From Data Capture to Useful Information PPRNet Practice Reports Patient-Level Reports PLR

1. From Data Capture to Useful InformationPPRNet Practice Reports & Patient-Level Reports (PLR) Ruth G. Jenkins, PhD Medical University of South Carolina Charleston, South Carolina Practice Partner User Meeting July 22, 2007

2. Agenda Data Processing Details Practice Practice Partner PPRNet Practice Report Contents Interpreting Patient-Level Report (PLR) Contents Interpreting Demonstration

3. Purpose of Reports Practice Performance � Feedback Evidence-based Guidelines Self Evaluation Guide Patient Care Decisions Guide Quality Improvement Aids Practice Research Participation �Better� Data Avoid Underuse, Overuse, Misuse Avoid Redundant Testing

4. PPRNet Data Collection / Analyses

5. Practice Activities Patient Care Enter �usable� data Point of care entry Direct entry Visit Note Templates Transcription System interfaces Lab results system Lab equipment Data Extraction Program Run every quarter Transmit data to Practice Partner

6. Usable Data=Meaningful Data Data recorded with meaning: HTN not OV Not embedded in the text of a progress note Structured or semi-structured Consistent wording for diagnoses or diagnostic codes in consistent place in patient record HTN Hypertension, NOS 401.9

7. Practice Data Recording Patient Status Update Usual Provider Update Vital Signs Diagnoses / Problems / Procedures Directly on Lists Using visit note template Text Section Titles Laboratory Results Prescriptions Health Maintenance

8. Data Extracted Limited demographics Diagnoses Procedures Medications Laboratory tests Vital signs Health maintenance

9. Data Not Extracted Identifiers (other than birth-date, gender, race, zip code, internal tracking number) Text of notes

10. Practice Partner Activities Receive quarterly data from practices Check data for errors Transmit data to PPRNet office Maintain extract program Technical support for extraction Indoctrinate new PPRNet practices

11. PPRNet Data Activities Receive data from Practice Partner Load raw data into database �Clean� data Strip extraneous text .OV HTN **DM Aggregate with previous data Remove duplicate data records

12. PPRNet Data Activities (continued) �Bridge� Data to Common Nomenclature

13. PPRNet Data Activities (continued) Determine patients eligible for specific guidelines e.g., identify patients with Diabetes Mellitus Link the different types of data for each patient to ascertain if the patient has met the guideline measure Calculate overall percent adherence for the practice to the guideline Create statistical process control charts showing this adherence over time

14. PPRNet Data Activities (continued) Data Processing Tools include: SAS � Statistical Analysis System Data loading and processing Calculation of adherence measures Creation of SPC charts Microsoft Access, Excel Clinical bridging Generate Practice Reports and PLRs Adobe Acrobat On-line Practice Report

15. Clinical Practice Guideline Measures Diabetes Mellitus (11) Cardiovascular Disease (16) Women�s Health Care (4) Immunizations (8) Respiratory Disease (2) Mental Health / Substance Abuse (5) Nutrition / Obesity (2) Inappropriate Rx prescribing in the elderly (2)

16. Types of Measures Process: tests done medication prescribed Outcome: clinical target reached

17. Diabetes GuidelinesProcess Measures

18. Diabetes GuidelinesOutcome Measures

19. Practice Performance Report 50 Indicators 3 Summary Measures SPC Methodology Time trends Comparison with PPRNet benchmark (ABC) Comparison with national benchmarks (where available)

22. Patient-Level Report (PLR) Generated each quarter End of last month of the quarter Excel Spreadsheet: 1 patient per row Same guideline criteria as practice report All �active� patients = 18 yo Children: Asthma controller: Age 5 + Chlamydia screening: ? Age 16-25 Tetanus vaccine: Age 12 + Influenza vaccine: Age 6 mon - 5 yr Meningococcal vaccine: Age 11-19

23. PLR Examine individual patient data Deficient in a certain guideline Identify patients for improvement Retrieve PLR from ATRIP folder on PPRNet web page Password is assigned when you join

26. PLR Data 69 fields (columns) Practice/Patient demographics � 7 Practice ID, Patient ID, DOB, Sex, Age, Race, Provider Diagnoses � 18 HTN, DM, CHD, Hyperlipidemia, CHF, AFIB, Atherosclerosis, CVD, PVD, Hysterectomy, Asthma, COPD, Renal Disease, Liver Disease, Alcohol Abuse, Obesity, Tobacco Abuse, Depression

27. PLR Data Blood pressure � 4 Last BP date and value 3 or more BP>140/90? Y/N Laboratory Tests � 14 Date and value for last Cholesterol, LDL, HDL, Triglycerides, HgbA1c, Microalbumin, Glucose

28. PLR Data Prescriptions � 9 Last date of anti-coagulant, ACE inhibitor, anti-platelet, lipid-lowering, beta blocker, spironolactone, inappropriate in elderly, rarely appropriate in elderly, asthma controller Women�s Health � 4 Date of last pap smear, mammogram, bone density measure, chlamydia screening

29. PLR Data Immunizations � 6 Date of last vaccine for tetanus, flu, pneumonia, Hepatitis A, Meningitis Mental Health, Substance Abuse, Diet � 5 Screening for Alcohol Abuse, Depression Counseling for Alcohol, Tobacco, Diet SQUID � 2 Number of eligible measures Proportion up-to-date or under control.

30. 50 indicators => 37 measures Example 30 year old ?; no chronic disease eligible for 7 processes, 0 outcomes BP monitoring ? PAP Smear ? Total Cholesterol HDL Depression Screening Td vaccine ? Alcohol Screening SQUID = 3 / 7 = 0.429 SQUID=Summary Quality Index

31. Identify pts with multiple problem areas May help in prioritizing outreach Practice reports that average SQUIDs across all pts give a sense of overall progress over time. Using the SQUID

32. Using the PLR Excel�s data filter Click the arrow in columns to filter. Arrow turns blue. Make your selection or choose �Custom� Pre-Programmed Macros

33. Data Filter Example Women Filter on Sex = F Ages 18 � 40 Custom filter on Age Greater than or = 18 and Less than or = 40 No Pap Smear in 2 years Custom filter on Pap Date Less than or = 7/22/05 or equals (blank)

35. Pre-programmed Macros To enable macros, set security to Medium Click the box Filter further to narrow set Macro Example � No Influenza Vaccine in past year for: Pts 6 months - 5 yrs old or Pts >= 50 yrs old or Pts 18 - 49 yrs old with DM, Asthma, COPD, CHD, CHF, Renal Dis, Alcohol Abuse

37. Practice Partner�s DBGui Identify patients in your Patient Records Links PPRNet ID with Patient Records ID Copy DBGui.exe and PPUtility.dll from PPRNet web page to PPART directory. Click on DBGui.exe � window opens Enter PPRNet ID in Internal ID field.

39. Practice Improvement Study Practice Report Select measures to target for improvement Follow improvement over time Use PLR to identify individual patients Implement Quality Improvement Cycle

40. Conclusions Usable data are essential Data management is a process Follow clinical processes and outcomes longitudinally over time Data Information Interpret the information Guide Improvement Support Decisions Evaluate Research Projects

41. Demonstrations