telephone system?• Trying to build an EDW using traditional software project management methodologies – Waterfall development techniques don‟t apply – In-depth requirements analysis and use cases are a waste of time and money 33
  34. 34. Data Content & Structure Lessons• Blindly believing that star schemas are the solution to everything – Star schemas are terrible for many of today‟s data analysis problems – They can be useful, but use them sparingly and with caution – Analysts love flat tables with lots of rows and columns– there‟s a reason Excel is so popular• Trying to “clean” data from the source systems before it is loaded into the data warehouse – For example, forcing your data to align with national standards, such as SNOMED, when the source systems don‟t align themselves – A never ending battle – Push accountability where it belongs• Standardizing the names of data structures for the sake of standardization – Making significant changes to the names and structure of data supplied by the source system – You will lose data familiarity with stewards who have used the data for years with the old naming conventions 34
  35. 35. Data Content & Structure Lessons• Failing to recognize that changes will be required in your source systems to support your data warehouse and analytics strategy – You will find that the data being collected in source systems is not optimized to support analytics – You will need to change human processes associated with data collection – You will need to change the way applications are written to collect data in the source systems• Very poor or no metadata repository – Where would the telephone be without the Yellow Pages? – Maybe the most overlooked, underestimated aspect of a data warehouse – Quite often considered a luxury item… it‟s not!• Overly complex security roles – Trying to be too granular with roles – Which actually leads to greater insecurity and risk• Sacrificing joins to minimize redundant data and storage space – Storage is cheap – Joins are expensive to CPUs and to data analysts 35
  36. 36. Data Content & Structure Lessons• The importance of master data management – For front-end data collection – For back-end data validation and analysis• When in doubt, extract more data, not less – Even if you don‟t think you‟ll need the data, extract it from the source system anyway – Chances are, you will eventually find a need for nearly all collected data• Under appreciating the role of Data Stewards – Formally assigning, by name, a Data Steward for each data content area – They can assist with proper use of the data (training) as well as data quality ownership• Assuming that all analysis must pass through a tool like Cognos or BusinessObjects – Those tools will appeal to a certain demographic customer, but not all – Allow direct access to tables for sophisticated analysts… What‟s the worst that can happen? – Accommodate the needs of Designers, Drillers, and Clickers 36
  37. 37. Data Content & Structure Lessons• Clinical vocabulary standards – Failing to balance something better against the pursuit of perfection – Many healthcare organizations, especially academics, look constantly for the perfect vocabulary tool or semantic model, like UMLS – In the meantime, they can‟t even managed basic terms and content around the most fundamental issues like Patient Identity and Provider Identity• “The art of being wise is knowing what to overlook.” – William James, Principles of Psychology, 1890 37
  38. 38. The Case For Timely UpdatesGenerally, to minimize Total Cost of Ownership (TCO), your update frequencyshould be no greater than the decision making cycle associated with the data.But… everyone wants more timely data. 100 % Requests for Data utilization 0 Today 1 year 2 years Data Age 38
  39. 39. In Summary• In the absence of culturally-driven process improvement, data warehouses are simply costly IT investments with no value• The future of data warehousing is at the frontend of the care delivery model, affecting what‟s happening – Not at the backend of reporting, wondering what happened• Data warehouses are relatively simple and safe to build – Despite their high failure rates – Look around… ask for advice… and stop reading Kimball, Inmon, and Imhoff  39
  40. 40. Role of the Enterprise Data Warehouse in…CLINICAL DECISIONSUPPORT 40
  41. 41. Medical Evidence Overload?• Medline, alone… – 4,500 journals in 30 languages – 11.7 million citations – Growth rate: 400,00 per year• No time to read – “As a general practitioner, how many hours per week do you have time to read to stay current in your profession?” – ½ hour or less per week: 3% – 1 hour: 46% – 1 ½ hours: 23% – 2 hours: 20% – 3+ hours: 8%• We need to deliver evidence at the point of care – Embedded smoothly in the clinicians‟ workflow 41
  42. 42. Decision Support Interventions From an acute To a health Illness enterprise improvement system Overall Quality of Care Complexity & Investment Population- based care Disease management Managing episodes of care “Random acts of clinical improvement” TimeDr. John Haugom 42
  43. 43. What is Clinical Decision Support? A workflow view • Synchronous – Real-time pop-ups, dialog boxes & advisories – Disruptive of workflow – Used only for high-value/high-risk situations • Asynchronous – Not real-time; usually is feedback after a decision is made – Can be as simple as a report • Blended – Semi-immediate, like an e-mail – Inbox population from background surveillanceDr. John Haugom 43
  44. 44. CDS Intervention Types • Forms and templates • Relevant data presentation • Proactive order suggestions and order sets • Support for guidelines, complex protocols, algorithms, clinical pathways • Reference information and guidance • Reactive alerts (i.e., unsolicited by patient or clinician recipient)Dr. John Haugom 44
  45. 45. Decision Support Interventions A Workflow View All Decision Support Synchronous Asynchronous Real-time •Defaults •E-mail •Population •Scorecards alerts •Menus •Inbox Surveillance/ •Population •Embedded infor- HMA‟s Reports Mation ( •Chronic Care •Order sets & Protocols protocols) •Order/Referral •Clinician access Follow-up to content (“pull”) CDSS HIS/EMR Data Mining Increasing ImmediacyDr. John Haugom 45
  46. 46. ExamplesChanges in quality measures during the first 3 months of the studyMEASURE Satisfied (%) Satisfied (%) Satisfied (%) Sept 301, 2007 Dec 31, 2007 April 30, 2008Coronary Heart Disease Beta blocker in MI 0.89 0.91 0.91 Antiplatelet drug 0.90 0.89 0.91 Lipid lowering drug 0.88 0.88 0.89 ACE inhibitor/ARB in DM or LVSD 0.84 0.84 0.85Heart Failure ACE inhibitor/ARB in LVSD 0.86 0.87 0.85 Anticoagulation in atrial fibrillation 0.63 0.64 0.72 Beta blocker in LVSD 0.83 0.84 0.85Hypertension control 0.76 0.75 0.76Diabetes Mellitus Blood pressure management 0.60 0.60 0.63 HbA1c control ( < 8) 0.63 0.65 0.64 LDL control 0.51 0.51 0.52 Aspirin for primary prevention 0.76 0.77 0.83 Nephropathy screening/management 0.81 0.82 0.83
  47. 47. Changes in quality measures during the first 3 months of the studyMEASURE Satisfied Satisfied Satisfied (%) (%) (%) Sept Dec 31, April 30, 301, 2007 2008 2007Prevention Screening mammography 0.79 0.80 0.84 Cervical cancer screening 0.80 0.81 0.80 CRC screening 0.49 0.48 0.47 Pneumococcal vaccination 0.49 0.52 0.54 Osteoporosis screening or 0.76 0.79 0.82therapy
  48. 48. Physician Performance (most recent 3 months) Aspirin for Primary Prevention in Diabetes 100 90 80 70 60 50% 40 30 20 10 0 -10 -20
  49. 49. Anticoagulation for Heart Failure with Atrial Fibrillation 100 90 80 70 60 50% 40 30 20 10 0 -10 -20
  50. 50. Cervical Cancer Screening 100 90 80 70 60 50 40% 30 20 10 0 -10 -20
  51. 51. Why Didn’t the Patient Follow the Protocol?• 167 patient reasons for not following advice for preventive service – 9 have resulted in patient having service• 2 patients unable to afford medication• 14 patients refused medication – 0 have started medication
  52. 52. Why Didn’t the Physician Follow the Protocol?• 147 cases in which medical exceptions or modifiers were given – 132 appropriate on initial review – 5 discussed with another reviewer and judged appropriate – 4 discussed with another reviewer and judged inappropriate: feedback given – 6 reviewed with peer reviewer and expert and recommended change in management
  53. 53. The Future EHR User Interface• Patient specific data – Much like current EHRs – “Tell me about this patient.”• Disease management data – “Tell me about managing patients like this.”• Treatment options data – “Tell me about my options for treating this patient.” – “Tell me about the most common tests and medications ordered for patients like this.”• Cost of care data – “Tell me about how much these treatment options cost.”• Quality of care data – “Tell me how satisfied patients were with these treatment options.” 53
  54. 54. Case Study ExamplesINTERMOUNTAINHEALTHCARE 54
  55. 55. Case Study• Primary Care: Diabetes – Motive: Improved long-term management of diabetes patients – RAND Study 2002: “64% of diabetic patients receive inadequate care.” – Integrates five disparate data sources – Lab – Problem list – Insurance claims: CPT‟s and pharmacy – In-patient pharmacy – Hospital ICD-9 – This one hits home – Winner – National Exemplary Practice Award 2002 – American Association of Health Plans 55
  56. 56. Big Picture• Two forms of data driven quality improvement – Point of care clinical decision support – Population and process improvement 56
  57. 57. Point of Care Decision SupportExamples and anecdotes• Antibiotic Assistant• ICU Glucose Manager• MRSA/VRE Alerting System• ARDS Vent Weaning Protocols• Drug-Drug Interaction Alerts 57
  58. 58. The Antibiotic Assistant• Balancing quality and cost at the point of careAntibiotic Dosage Route Interval Predicted AverageProtocol Efficacy Cost/Patie ntOption 1 500mg IV Q12 98% $7,256Option 2 300mg IV Q24 96% $1,236Option 3 40mg IV Q6 90% $1,759…Option 10 58
  59. 59. The Antibiotic Assistant Impact• Outcomes improved 47%• Avg # doses declined from 19 -> 5.3• The replicable and bigger story – Antibiotic cost per treated patient: $123 -> $52 – By simply displaying the cost to physicians – Information Technology created the illusion and benefits of First Order Economics…! 59
  60. 60. General Lessons• These specific examples of decision support are not extensible or possible for smaller, less IT capable organizations – Teams of MDs and PhDs build and maintain these systems – Vendors have not been successful in making these systems possible for smaller organizations• Drug-Drug Interaction alerts have generally been a failure – Many organizations turn them off completely 60
  61. 61. Diabetes CPM: Key Indicators Measure Goal HbA1c (test at least 2 times a <7.0% year) Blood Pressure <130/80 mm (check at each office visit) Hg LDL Cholesterol <100 mg/dL (test at least every 2 years) Triglycerides <150 mg/dL (test at least every 2 years) Foot Exam (perform at least normal annually) Urine Microalbumin/Creatinine <30 Ratio (test at least annually) Dilated Eye Exam (check normal annually, or every 2 years if well controlled)Intermountain Healthcare, Steve Barlow 61
  62. 62. Case Study: Diabetes Management 62
  63. 63. Case Study: Diabetes Management 63
  64. 64. Diabetes Management Peer Comparison Chart 64
  65. 65. Case Study: Asthma• Primary Care: Asthma – Motive: Increase controller medication use – Reduce Asthma related ER visits – Source of data: Health Plans Claims and ER records 65
  66. 66. Case Study: Asthma 66
  67. 67. Case Study: Asthma 67
  68. 68. Case Study: Asthma 68
  69. 69. Asthma Peer Comparison Report 69
  70. 70. Case Study• CV Discharge Medications – Motive: Basic protocol adherence – Appropriate discharge meds ordered following CV (IHD and MI) diagnosis and treatment – Results – 1994: 15% (estimate, no hard data) – 2004: 98% (hard data) 70
  71. 71. Case Study: CV Discharge Meds 71
  72. 72. Case Study: CV Discharge Meds 72
  73. 73. The Tangible Benefits From Intermountain’s Cardiovascular Clinical Program 73
  74. 74. Case Study• Labor and Delivery - Elective Inductions – Current Care Process Goals: Continued Clinical Program Focus – Continue to educate physicians and patients on the safe and efficacious practice of elective labor induction. – To maintain at ≤5% elective deliveries that do not meet strict criteria (39 weeks gestation) developed by the Intermountain Obstetrical Development Team. – To measure clinical outcomes of care and report quarterly by provider. Intermountain Healthcare, Steve Barlow 74
  75. 75. Percent <39 Weeks 19 99 0% 5% 10% 15% 20% 25% 30% 35% J Fan Meb Aar M pr a Ju y Jun Au l Seg Op 20 Noct Dv 00 e J c Fan Meb Aar M pr a Ju y Jun Au l Seg Op 20 Noct Dv 01 e J c Fan Meb Aar M pr a Ju y Jun Au l g Intermountain Healthcare, Steve Barlow Se Op 20 Noct 02 Dev J c Fan Meb Aar M pr a Ju y Jun Au l Se g Op Month 20 Noct 03 Dev J c Fan Intermountain Healthcare Meb Aar Elective Deliveries <39 Weeks M pr a Ju y Jun Au l Seg Op 20 Noct Elective Inductions 04 Dev J c Fan Meb Aar M pr a Ju y Jun Au l Seg Op 20 Noct 05 Dev J c Fan Meb Aar M pr a Ju y Jun Au l Seg Op N ct Dov ec75
  76. 76. Elective Inductions Estimated Variable Cost Savings From Elective Induction Protocol Intermountain Healthcare 2001-2005 $700,000 $1,600,000 $597,367 $1,400,000 $600,000 $1,200,000 Cumulative Variable Cost Savings $500,000 Variable Cost Savings $1,000,000 $400,000 $380,833 $800,000 $300,000 $600,000 $207,772 $188,606 $200,000 $400,000 $100,000 $200,000 $26,479 $- $- 2001 2002 2003 2004 2005 Year Yearly Savings Cumulative SavingsIntermountain Healthcare, Steve Barlow 76
  77. 77. So far, so good…NORTHWESTERN’S DATAWAREHOUSE 77
  78. 78. Northwestern • 2.1 billion clinical data points • 1.9 million patients Northwestern Medicine Enterprise Data Warehouse (EDW)Hospital Research Data Data Clinic Data 78
  79. 79. Data Loaded to DateMetric ValueNumber of Rows 3,173,632,200Terabytes 2.2Truckloads 1,233Complete works of Shakespeare 252,483
  80. 80. Early Adopters of the EDWCustomer Analytic UseNUgene Relating genomic data and clinical profiles for phenotyping high risk diseases such as diabetes and asthmaNeurosurgery A summary of new patients, encounters and diagnoses from the EDW is import daily into MDAnalyze, a Neurosurgery outcomes databaseAlan Peaceman, MD Creation of a perinatal patient registry for studying clinical quality outcomes; BMI relationships to complicationsBill Grobman, MD Statistics of deliveries at NMH in preparation for a grant proposalDana Gossett, MD Application of Systemic Inflammatory Response Syndrome (SIRS) criteria to pregnant and postpartum women with infectious complicationsAndrew Naidech, MD First adopter of the Research Patient Data Aggregator for use in research and clinical quality assessment of subarachnoid hemorrhage, intracerebral hemorrhage, and stroke patientsNMH Process Improvement A DMAIC project aimed at improving the quality of care for patients seen with bone fractures at NMH. Used the EDW to help narrow and speed their search for bone fracture patients using a query of text-based Radiology reports. 81
  81. 81. Specific ExampleRapid turnaround (<2 days) to meet a grant submission deadline… For the last year for the women who deliver, provide… • mean age and standard deviation • percent between 18-34, inclusive • ethnic breakdown, at least by white, black, latino • % smokers • % singletons (i.e. no twins or triplets) • % who receive their prenatal care with an NMH doc • mean BMI and standard deviation • % BMI < 19 • % BMI 19 - 29.9 • % BMI > 29.9 • % who start prenatal care in the first trimester 82
  82. 82. Other Examples• How many patients were prescribed an NSAID and who also had a lab value which indicated reduced renal function (lab result of GFR < 50 or Creatinine > 1.5)? – Answer: 725 out of 16214 in calendar year 2007• What percentage of patients diagnosed with multiple myeloma in remission over age 18 were prescribed bisphosphonates in the past 12 months? – Answer: 18%• How many patients who have had 1 or more low LVEF (<40) measurements in our outpatient echo system (Xcelera) and who have received a low LVEF measurement within the last 180 days and who have not seen one of the following doctors in an NMFF office visit within the last 120 days? – KADISH, ALAN H. – GOLDBERGER, JEFFREY J. – PASSMAN, ROD S. – DENES, PABLO – JACOBSON, JASON„ – Answer: 309 83
  83. 83. The High Clinical and Research Value of…DISEASE REGISTRIES 85
  84. 84. Disease RegistryA database designed to collect information about the occurrence and incidence of a particular disease, and for which, the inclusion criteria are defined in such a manner that minimizes variability within the included cohort.“Computer Applications used to capture, manage, and provide information on specific conditions to support organized care management of patients with chronic disease.” --”Using Computerized Registries in Chronic Disease Care”; California Healthcare Foundation and First Consulting Group, 2004. 86
  85. 85. History of Disease Registries• Historically, the term implies stand-alone, specialized products and clinical databases – Our premise: No more stand alone registries – They must be integrated within an overall EMR and Data Warehouse strategy• Pioneered by GroupHealth of Puget Sound in the early 1980s for diseases other than cancer – “Clinically related information system”• Long precedence of use and effectiveness in Cancer – 1926: First cancer registry at Yale-New Haven hospital – 1935: First state, centralized cancer registry in Connecticut – 1973: Surveillance, Epidemiology, and End Results (SEER) program of National Cancer Institute, first national cancer registry – 1993: Most states pass laws requiring cancer registries 87
  86. 86. Use Cases for Disease RegistriesDisease registries can drive...• Consistent profiling for prospective, predictive intervention – The goal is to keep people off of disease registries, but first you have to know how those who are on the registry, got there…• Best practice guidelines within the EMR – Guideline-based intervals for tests, follow-ups, referrals – Interventions that are overdue – “Recommend next HbA1C testing at 90 days because patient is not at goal for glucose control.”• Outreach communication to patients – Reminders about care and intervention• Consistent patient education for all members on the disease registry• Quality of care reporting to payers and employers• Feedback reports to physicians about their care practices• Population reporting and analysis for research• Process improvement projects for service line clinical programs 88
  87. 87. Disease Registries Data • How do we define a particular disease? SCHEDULING • Who has the disease? • What is their demographic profile? REGISTRATION MORTALITY PATH * DISEASE MANAGEMENT * OUTCOMES ANALYSIS TUMOR REG * RESEARCH * P4P REPORTING * CLINICAL TRIALS ENROLLMENT RAD RESULTS LAB RESULTS INCLUSION CRITERIA & COSTS & DISEASE MEDICATIONS STRUCTURED REIMBURSEMENT REGISTRY EXCLUSION DATA CODES ICD9 CODES CPT CODES PATIENT PROVIDER CLINICAL OBS RELATIONSHIP PROBLEM LIST PATIENT • Are we managing these patients according to VALIDATION accepted best protocols? CLINICIAN • Which patients had the best outcomes and why? VALIDATION • Where is the optimal point of cost vs. outcome? CARDIOLOGY IMAGING 89
  88. 88. For Example: Heart Failure• Inclusion codes based entirely on ICD9, which is a good place to start, but not specific enough – Heart failure codes for study inclusion – 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.xx – Exclusion criteria for beta blocker use† – Heart block, second or third degree: 426.0, 426.12, 426.13, 426.7 – Bradycardia: 427.81, 427.89, 337.0 – Hypotension: 458.xx – Asthma, COPD: see above – Alzheimers disease: 331.0 – Metastatic cancer: 196.2, 196.3, 196.5, 196.9, 197.3, 197.7, 198.1, 198.81, 198.82, 199.0, 259.2, 363.14, 785.6, V23.5-V23.9 – † Exclusion criteria were only assessed for patients who did not have a medication prescribed; Thus, if a patient was prescribed a medication but had an exclusion criteria, the patient was included in the numerator and the denominator of the performance measure. If a patient was not prescribed a medication and met one or more of the exclusion criteria, the patient was removed from both the numerator and the denominator. Acknowledgements to Dr. David Baker, NU Feinberg School of Medicine 90
  89. 89. Disease Registry Exclusions• The industry will need standard vocabularies for excluding patients – Removing patients from the registry whose data would otherwise skew the data profile of the cohort• “Why should this patient be excluded from this registry, even though they appear to meet the inclusion criteria?” – Patient has a conflicting clinical condition – Patient has a conflicting genetic condition – Patient is deceased – Patient is no long under the care of this facility or physician – Patient is voluntarily non-compliant with the care protocol – Patient is incapable of complying with the care protocol• You can see that the exclusion criteria imply a connection between a patient‟s inclusion and their managed care – This might not be true in all cases, e.g., research 91
  90. 90. Large n Disease Registries• Asthma • HIV• Breast cancer • Hypertension• Cataracts • Lower back pain• Chronic lymphocytic leukemia • Systemic Lupus• Chronic obstructive pulmonary • Macular degeneration disease • Major depression• Colorectal cancer • Migraines• Community acquired bacterial • MRSA/VRE pneumonia • Multiple myeloma • Myelodysplastic syndrome & acute leukemia• Coronary artery bypass graft • Myocardial infarction• Coronary artery disease • Obesity• Coumadin management • Osteoporosis• Diabetes • Ovarian cancer• End stage renal • Preoperative antibiotic prophylaxis • Prostate cancer• Gastroesophageal reflux disease • Rheumatoid Arthritis• Glaucoma • Sickle Cell• Heart failure • Upper respiratory infection (3-18 years)• Stroke (Hemorrhagic and/or • Urinary incontinence (women over 65) Ischemic) • Venous thromboembolism prophylaxis• High risk pregnancy 92
  91. 91. Small n Disease Registries• More and more, rare diseases will attract emphasis from research, pharmas, payers, and families• We can plan our disease registry strategy now• For example… – Amyotrophic Lateral Sclerosis – Alzheimers – Hemophilia – Hodgkins Disease – Rett Syndrome – Scleroderma 93
  92. 92. Thank You!• Questions?Dale Sandersdsanders@northwestern.edu312.695.8618 94

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