Using Merlin to Grow Research

1. Using Merlin to Grow Research Charles J. Mullett, MD, PhD

2. Objectives • Review the ultimate breadth and depth of the Merlin implementation on the health sciences campus. • Understand the tension between free text notes and explicit data entry. • Learn the options and mechanisms for large data set retrieval. • Discover automated methods to identify potential study subjects • Discover the potential for scholarly activity around the use of Merlin in the process of delivering care.

3. Outline • Merlin • Retrospective Studies • Data availability • Data retrieval method • Examples • Prospective Studies • Alerts for potential subject candidacy • Clinical decision support – improving current care

4. Merlin is our name for Epic’s clinical information system (suite of applications) Broad ranging implementation for UHA and WVUH: “soup to nuts.” What is Merlin?

5. What will be in Merlin? • Outpatient registration: • appointments and insurance info • Outpatient nurses • Vitals, chief complaints, immunizations • Outpatient physicians • Problem lists, medications, physical exams findings • Problem lists/diagnoses, laboratory results, notes, therapies • Inpatient nurses • Vitals, assessments, medications administered • Ancillary • Respiratory therapy treatments • Inpatient physicians • Exams, problems, medications, laboratory results, diagnoses, orders

6. Retrospective “Chart” Reviews • Potentially a large amount of queryable information • Example: • Old method (billing codes based): • May I have all patients discharged with an ICD-9 code for diabetic ketoacidosis? • Receive a large list of MRNs, pull paper charts and gather data by hand, searching for patients with the specific complication of cerebral edema • New method (potentially more specific): • May I have all patients between the ages of 0 and 21 years treated with IV insulin and having a bedside or laboratory blood sugar value over 200 and treated with mannitol or hypertonic saline for possible cerebral edema? • Receive a smaller list of MRNs, then review and collate data from online medical records

7. Data Query Issues • Garbage in / Garbage out • Example from Epic meetings: Hysterectomy in a male • Form of the information – coded data versus dictated speech (free text) • Uniformity of data-field use – do all floors dip urine for glycosuria?

8. Free Text vs. Coded Entry • Historically – physicians document in prose in the paper medical record. • Even more in dictated communications • Easy to understand, tells a good story • However, opaque to computers • Alternative – template-based coded data entry

9. Insert slide of coded template

10. Benefits of Free Text Tells a story Faster for physician if dictating Explain complex medical reasoning Shortcomings of Free Text Not easily retrievable for research Not interpretable by Merlin No assistance with coding Reduced alerts and reminders Benefits of coded data entry Retrievable for research Interpretable by Merlin Faster? Shortcomings of coded templates Awkward for user. Learning curve Awkward/hard to interpret for downstream clinicians. Generic notes? Free Text vs. Coded Data

11. Merlin Researcher: know your target • Medications – solid via outpatient list and inpatient MAR • Vitals, growth parameters – good • Immunizations – need 5-10 year period of “ramping up” • Diagnoses – good secondary to the problem list, but hampered by synonyms • Patient histories – not so good. • Search for patients presenting with polydipsia • ED – diabetes template – polydipsia is a datapoint in template • Peds clinic – no template? History typed by medical student. “Polydipsia” perhaps not even mentioned

12. Anil Jain, MD & Holly Miller, MD, MBA

18. WVU Decision Support • Contact info for data requests: • Nancy Vest, director – vestn@wvuh.com • Kim Evans, - evansk@wvuh.com • Barbara Haddix, haddixb@wvuh.com • Turnaround time will vary • IRB approval required for projects with an intent to publish.

19. Prospective Studies • Subject identification / patient enrollment • Improving care through embedded clinical decision support

25. Michael G. Kahn; The Childrens Hospital, Denver

30. Michael G. Kahn; The Childrens Hospital, Denver

39. Clinical Decision Support • Switching from Merlin for finding data or eligible patients to • Merlin as the tool to improve care

41. Checklists • 1935 – Boeing’s Flying Fortress crashed on its inaugural flight – pilot error. Post-crash, a group developed pre-flight checklists to simplify the task of managing multiple settings for takeoff. Now routine for pilots. • Peter Pronovost, MD developed a similar checklist for insertion of central lines. Not rocket science: • Wash hands with soap • Clean patient’s skin with chlorhexidine • Put sterile drapes over the entire patient • Wear a hat, mask, gown, and glove • Put a sterile dressing over the entire catheter site. • Benefit: • Help with memory recall • Make explicit the minimum expected steps; higher standard of baseline

42. Study of Checklist for CVLs • Empowered ICU nurses to enforce use of the checklist during physicians’ insertion of central venous lines • The 10 day line infection rate decreased from 11 to 0%. Calculated that 8 lives were saved and $2 million in costs • Pronovost invited to repeat efforts for all of Michigan and Spain. Similar impact. Michigan calculates 1500 lives and $75 million in costs saved. Citation: Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32:2014-2020.

43. Pronovost: The Third Bucket • Tasks of medical science • Bucket 1: Understanding disease biology • Bucket 2: Finding effective therapies • Bucket 3: Insuring those therapies are delivered effectively • Insuring delivery of effective therapies has been largely ignored by research funders • Merlin = opportunity • via clinical decision support alerts and reminders

44. Impact of Epic’s Clinical Decision Support • Reference • Feldstein et. al. J Am Geriatr Soc. 2006 Mar;54(3):450-7 [Kaiser Permanente, Portland] • Title • Electronic medical record reminder improves osteoporosis management after a fracture: a randomized, controlled trial • Finding • In a study of women with a hip fracture in the Kaiser Permanente health system, an alert to the primary care physician increased the percentage of women receiving a bone mineral density test and/or osteoporosis medications from 5.9% to 43.9%

45. Our Opportunity - • Help unify bedside care with known best practices • Via checklists – IHI ventilator bundle • Via computerized clinical decision support • Formula: • Patient problem + underutilized (screening test OR therapy) + computer readable trigger points = potential site for a helpful alert • Have a good idea? Contact me, Kevin Halbritter, or Ann Chinnis.

46. Growing Research - Summary • Broad data queries will be available in Merlin. Potentially easy and very helpful. But your results will vary. • Automated alerts for study subject identification and screening has the potential to dramatically increase the rate of enrollment of prospective clinical studies • Research data forms can be developed in Merlin (not discussed much today) • Computerized clinical decision support can bring clinical trial breakthroughs to the bedside for improvements in care. These improvements can be measured and reported.