1 / 12

Time to Reap: Improving Quality by Harvesting Data from the EMR

Time to Reap: Improving Quality by Harvesting Data from the EMR. Marie J. Eidem, BS Aaron A. Kurtzhals, BS James M. Naessens, MPH. Mayo Clinic – Rochester, MN. Mayo Integrated Clinical Systems (MICS). Measuring Quality. External JCAHO CMS Leapfrog National Quality Forum

thane-bruce
Download Presentation

Time to Reap: Improving Quality by Harvesting Data from the EMR

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. Time to Reap: Improving Quality by Harvesting Data from the EMR Marie J. Eidem, BS Aaron A. Kurtzhals, BS James M. Naessens, MPH

  2. Mayo Clinic – Rochester, MN

  3. Mayo Integrated Clinical Systems (MICS)

  4. Measuring Quality • External • JCAHO • CMS • Leapfrog • National Quality Forum • Blue Cross Blue Shield • Minnesota Community Measurement • Institute for Healthcare Improvement 100K Lives . . . • Internal • Continuous Improvement teams • Mayo Board of Governors • Clinical Practice Quality Oversight Committee • Departmental Quality committees . . .

  5. Study Question Can a computer algorithm match the accuracy of manual chart review for determining whether Pneumococcal vaccination was given to hospitalized pneumonia patients (as reported for ORYX Core Measures)?

  6. Methods • Population: • Inpatients from July 2004-March 2005 • Discharge diagnosis of community acquired pneumonia • Age>=65 • N=224 • Compare measure manually abstracted (RN) vs. result electronically calculated (EMR) • Second nurse reviewed sample of patients with discrepancies to determine “true” (T) immunization status

  7. First Step: Reliable Algorithm • The very first attempt was not correct • Previously collected data, the EMR’s audit trail, and the data generated by the algorithm helped to find errors • Developing the algorithm was an iterative process

  8. Pneumococcal Vaccination Measure for Pneumonia Truth Truth + - + - EMR + + RN - - PPV=151/151(100%) NPV=25/73(34.2%) PPV=197/200(98.5%) NPV=22/24(91.6%)

  9. Pneumococcal Vaccination Measure for Pneumonia Truth Truth + - + - EMR + + RN - - PPV=151/151(100%) NPV=25/73(34.2%) PPV=197/200(98.5%) NPV=22/24(91.6%)

  10. Chart+ Clinical Document Management Reports (CDM) Clinical Notes  Hospital Summary Digital Dictation Documents Browser History Location System, Mass (HLSM) Image Capture Environment (ICE) Master Sheet Mayo Single Logon (MSL) Orders97 Patient Appointment Guide (PAG) Patient Provided Information (PPI) QREADS Scheduling (GPAS, MSS) Shorthand LastWord LW Navigation (Home Screens & Chart Tabs) Allergies Chart Summary View Episodes Flowsheet Charting Immunizations InBox Medication Management  Orders: Cumulative Orders97 Summary (COS) Inpatient Plan of Care Outside Film & Media Patient Check-In Locator (PCIL) Problem List/Service Recognition (PLSR) Results and Reports (Viewers/Labs, Documents Display, Printing, Patient List Manager) Mayo Integrated Clinical Systems (MICS)

  11. Next Steps • Proceed with pre-filling “positive” immunizations from EMR. • Manually review “negatives” until NPV becomes acceptable. • Reminders/education on use of Immunization Module. • Repeat process with other common quality measures. • Develop method to ensure accuracy of EMR-based measurement.

  12. Lessons Learned • Initial algorithm will contain flaws– plan to revise. • Computer can easily determine measure when service is performed and documentation standards followed. • Verification of EMR-based measures extremely valuable. • Large sample to establish accuracy • Buy-in from stakeholders to use EMR • EMR has wealth of capabilities for quality measurement.

More Related