1. © 2011 Jones and Bartlett Publishers, LLC

2. Why is it important to quality? Donald E. Lighter, MD, MBA, FAAP Professor, University of Tennessee College of Business Medical Informatics

3. © 2011 Jones and Bartlett Publishers, LLC What is medical informatics and why is it important? –Deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of data in health and biomedicine. –Encompasses hardware and software, processes and programs like clinical guidelines, formal medical terminology and computer syntax, and information management and reporting systems. –Science of medical informatics has been evolving since the 1950s, when physicians and computer scientists at the Massachusetts General Hospital developed a computing language for medical applications called MUMPS (Massachusetts General Hospital Utility Multi-Programming System). Medical Informatics is a a Health Care Discipline

4. © 2011 Jones and Bartlett Publishers, LLC Fundamental axiom: You can’t manage what you don’t measure. Fundamental approach: –What is the gap between current process performance and the ideal expected performance? –What parts of the process can be targeted for improvement? –How does a team determine what parts of the process to improve? –How does a team determine when the process is improving as expected? Improvement teams often include a medical informaticist as a core member, helping to find the appropriate data sources and develop process and outcome measures Medical Informatics is a a Health Care Discipline

5. © 2011 Jones and Bartlett Publishers, LLC Avedis Donabedian model – characterized medical quality in three domains: –Structure – the necessary infrastructure needed to deliver medical care, which includes such elements as the medical equipment, staff, facilities, and information systems, as well as leadership and human capital resources that create a culture to sustain improvement. –Process – the procedures and steps required to deliver health care services to customers, including both medical and support services. –Outcome – the net effect of the delivery of services to customers, including restoration of function, recovery from interventions, complications, and survival. Implications of Donabedian model –Organizational structure supports processes to produce outcomes –Measurement systems must account for each of these domains and the parameters that exist within them. –Accreditation and certification organizations have developed standards and measures for all three of these domains Types of Measurement Systems

6. © 2011 Jones and Bartlett Publishers, LLC Performance measures are increasingly used for quality improvement in programs such as: –Pay-for-performance (P4P) or pay-for- reporting (P4R) programs –Purchaser and/or consumer decision- making –Accreditation and external quality oversight Operational definitions (detailed instructions for the collection, analysis, and reporting of data) are key to ensuring appropriate use of data to meet the criteria above Performance measures must have three characteristics to be effective: –Important – relevant to stakeholders, to the health care system being measured, and to any third parties that may use the measures for delivery or improvement of health care. –Scientifically sound – based on current evidence of quality and efficacy, including cost effectiveness; numerator and denominator must be based on valid and reproducible data. –Feasibility – data must be available for collection, analysis, and reporting; measures must be associated with processes that can be modified through reasonable methods and procedures. Because of these rigorous criteria, some practitioners argue that medical care is too complex to be measured Use of Performance Measures

7. © 2011 Jones and Bartlett Publishers, LLC “Profile measures” = descriptive of an organization’s facilities, staff, and culture, for example: –Number of beds in a hospital –Number of physicians or practitioners in a medical practice –Presence of a Positron Emission Technology (PET) scanner in an imaging center –Gamma knife equipment in a surgical center –Multi-disciplinary teams for care –Capability to perform patient education in self-care management –Disease registries –Electronic health records and decision support systems Structure measures not a guarantee of quality (necessary, but not sufficient), but some practitioners and consumers may equate availability of certain equipment (e.g. MRI) as a sign of quality. Structure Measures

8. © 2011 Jones and Bartlett Publishers, LLC Manpower – medical staff, other health professionals, support personnel –Nurse staffing ratios –Nurse specialty certification –Environmental services staffing rations –Nutritional services –Admin staff ratios –Certified therapist ratios Materials – supply chain elements; often cost, as well as quality issues –Turnaround time for surgical procedure trays –Inventory turns –Delivery times –Breakage rates – e.g. needle breakage during suturing Milieu - environment of care; entire domain in TJC standards –Safety –Security –Hazardous materials –Emergency management –Fire prevention –Medical equipment –Policies and procedures –“Hospitality services” Categories of Structure Indicators

9. © 2011 Jones and Bartlett Publishers, LLC Financial - track cost of care, as well as potential revenue if appropriate, for each step –Cost of materials – the cost of any materials or supplies used in the step –Staff costs – the allocated cost of staff time and benefits for employees involved in the process step –Equipment amortization – the fractional cost of equipment used in a process step, based on the equipment’s depreciation schedule –Building and utilities costs – the fractional cost incurred because of use of the building and utilities to provide the service –Administrative costs – any costs for administrative services that might be related to the process step Utilization – used to determine if the step is being followed or being avoided (practicality, lack of knowledge, failure to understand process flow) –Example – HEDIS measures that evaluate clinical process steps like use of beta blockers after myocardial infarction –Example – efficiency metrics (blood draws per hour, patients triaged in the ED per shift, days from discharge to final bill) Process Measure Categories

10. © 2011 Jones and Bartlett Publishers, LLC Compliance – adherence to oversight organization rules, regulations, and standards –Disposal of medical waste –Billing practices following collection laws –Joint Commission standards –Often binary, i.e. “meets” or “does not meet” Disease-specific – evaluation of performance in managing specific diseases –Examples – CMS Physician Quality Reporting Initiative (PQRI) measures, National Quality Forum measures, HEDSI measures –Efforts to direct improvement efforts that are disease specific Satisfaction with care – customer satisfaction with care; highly important to all health care organziations –Usually conducted by third part (Press Ganey, Gallup, etc.) –Standardized questions to allow benchmarking between organizations –Standardized surveys created by Agency for Healthcare Research and Quality include CAHPS, HCAHPS Process Measure Categories

11. © 2011 Jones and Bartlett Publishers, LLC Business outcome measures – determine the business effectiveness of process; usually not health care specific, for example: –Collection rates for the accounts receivable (AR) collection process –Return on investment to gauge management effectiveness –Customer satisfaction for the ombudsman function at a hospital –Insurance billing returns to measure the effectiveness of the billing system –Room inspection passage rate for the environmental services processes –Customer satisfaction with food service for the nutritional services department Outcome Measures - Business

12. © 2011 Jones and Bartlett Publishers, LLC Specific to the clinical condition being measured Metrics may be risk adjusted to account for comorbidities, i.e. coexisting diseases and other clinical problems that may affect the measure. –Example: obstetric complications often vary with age, with lower and higher ages having greater complication rates; the complication rate measure may be stratified by age to better understand underlying opportunities for improvement Example clinical outcome measures: –Mortality from chronic renal disease –Fracture healing rates in individuals treated for osteoporosis –Rates of hearing loss in children after an episode of otitis media (ear infection) Outcome Measures - Clinical

13. © 2011 Jones and Bartlett Publishers, LLC Validation = testing in real world environments, to ensure that the metric correctly assesses what it is intended to measure Validation criteria: –Appropriateness of the measure – does the measure provide information about the clinical entity that is being assessed? –Reliability – is the measure reproducible and internally consistent? –Validity – does the metric measure what it is supposed to measure? –Responsiveness – is the measure sensitive to changes that are important to patients/customers? –Precision – does the measure correctly differentiate between different levels of performance or responses? –Interpretability – are the results of the measure easy to understand and use for determining improvement opportunities? –Acceptability – are measurement efforts satisfactory to those being measured and those doing the measurement? –Feasibility – are data for the measure easy to collect and analyze? Clinical Outcome Measures Validation

14. © 2011 Jones and Bartlett Publishers, LLC Medical billing transaction data –ICD and CPT coded data (soon to be ICD-10 –Coded data not as robust a resource, but metrics have been developed around these coded information sources Medical record systems – Medical records provide the source data for medical transactions –Extraction of clinical data from paper records is labor intensive, expensive, fraught with error –Electronic systems best source, since data are in a form that can be analyzed electronically –EMR not standardized, so analysis is still hampered, but methods of standardization are being developed: Templates ICD-10 coding from EMR entries SNOMED coding for clinical analysis –EMRs that record data concurrently with care event are most likely to provide the most accurate data Data Sources for Performance Measurement

15. © 2011 Jones and Bartlett Publishers, LLC Mid-2008 survey of 2,758 physicians (published in the New England Journal of Medicine 1) : Only 4% of providers have a fully functional EHR system 13% have a basic system 16% said their practice had purchased an EHR but had not employed it yet 26% said their practice was planning on purchasing a digital recordkeeping system within the next two years. American Recovery and Reinvestment Act (ARRA) allocated $20B to incentivize physicians to adopt EMR by 2014 “Meaningful use” criteria established for payment of incentives to physicians and other providers, e.g. Computerized Provider Order Entry (CPOE), e-prescribing, care management, integration of lab, imaging, other clinical services. 1 DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, Kaushal R, Levy DE, Rosenbaum S, Shields AE, and Blumenthal D, Electronic Health Records in Ambulatory Care — A National Survey of Physicians, New England Journal of Medicine, 339:1, pp 50-60, 3 July 2008. EMR – How Do We Get There?

16. © 2011 Jones and Bartlett Publishers, LLC EHRs must be subject to standards created by certification organizations, e.g. CCHIT Clinical vocabularies, i.e. descriptions of clinical conditions in a uniform way using the same group of words for a specific condition. Interoperability, which allows one system to share data and messages with another EHR ontologies, which consist of the content and structure of data entries in relationship to each other. EHR user interfaces must be conducive to fitting with varying clinician workflow to facilitate data collection at the point of care. EMR – How Do We Get There? 1 DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, Kaushal R, Levy DE, Rosenbaum S, Shields AE, and Blumenthal D, Electronic Health Records in Ambulatory Care — A National Survey of Physicians, New England Journal of Medicine, 339:1, pp 50-60, 3 July 2008.

17. © 2011 Jones and Bartlett Publishers, LLC Access to multiple data sources has become a major challenge –Claims –Clinical –Encounters (clinical visit information for capitated providers) –Demographic data –Personal health records Data warehouse (repository); –Relational database designed for query and analysis, rather than transaction processing –Nearly all data are historic, not real time, refreshed regularly (e.g. on a daily or weekly basis) –Integration of multiple data sources into a single source Example: insurer data repository might contain claims data, encounter data, member satisfaction data, provider satisfaction data, complaint data, etc.; all these disparate data sources are combined into one single resource Example: clinical data repository might contain lab, radiology, pharmacy, clinical, financial, and patient complaint data Challenges to Using Clinical Data – The Evolution of the Data Repository

18. © 2011 Jones and Bartlett Publishers, LLC –Advantages of data warehouse: Takes load off transaction database, thus avoiding decrement in performance of the system used in real time Speeds access to historical data for analysis projects Integrates multiple data sources into a single data resource to facilitate analyses across disciplines, e.g. financial analysis of clinical patterns of care User friendly interface for straightforward queries and reports Structured data set that lends itself to data mining and other exploratory techniques –Disadvantages Cost, staffing requirements Data validation requirements Lack of standardization Complexity of translation of source data to data warehouse Challenges to Using Clinical Data – The Evolution of the Data Repository

19. © 2011 Jones and Bartlett Publishers, LLC Newer approaches –Data warehousing systems in the “cloud”, i.e. Internet based systems using Application Service Provider (ASP) technology, also called Software as a Service (SaaS) –Examples: Microsoft HealthVault – structured data source which will serve as a “backbone” for data exchange, with flexible front end and back end interfaces (www.healthvault.com)www.healthvault.com Google Health (http://www.google.com/intl/en- US/health/tour/index.html) – personal health records via Google servicehttp://www.google.com/intl/en- US/health/tour/index.html Newer Approaches to Data Warehousing

20. © 2011 Jones and Bartlett Publishers, LLC Statistical analysis – multitude of approaches depending on question and data structure (e.g. ANOVA, regression, capability analysis, time series, etc.) –Freestanding packages, e.g. SAS, SPSS, Minitab) –Spreadsheets, e.g. Excel, with add ins like QI Macros Predictive modeling – stratifying populations into categories for specific population based interventions designed to improve health or decrease the rate of deterioration –Example: identifying strata of diabetics based on severity of the condition and comorbidities using insurance claims data; used to predict potential resource utilization for payers and for targeted interventions for managing care Analysis of Data - Approaches

21. © 2011 Jones and Bartlett Publishers, LLC Data mining – identification of patterns within data sets that help with tasks like classification of clinical conditions, profiling, infection control surveillance, fraud detection, and scientific research –Example: use of large clinical data sets to detect patterns of care that are more conducive to favorable outcomes. –May yield false-positives –Frequently used in business sector to evaluate large volumes of data for market research –Two techniques: knowledge discovery and prediction –Artificial intelligence procedures = rules engines, knowledge bases, neural networks to perform the analysis. Analysis of Data - Approaches

22. © 2011 Jones and Bartlett Publishers, LLC Quality management requires valid, accurate data to support initiatives to improve Data collection has traditionally been problematic, but move toward electronic medical record should help Statistical analyses including predictive modeling and data mining are helping to create information from data and direct improvement efforts more precisely Medical informatics promises to provide the framework for collection and use of data to increase knowledge Summary

23. © 2011 Jones and Bartlett Publishers, LLC AHRQ developed survey of consumer satisfaction with health plan performance Public domain survey, improvements supervised by AHRQ Two components –Clinical aspects of care –Consumer experience with health services Principles for development –Emphasis on actual experience –Standardization –Use of the best science –Meaningful information –Input from all affected parties –Public resource Example: CAHPS

24. © 2011 Jones and Bartlett Publishers, LLC Current emphasis: –Create evidence based and tested surveys that met the needs of other components of the health care system, such as hospitals, nursing homes, and dialysis centers. –Explore ways to improve the utility and suitability of CAHPS instruments for vulnerable health care populations –Study ways to use CAHPS results to evaluate and improve quality of care. Example: CAHPS

25. © 2011 Jones and Bartlett Publishers, LLC CAHPS III effort started in June 2007 to support use of CAHPS for performance improvement and consumer choice. Surveys available for both ambulatory and institutional settings, includes: –Questionnaires –Administration protocols –Analysis programs –Guidance in reporting results CAHPS

26. © 2011 Jones and Bartlett Publishers, LLC Currently administered to over 120 million health plan beneficiaries. National Committee for Quality Assurance (NCQA) includes CAHPS results in health plan performance reports and as part of the accreditation process for health plans CMS uses a version of the survey to poll Medicare beneficiaries in both traditional and managed care plans and reports the scores publicly (http://www.cms.hhs.gov/CAHPS/CAHPS/).http://www.cms.hhs.gov/CAHPS/CAHPS/ CAHPS

27. © 2011 Jones and Bartlett Publishers, LLC Other CAHPS survey types –Physician offices – example topics covered –Managed behavioral healthcare organizations –Dental plans –Tribal clinics. –Subpopulation modifications of surveys, e.g. Children and Youth with Special Health Care Needs (CYSHCN) add on for CAHPS health plan survey –CAHPS Hospital Survey, (H-CAHPS or Hospital CAHPS) –CAHPS In-Center Hemodialysis Survey for dialysis facilities and End Stage Renal Disease (ESRD) networks AHRQ has developed the National CAHPS Benchmarking Database (CAHPS Database), which currently contains 10 years of data from commercial and Medicaid plans –The CAHPS Database has become the national repository for data from the CAHPS Health Plan Survey –H-CAHPS data were added to the database, in 2005 More information: CAHPS®: Assessing Health Care Quality From the Patient's Perspective, available at https://www.cahps.ahrq.gov/content/cahpsOverview/CAHPS-ProgramBrief.htm, October, 2008. https://www.cahps.ahrq.gov/content/cahpsOverview/CAHPS-ProgramBrief.htm CAHPS Surveys For All Settings

28. © 2011 Jones and Bartlett Publishers, LLC AHRQ supports surveys with technical support line, email availability, and website (www.cahps.ahrq.gov)www.cahps.ahrq.gov –CAHPS products and research findings –Information about managing survey projects and reporting survey results –Improvement projects to use results to improve the patient's experience –Product-specific CAHPS Survey and Reporting Kits – questionnaires, reporting composites, administration protocols, SAS analysis programs, and instructions for using the programs. –Information about new CAHPS products, webcasts, and user meetings –Public reporting of survey results –Searchable bibliography and frequently asked questions –Networking information with brief profiles of CAHPS Health Plan Survey projects around the country, a link to AHRQ's Report Card Compendium, and links to related organizations, programs, and initiatives. CAHPS Surveys For All Settings

29. © 2011 Jones and Bartlett Publishers, LLC A digression Activity Based Costing

30. © 2011 Jones and Bartlett Publishers, LLC “Identifies activities in an organization and assigns the cost of each activity resource to all products and services according to the actual consumption by each: it assigns more indirect costs (overhead) into direct costs” – Wikipedia (italics added) Cost/activity = Q L *P L + C M + C E + C S –Q L = quantity of labor –P L = price of labor –C M = materials cost –C E = amortized cost of equipment –C S = cost of any subcontracted services Activity Based Costing

31. © 2011 Jones and Bartlett Publishers, LLC Assigning costs to services has become important to ensure payments are appropriately covering costs; if not, and pricing is inelastic, the service may be dropped Effective service line management requires precision in understanding margins and ensuring profitability ABC

32. © 2011 Jones and Bartlett Publishers, LLC A digression Microsystems in Health Care

33. © 2011 Jones and Bartlett Publishers, LLC Microsystem –“A microsystem in health care delivery can be defined as a small group of people who work together on a regular basis to provide care to discrete subpopulations including the patients. It has clinical and business aims, linked processes, shared information environment and produces performance outcomes. They evolve over time and are (often) embedded in larger organizations. As a type of complex adaptive system, they must: (1) do the work, (2) meet staff needs, (3) maintain themselves as a clinical unit.” 1 Useful in data analysis –Individual work units can be evaluated for performance gaps –Physician performance often used as a surrogate for microsystem performance Useful for evaluating and improving quality of care in complex organizations –Divide the larger organization into smaller components, the clinical microsystems, which then can be analyzed using measures that have uniform operational definitions consistent across the enterprise, and also with outside benchmarks. 1. Clinical Microsystems, available at http://dms.dartmouth.edu/cms/about/background/http://dms.dartmouth.edu/cms/about/background/ Microsystems in Health Care