Looking Back on Clinical Decision Support and Data Warehousing
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Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
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Looking Back on Clinical Decision Support and Data Warehousing
- 1. Culturally-Driven Process Improvement Enabled By Technology Guest Lecture for Health Information Science HINF 551 University of Victoria May 2008 Clinical Decision Support and Data Warehousing Dale Sanders 312-695-8618 dsanders@nmff.org
- 2. 2 • Complex, life-critical, time-critical computerized decision support • It all boils down to managing false positives and false negatives, then optimizing your intervention and response My background US Air Force Command, Control, Communications, Computers & Intelligence (C4I) Officer TRW/National Security Agency • START Treaty • Nuclear Non- proliferation • US nuclear weapons threat reduction Director of Medical Informatics, LDS Hospital/Intermountain Healthcare CIO, Northwestern CIO, Cayman Islands National Health System Product Development, Health Catalyst 20161983 Reagan/Gorbachev Summits Nuclear Warfare Planning and Execution– NEACP & Looking Glass
- 3. 3 Acknowledgements & Thanks Robert Jenders, MD, MS Associate Professor, Dept of Medicine, Cedars-Sinai Medical Center & UCLA Co-chair, HL7 Clinical Decision Support TC & Arden Syntax SIG R. Matthew Sailors, PhD Assistant Professor, Dept of Surgery, UT-Houston Co-chair, HL7 Clinical Decision Support TC & Arden Syntax SIG Clinical Decision Support and Arden Syntax
- 4. Overview • Patient information systems trends & concepts • Enterprise Data Warehouse (EDW) – Basic Terms and Concepts – Case Study Examples – Intermountain Healthcare – Northwestern University • Clinical Decision Support
- 5. 5 Information Systems: The Three Perspectives Transaction Systems: Collecting data that supports analytics & efficient workflow Analytic Systems: Aggregating and exposing data to improve workflow Knowledge Systems: Organizing, sharing, and linking information • Query and reporting tools • Enterprise data warehouses • Benchmarking data • Document imaging • Videoconferencing • Collaboration tools • Intranets/Internet access • Search engines • EMR’s • Billing systems • GL systems • HR systems • Scheduling systems • Inventory management systems Goal Measurement Goal achievement Goal Achievement Designed to support
- 6. 6 Patient Information Systems Trends Transportability and Interoperability – Information moves with the patient Real-time alerts and reminders – Drug-drug and drug-allergy interactions Data-driven treatment planning Disease management at the point-of-care Payer-driven data collection – Pay for Performance (P4P) Quality of care reporting Transparency of cost is coming
- 7. 7 Health consumerism movement – Demands for improved and more transparent information access – Demands for more security and privacy – The “credit report” phenomenon Computerized patient records – Legislation and state and federal initiatives are supporting investment in collaborative software Regional health information networks are receiving funding – For collaborative clinical information sharing and for pay-for-performance initiatives Patient Information Systems Trends
- 8. 8 Patient Care Data “Customers” Patient Care Data Financial HIS Coding (HDM) A/R Management Standard Costing Materials Management Case Mix Clinical Patient Safety Clinical Programs Clinical Support Services Case Mix Accreditation/Regulatory JCAHO, NCQA, HEDIS HIPAA, EMTALA, OSHA, CLIA Third-party Payers Claims information Utilization management Case management
- 9. 9 Meaningful, maintainable point-of-care clinical decision support •Registration •Scheduling •Accts Receivable •Patient/payer billing •Reporting •HIPAA claims, eligibility, remittance •Benefit plan tracking •Co-pay tracking •Referral management •COB •Risk management •Patient education •Encounter documentation •Charge capture •Diagnostic coding •ePrescribing •Allergy alerts •D-D interactions •Medical history •Messaging & real time collaboration •Patient portal • Self-scheduling • Self-registration • Account management • Results & history • Rx refills • Credit card payment •Lab interfaces •Payer/clearinghouse interfaces (HIPAA) •Integrated orders •Integrated results •ePrescribing •Patient education •Clinical references within context •Affiliated referring partners Business Intelligence/”Pay for Performance” Metrics Workflow & Handoff Between Clinical and Business Processes Core Best Practices Reminders Meaningful Alerts Advantage Differentiator Off The Edge Regional/External Entities Functional Framework: Electronic Health Record Leading Edge • Rare & difficult • The next frontier
- 10. 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.” • Clinical outcomes data – “Tell me how satisfied patients were with these treatment options.” 10
- 12. 12
- 14. 14 Multiple, Collaborative Organizations EDW A single data perspective on the patient care process Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index •ADT System •Master Patient Index PharmacyPharmacy Electronic Medical Record Electronic Medical Record SurveysSurveys•Diagnostics •Pharmacy •Diagnostics •Pharmacy Billing and AR System Billing and AR System Claims Processing System Claims Processing System Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index •ADT System •Master Patient Index PharmacyPharmacy Electronic Medical Record Electronic Medical Record SurveysSurveys•Diagnostics •Pharmacy •Diagnostics •Pharmacy Billing and AR System Billing and AR System Claims Processing System Claims Processing System Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnostic systems •Lab System •Radiology •Imaging •Pathology •Cardiology •Others Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index Pharmacy Electronic Medical Record Surveys•Diagnostics •Pharmacy Billing and AR System Claims Processing System Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation •ADT System •Master Patient Index •ADT System •Master Patient Index PharmacyPharmacy Electronic Medical Record Electronic Medical Record SurveysSurveys•Diagnostics •Pharmacy •Diagnostics •Pharmacy Billing and AR System Billing and AR System Claims Processing System Claims Processing System Hospital X Hospital Y Physician Office Z
- 15. Sanders’ Hierarchy of Analytic Maturity • Basic business reporting – 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.” 15 Increasing Maturity
- 16. Healthcare Analytics Adoption Model Level 8 Personalized Medicine & Prescriptive Analytics Tailoring patient care based on population outcomes and genetic data. Fee-for-quality rewards health maintenance. Level 7 Clinical Risk Intervention & Predictive Analytics Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment. Level 6 Population Health Management & Suggestive Analytics Tailoring patient care based upon population metrics. Fee- for-quality includes bundled per case payment. Level 5 Waste & Care Variability Reduction Reducing variability in care processes. Focusing on internal optimization and waste reduction. Level 4 Automated External Reporting Efficient, consistent production of reports & adaptability to changing requirements. Level 3 Automated Internal Reporting Efficient, consistent production of reports & widespread availability in the organization. Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content. Level 1 Enterprise Data Warehouse Collecting and integrating the core data content. Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting. 16
- 17. 17 Lab Admissions Radiology Registration Pharmacy Nursing AR/AP MaterialsMgt Vertical and Horizontal Strategy Intensive Medicine Cardiology Oncology Women’s Health Neurology Step One: Clinical Excellence Programs Step Two: Operational Excellence Programs
- 18. 18 Examples of Clinical Goals • Decrease the total number of nulliparous elective inductions with a Bishop Score <10 by 50% • Keep the variable cost increase of deliveries without complications resulting in normal newborns to 5.73% for 2003 • For all adult patients with diabetes, increase the percent of patients with LDL less than 100 to >=45.5%. (Currently 44.5%) • Measured glucose values will be between 60 and 155 mg/dl 80% of the time for all ICU patients • 100% compliance to post-surgery radiation therapy protocols for breast cancer cases with >4 positive nodes and tumor size >=5cm • Compliance with the timing of administration of Pre-surgical Prophylactic Antibiotic Usage will exceed 91% • For patients being treated for depression, increase the percentage continuing on prescribed antidepressant for 6 months after filling first prescription to >=44.6%
- 20. The Advisory Board Company
- 21. The Advisory Board Company
- 22. The Advisory Board Company
- 23. The Advisory Board Company
- 24. The Advisory Board Company
- 25. 25 Structured vs. Unstructured Data Representation of Human Experience & Knowledge ComputableAnalyticValue • Text • Video • Recorded Audio • Structured, discrete data • Face-to-Face Audio
- 27. 27 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
- 28. Measure Goal HbA1c (test at least 2 times a year) <7.0% Blood Pressure (check at each office visit) <130/80 mm Hg LDL Cholesterol (test at least every 2 years) <100 mg/dL Triglycerides (test at least every 2 years) <150 mg/dL Foot Exam (perform at least annually) normal Urine Microalbumin/Creatinine Ratio (test at least annually) <30 Dilated Eye Exam (check annually, or every 2 years if well controlled) normal Diabetes CPM: Key Indicators 28
- 29. 29 Case Study: Diabetes Management
- 30. 30 Case Study: Diabetes Management
- 31. 31 Diabetes Management Peer Comparison Chart
- 32. 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) 32
- 33. 33 Case Study: CV Discharge Meds
- 34. 34 Case Study: CV Discharge Meds
- 35. 35 The Tangible Benefits From Intermountain’s Cardiovascular Clinical Program
- 36. Case Study • Labor and Delivery - Elective Inductions – 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. 36
- 37. Elective Inductions Elective Deliveries <39 Weeks Intermountain Healthcare 0% 5% 10% 15% 20% 25% 30% 35% 1999 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2000 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2001 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2002 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2003 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2004 JanFebM arAprM ayJunJulAugSepO ctN ovD ec 2005 JanFebM arAprM ayJunJulAugSepO ctN ovD ec Month Percent<39Weeks 37 Intermountain Healthcare, Steve Barlow
- 38. Elective Inductions Estimated Variable Cost Savings From Elective Induction Protocol Intermountain Healthcare 2001-2005 $26,479 $207,772 $597,367 $380,833 $188,606 $- $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 2001 2002 2003 2004 2005 Year VariableCostSavings $- $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 $1,400,000 $1,600,000 CumulativeVariableCostSavings Yearly Savings Cumulative Savings 38
- 39. NORTHWESTERN’S EDW So far, so good… 39
- 40. Data Loaded to Date Metric Value Number of Rows 3,173,632,200 Terabytes 2.2 Truckloads 1,233 Complete works of Shakespeare 252,483
- 41. 41 Early Adopters and Value of the EDW Customer Analytic Use NUgene Relating genomic data and clinical profiles for phenotyping high risk diseases such as diabetes and asthma Neurosurgery A summary of new patients, encounters and diagnoses from the EDW is import daily into MDAnalyze, a Neurosurgery outcomes database Alan Peaceman, MD Creation of a perinatal patient registry for studying clinical quality outcomes; BMI relationships to complications Bill Grobman, MD Statistics of deliveries at NMH in preparation for a grant proposal Dana Gossett, MD Application of Systemic Inflammatory Response Syndrome (SIRS) criteria to pregnant and postpartum women with infectious complications Andrew 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 patients NMH 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.
- 42. 42 Specific Research Example 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 Rapid turnaround (<2 days) to meet a grant submission deadline…
- 43. 43 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 a Northwestern clinic office visit within the last 120 days? – 'KADISH, ALAN H.' – 'GOLDBERGER, JEFFREY J.' – 'PASSMAN, ROD S.' – 'DENES, PABLO' – 'JACOBSON, JASON‘ – Answer: 309
- 44. Changes in quality measures during the first 3 months of the study MEASURE Satisfied (%) Sept 301, 2007 Satisfied (%) Dec 31, 2007 Satisfied (%) April 30, 2008 Coronary 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 0.85 Heart 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 0.85 Hypertension control 0.76 0.75 0.76 Diabetes 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 Examples
- 45. Prevention 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 therapy 0.76 0.79 0.82 Changes in quality measures during the first 3 months of the study MEASURE Satisfied (%) Sept 301, 2007 Satisfied (%) Dec 31, 2007 Satisfied (%) April 30, 2008
- 46. -20 -10 0 10 20 30 40 50 60 70 80 90 100 % Aspirin for Primary Prevention in Diabetes Physician Performance (most recent 3 months)
- 47. -20 -10 0 10 20 30 40 50 60 70 80 90 100 % Anticoagulation for Heart Failure with Atrial Fibrillation
- 49. 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
- 50. 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
- 51. Clinical Decision Support Systems
- 52. 52 Clinical DSS Structure Point-of-Care DSS – Alerts, reminders Retrospective – What happened? Prospective – What will happen?
- 53. 53 Where Does It Appear? Organization of Data – “checklist effect” Stand-Alone Expert Systems – often require redundant data entry Data Repository: Mining CDSS Integrated into Workflow – push information to the clinician at the point of care – examples: EMR, CPOE
- 54. 54 The Revolutions in CDSS Phase 1: Quality and safety of care – What is “good care”? – Did we provide good care? – Barely entering this phase now Phase 2: Economics of care – What does good care cost? – Did we provide good care at the most effective cost? Phase 3: Genomics of care – What are the genomic influences on good care? – Did we provide personalized, tailored care?
- 55. 55 Key Architectural Elements Data capture/display/storage – EMR – central data repository Controlled, structured vocabulary Knowledge representation (e.g., Arden) Knowledge acquisition Clinical event monitor: integrate the pieces for many different uses (clinical, research, administrative)
- 56. 56 Foundation and Rationale for Decision Support Models Mathematics, mathematical models and decision making Probability and statistics (Bayesian models) Rule-based decision-making – IF the patient has symptoms A or B or C THEN – Prescribe medication X and treatment Y and schedule next visit for T weeks Data-driven models – Looks for patterns within a test set of data and then generalize
- 57. 57 Justification for CDSS: Medical Errors Estimated annual mortality: Air travel deaths 300 AIDS 16,500 Breast cancer 43,000 Highway fatalities 43,500 Preventable medical errors 44,000 - (1 jet crash/day) 98,000 Costs of Preventable Medical Errors: $29 billion/year overall 1999 Institute of Medicine (IOM) Report
- 58. 58 Definitions: What is an error? Error of execution: Failure of an action to be completed as planned Error of planning: Use of a wrong plan to achieve an aim Adverse event: An injury caused by medical management (and not the result of the patient’s condition) Preventable adverse event: An adverse event attributable to error Negligent adverse event: A preventable adverse event that satisfies criteria for malpractice
- 59. 59 Errors in Medicine Hospital admissions: 2.9% (UT/CO, 1992) - 3.7% (NY, 1984) have an adverse event Proportion of preventable adverse events: 53% (CO/UT) - 58% (NY) Extrapolate to USA (33.6M admissions in 1997): 44,000 - 98,000 deaths
- 60. 60 Errors in Medicine Types of adverse events (Harvard Medical Practice Study, 1991): – drug complications: 19% – wound infections: 14% – technical complications: 13% 50% associated with operations
- 61. 61 Clinical DSS: The Impact Examined randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. CDSS improved practitioner performance in 62 (64%) of the 97 studies JAMA. 2005;293:1223-1238.
- 62. 62 Case Studies: Examples of CDSS Effectiveness Perioperative Antibiotic Administration – intervention: reminder re timing and type of abx – period: 1988 - 1994 – result: perioperative wound infections dec 1.8% -> 0.9% – avg # doses: 19 -> 5.3 – overall antibiotic cost (constant $) per treated patient: $123 -> $52 Pestotnik SL, Classen DC, Evans RS, Burke JP. Implementing antibiotic practice guidelines through computer-assisted decision support: clinical and financial outcomes. Ann Intern Med 1996;124(10):884-90.
- 63. 63 Examples (continued): Preventable ADEs CPOE Implementation – Population: hospitalized patients over 4 years – Non-missed-dose medication error rate fell 81% – Potentially injurious errors fell 86%
- 64. 64 Examples (continued) Reminders of Redundant Test Ordering – intervention: reminder of recent lab result – result: reduction in hospital charges (13%) – Tierney WM, Miller ME, Overhage JM et al. Physician inpatient order writing on microcomputer workstations. Effects on resource utilization. JAMA 1993;269(3):379-83. Preventive Health Reminders in HIV – intervention: reminders to perform screening tests or vaccination (e.g., pap smear, HBV) – result: sig decreased time to documentation (median = 11 vs 52 days) – Safran C, Rind DM, Davis RB et al. Guidelines for management of HIV infection with computer-based patient's record. Lancet 1995;346(8971):341-6.
- 65. 65 Examples (continued) Systematic review – 68 studies – 66% of 65 studies showed benefit on physician performance • 9/15 drug dosing • 1/5 diagnostic aids • 14/19 preventive care • 19/26 other – 6/14 studies showed benefit on patient outcome Hunt DL, Haynes RB, Hanna SE et al. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998;280(15):1339-46.
- 66. 66 Other CDSS Success Stories Point-of-Care Decision Support – Bilirubin Management in neonates – Ventilator Management in ARDS – Coumadin Management – Glucose Management in the ICU – Antibiotic Assistant – Infectious Disease Monitoring
- 67. Medical Artificial Intelligence Just Another Term For Decision Support
- 68. 68 Goals of AI Study the thought processes of humans to better understand the complexity of human intelligence Create computer systems which achieve human levels of reasoning
- 69. 69 Knowledge Representation Formalisms: Their Role Express policies (institutional, national, international) in computable format Formulate interventions in medical practice Make local variations in guidelines Provide “intelligence” to a clinical expert system
- 70. 70 Forms of Knowledge Representation Bayesian/probabilistic = Decision Analysis Special Issues: Guidelines & GLIF (Guideline Interchange Format) Case-based reasoning Ontologies Decision Tables Artificial Neural Networks Bayesian Belief Networks Procedural Production rules Arden Syntax
- 71. 71 Roots of Medical AI MYCIN (late 1070s) – Shortliffe, et al, at Stanford – 1970s, infectious disease and antibiotic therapies – Rules-based PUFF (early 1980s) – Based on MYCIN – Pulmonary data interpretation
- 72. 72 Roots of Medical AI APACHE (1981) – http://www.cerner.com/public/Cerner_3.asp?id=3562 – Point of care in ICU
- 73. 73 Computers Are Good At… Computational functions - add, subtract, multiply, divide, compare – The most familiar Symbolic reasoning Pattern recognition
- 74. 74 The Arden Syntax A symbolic language for encoding medical knowledge Adopted by HL7 and ANSI in 1999 Used to develop Medical Logic Modules (MLMs) Each MLM can make a single medical decision – MLMs can be chained Can be used for variety of clinical decision support functions – E.g., alerting physicians of potential kidney failure
- 75. 75 Arden Syntax: Assessment Incorporated into several vendors’ products Growing number of installation sites Facile for simple alerts/reminders May not be sufficiently expressive for complex guidelines
- 76. 76 Support for Arden Syntax Institutions Cedars-Sinai Medical Center Software Vendors Eclipsys/Healthvision McKesson Siemens Knowledge Vendors Micromedex
- 77. 77 Arden Syntax - History HELP LDS Hospital Salt Lake City, UT CARE Regenstrief Institute Indianapolis, IN Arden Syntax 1989
- 78. 78 Arden Syntax - Rationale Arden Syntax arose from the need to make medical knowledge available for decision making at the point of care. Allow knowledge sharing within and between institutions Make medical knowledge and logic explicit Standardize the way medical knowledge is integrated into hospital information systems
- 79. 79 Pattern Recognition Objects, events or processes are described by their qualitative features, logical, and computational relationships Examples – Computer matches pattern found in a new x-ray to other cases to determine diagnosis – Searching text for context-based key words • Spam filters
- 80. 80 Wikipedia Based on either a priori knowledge or on statistical information extracted from the patterns Sensor Feature Extraction Classification Engine Training Set Real Data
- 81. 81 Other AI Methods Genetic algorithms – Selection, recombination, mutation Search algorithms Constraint-based problem solving – When conditions in variables are met, then execute Frame-based reasoning
- 82. Frame Example 82
- 83. Arden Example 83 JAMIA, Volume 19, Issue 4, 1 July 2012
- 84. 84 In Summary Enterprise Data Warehouses and Electronic Medical Records work hand- in-hand to address Clinical Decision Support Artificial Intelligence has yet to prove itself scalable beyond informatics research projects
- 85. 85 Thank You! Questions and discussion?
- 86. © 2016 Health Catalyst Proprietary and Confidential Healthcare Analytics Summit 16 Here’s a sneak preview … David F. Torchiana, M.D. President and CEO Partners HealthCare Former Chairman and CEO of the Mass, General Physicians Organization Summit highlights Jam-Packed Agenda, all focused on outcomes 67 Sessions and stories 8 keynotes, 27 breakouts, 32 Analytics Walkabouts Industry Leading Keynote Speakers We’ll hear from well-known healthcare industry champions. And by popular demand, we've invited back two of our top-rated speakers. CME Accreditation for Clinicians Last year’s HAS 15 summit was awarded 24.0 AMA PRA Category 1 Credits ™ and we expect a similar number this year. Improved "How-to" Case Study Sessions We’ve increased the breakout session times to give more time for detailed “how to” learning while also extending the Q&A time. The Analytics Walkabout Back by popular demand, we will feature 32 new projects highlighting a variety of additional clinical, financial, operational, and workflow analytics outcomes improvement successes. Analytics-Driven, Hands-on Engagement for Teams Analytics will continue to flow through the three-day summit touching every aspect of the agenda. Networking and Fun We will have longer breaks, frequent fun-run/walk opportunities, a night on the town, and some fun and games, including your favorite retro arcade games. Pre-Summit Classes and Training An early half-day of pre-session classes and training options specifically for Health Catalyst clients. Liz Wiseman President, theWiseman Group Bestselling Author, Speaker & Executive Advisor “Rookie Smarts: Why Learning Beats Knowing in the New Game of Work” Anne Milgram Former NJ Attorney General Senior Fellow at NYU School of Law, VP of Criminal Justice, Laura and John Arnold Foundation “ “Criminal Justice Analytics” Eric Siegel, Ph.D. President, Prediction Impact, Inc. Best Selling Author and Founder of Predictive Analytics World “The Power to Predict Who Will Click, Buy, Lie, or Die” Taylor Davis VP, Analysis & Strategy, KLAS Associate Professor of Statistics of Utah David Eccles School of Business Healthcare Analytics Mkt Overview Don Berwick, M.D. Former Administrator, CMS Founding CEO, Institute for Healthcare Improvement Jay Bishoff, MD Director, Intermountain Urological Institute Intermountain Healthcare Top Rated HAS 15 speaker Toby Freier, FACHE President, New Ulm Medical Center Hearts Beat Back ™ Heart of the New Ulm Project HAS 16 Documentary Salt Lake City Sept 6-8 2016 The Grand America Hotel 86