1 / 41

Contact: Skip Valusek MHQP Education Chair skipvalusek@comcast

Welcome to the MHQP & HealthForce MN Quality Brownbag Room Monthly Noon Brownbag Fourth Thursday Every Month. Aug 28 2008. DATA Requirements Governance Evaluating reporting aspects of software applications Organizing for the reporting function

blythe
Download Presentation

Contact: Skip Valusek MHQP Education Chair skipvalusek@comcast

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Welcome to the MHQP & HealthForce MN Quality Brownbag Room Monthly Noon Brownbag Fourth Thursday Every Month Aug 28 2008 • DATA • Requirements • Governance • Evaluating reporting aspects of software applications • Organizing for the reporting function • Qualitative and Quantitative data; • Information for Committee Meetings • Data capture alternatives • MEASUREMENT • Data displays • SPC representations ; • baselines; benchmarking • Basic statistics • Next-level statistics: regression; correlation • Organizational vs project measurement Contact: Skip Valusek MHQP Education Chair skipvalusek@comcast.net Slides are posted at: http://www.healthforceminnesota.org/pages/Programs/courses.html

  2. Register your Attendance Hopefully you provided your name & organization when you signed in. • If so: Just say Hi in the Chat Pod and we’ll capture your name and organization in the log. • If not: identify yourself and organization in the Chat Pod to the left of your screen. If there are more than one attending on your sign-in, tell us how many by saying “Hi (tell us the number of attendees) “

  3. Poll: Who is Attending this Session ? • Rural / Outstate ? • Metropolitan area ? • Organization that has (or serves) both ?

  4. Healthcare system Hospital Clinic or Clinic System Long term care Healthplan Homecare / Hospice A Quality Support Organization Other ? (Identify other in Chat Pod) Poll: Who is attending: Organization Type ?

  5. Poll: What do you hope to gain by participating? • I am a CPHQ and want to obtain CEU’s for recertification. (Note: this is not guaranteed at this time. We are still working on this) • I am a healthcare quality professional and am interested in additional education. • I am a healthcare professional interested in developing quality skills as a core competency. • I am a healthcare professional interested in learning more about healthcare quality.

  6. Agenda • DATA • Requirements • Governance • Evaluating reporting aspects of software applications • Organizing for the reporting function • Qualitative and Quantitative data; • Information for Committee Meetings • Data capture alternatives • MEASUREMENT • Data displays • SPC representations ; • baselines; benchmarking • Basic statistics • Next-level statistics: regression; correlation • Organizational vs project measurement

  7. Performance Assessment: Measuring Results • Quality Improvement • Early 1990’s TQM/QI • Collaborative culture • Focusing on processes • How customers define quality • Focusing on reducing variation • Shift from focus on individuals to focus on systems & processes 115-117 60

  8. Requirements: Planning the Change(is an iterative process) Aims Measures Interventions

  9. Seven Basic ConceptsData & Measurement Requirements • Healthcare data must be carefully defined & systematically collected & analyzed • Tremendous amounts of healthcare data & information available, but not all useful • Mature QI information revolves around clearly established patterns of care, not individual cases • Most quality indicators currently available are useful only as indicators of potential problems, not definitive measures of quality • Multiple measures of quality need to be integrated to provide a clear picture of quality of care • Developing outcomes information without monitoring the process of care is inefficient because it can’t lead directly to quality improvements • Cost and quality are inseparable 30-31

  10. Data Governance • Planning and organizing • Anticipate barriers, identify responsibilities, lay groundwork for multidisciplinary collaboration • Develop data dictionary defining data elements & calculations; data stewards • Verifying and correcting • Identify data limitations; provide opportunity for correcting data & identifying opportunities for improving internal systems & review data 31

  11. Poll: Who has identified data stewards for their critical data elements ? • What’s a data steward and where can I get one ? • We’ve talked about it a lot but have never been able to take action. • We have a few stewards who are responsible for the quality of their element(s) • We not only have stewards for many of our elements . . . . We also have a comprehensive data dictionary “owned” by the stewards.

  12. Evaluating reporting aspects of software applications • Real Time “Reports” • Alerts (patient safety) & Reminders (best practices) • Efficiency of capture • Operational Reports (e.g. Patient lists) • Minimal impact on real time response time • Retrospective / Analytical “Reports” • Extract/Transform/Load Process • Data warehouse integration of: • clinical, • financial, • operational • Value analysis = Quality / cost

  13. Organizing for the reporting function • Centralized • Minimize duplication • Best expertise against right data source(s) • Facilitates the reduction of island data bases • Distributed • Direct and “immediate” response • Often depends on personal relationships • Harder to push organization toward analytic maturity. • Often get different answers depending on who you ask

  14. Quantitative Data • Two types of data • Continuous: Measurement (e.g. bedside monitors; arrival time; ) • Discrete: Counts & categories • Sampling, data collection & analysis is different for each type 39

  15. Surgical Patient Preoperative Postoperative Gender Male Female Patient Education Received education brochure Didn’t receive brochure Discrete: Nominal Data

  16. Nursing staff rank Nurse Level 1 Nurse Level II Nurse Level III Associate Degree Education BS MS PhD Attitude toward research scale Agree Neutral Disagree Discrete: Ordinal Data

  17. Continuous Data • Scales theoretically have no gaps • Interval- • distance between each point is equal (e.g thermometer) • Ratio- • distance between each point is equal and there is a true zero (e.g. money) • Continuous data could be converted to count/categorical data (e.g. create histograms) • The critical issue is whether the right data are measured or counted • Much QI data is “surrogate measures” because they are easy to retrieve 40

  18. Qualitative/ Subjective Data • e.g. • Patient Safety stories • Patient complaints and praises • Employee comments • Content analysis of comments • Difficult but powerful pattern recognition

  19. Data Capture Alternatives • Data Collection Plan • Determine who, what, when, where, how, why ? • Choose & develop sampling method • Stratified Random Sampling-Divide population into stratas (subpopulations-sex, ethnicity, disease); each member of strata has equal probability of being selected • Cluster Sampling-Divide population into groups or clusters (studying medical students get list of medical schools) derive random sample • Who ? (Frequently overlooked until too late) • How? • Prospective capture (interview at time of care). • Manual abstraction • Structured report from EHR ? • Survey (web e.g. Survey Monkey; scan-able form) • Where? • Island excel databases (archipelago ?) • Duplication of (and errors in) data, especially ADT data ?

  20. Poll: Who designs your surveys ? • Anyone that wants to. Sometimes quality is consulted for support in design and implementation. • Quality is responsible for all survey designs. • We have a “survey central” that is responsible (identify organizational entity in the chat pod). • Other (identify in the chat pod).

  21. 100% or Sample ?? • Population (N) total aggregate or group (all cases meeting a designated set of criteria) • Sample (n) a portion of the population representing the entire population • Sampling • Provides a logical way of making statements about a larger group based on a smaller group . . . Saves resources ! • Allows researchers to make statements or generalize from the sample to the population if the selection was random & systematic (unbiased) • This is one place you defer to your quality expert ! 41

  22. Basic Sampling Designs: Types of Sampling • Probability-every element in the population has equal/random chance of being selected • Simple Random Sampling-each individual in sampling frame (all subjects in population) has an equal chance of being chosen • Systematic Sampling-After randomly selecting first case, draw every nth element from a population • Non probability sampling-no way of estimating probability of every element being included 41

  23. Basic Sampling Designs: Types of Sampling • Convenience Sampling-Use of any available group of subjects; lack of randomization; atypical subjects • Snowball Sampling-Subtype of convenience sampling; subjects suggesting other subjects • Purposive/Judgment Sampling-Select particular group based on criteria; subjective; researcher uses own judgment to identify groups • Expert Sampling-Type of purposive sampling involving selecting experts in a given area due to access to relevant information • Quota Sampling-Make judgment decision about best type of sample; prespecifies characteristics of sample to increase representativeness 41-42

  24. Sample Size • Factors influencing sample size: research purpose, design, level of confidence desired, anticipated degree of difference between study groups, population size • Larger sample, more valid & accurate the study; more representative of population • Smaller sample error of the mean-measure of fluctuation from one sample to another from same population 42

  25. Measurements for Committee Meetings • Scorecards • Stoplight (ideally colors over time) • Detail via “drilldown” trends • Drill down interactively • By unit and shift • By day of week • By physician

  26. Measurement: Data Displays • Identifying & Presenting Findings • How do data compare with other organizations? • What is the trend over time? • How are data likely to be interpreted? • Is there an opportunity for improvement? • Who should receive the data? • For what purpose? 31

  27. Statistical Process Controls (SPC) representations • Use when you have valid statistical data and enough of it to calculate standard deviations (and assumes a normal distribution of your data). • Good for use with committees and with trends to detect when you need to do something • More in analysis & communication session in September

  28. Baselines & Benchmarking • Baseline: Collecting that starting comparative • Often overlooked until opportunity is lost • Include in your data plan and do it early • Benchmarking: How do we know we’re the best ? • Compare to yourself: Only if everyone knows you’re the best; otherwise • Except for core measures, be prepared to pay for someone else to compile the data and sell it to you.

  29. Basic Statistics • Mean; Median; Mode; 2 & 3 sigma SPC’s • Most data not sophisticated or accurate enough to provide much more. • Trends are the key. • Where possible look for shifting distributions (visual detection followed someday by statistical verification) 33-34

  30. Planning the Change(is an iterative process) Aims Measures Interventions

  31. Statistical Power of Different Data Types • Categorical data least powerful statistically (hypertensive/ non-hypertensive) • Continuous data have most power; need fewer data points (measured systolic & diastolic levels) 40

  32. Next level statistics • Correlations & Regression analysis to “prove” relationship of variables. • Next month’s topic of analysis

  33. Other Data considerations • Confidentiality • Delineation of specific information to which individuals have access • Protect records against loss, defacement, tampering, and unauthorized use • Risk Adjustment • Take into account/control the fact that different patients with same diagnosis might have additional characteristics that could affect response to treatment • Analysis of outcomes data takes into account & controls characteristics or conditions clinically meaningful & have demonstrated statistical effect on rates 19

  34. Both raw & risk-adjusted data can be available for outcomes Handling outliers (are patients > 2 standard deviations deleted ?) Level of detail Best system includes every patient, practitioner, & payer Risk Adjustment 27

  35. Organizational vs Project Reporting • Project data is much more suited to aims-measures-interventions and PDSA. Sometimes the project warrants status updates in organizational scorecards. • Both organizational and project reporting often intersect at Service Line quality improvement • Common issue to both: When/how does the data tell you to STOP ?

  36. FYIUsing Data For Improvement: The Toolkit A Two Set DVD by Sandra Murray Available through NAHQ

  37. Reminder to Register your Attendance Hopefully you signed in with your name & organization. • If so: say Hi in the Chat Pod and we’ll capture you in the log. • If not: identify yourself and organization in the Chat Pod to the left of your screen. If there are more than one attending on your sign-in, tell us how many by saying “Hi (tell us the number of attendees) “

  38. Interest in CPHQ Prep Class in March 2009 ? • Registration would be around $150 for 1.5 days (Saturday and half day Sunday). • Would like to take this prep course in March somewhere in the Twin Cities? • Any interest in pairing you with a Twin Cities buddy to save on room expenses? Contact Skip

  39. Thank you for joining us in this informal quality forum !!!

  40. Next Session September 25 Information Management : Analysis & Communication • ANALYSIS • comparative data • Interpreting benchmarking data • Interpreting incidence/event reports • Interpreting outcome data • Intuition/stories vs objective/facts • COMMUNICATION • Event/individual patient issues • PI feedback processes • Reports - Scorecards - OLAP - mining • Right information for right audiences • Accrediting; boards; leaders; Dir & Mgr; Staff

  41. Welcome to the MHQP & HealthForce MN Quality Brownbag Room Monthly Noon Brownbag Fourth Thursday Every Month Sep 25 Information Management: Analysis & Communication • ANALYSIS • comparative data • Interpreting benchmarking data • Interpreting incidence/event reports • Interpreting outcome data • Intuition/stories vs objective/facts • COMMUNICATION • Event/individual patient issues • PI feedback processes • Reports - Scorecards - OLAP -s mining • Right information for right audiences • Accrediting; boards; leaders; Dir & Mgr Questions? Contact: Skip Valusek MHQP Education Chair skipvalusek@comcast.net Slides are posted at: http://www.healthforceminnesota.org/pages/Programs/courses.html

More Related