using merlin to grow research charles j. mullett, md, phd
TRANSCRIPT
Using Merlin to Grow Research
Charles J. Mullett, MD, PhD
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.
Outline
Merlin Retrospective Studies
– Data availability– Data retrieval method– Examples
Prospective Studies– Alerts for potential subject candidacy– Clinical decision support – improving current care
What is Merlin?
Merlin is our name for Epic’s clinical information system (suite of applications)
Broad ranging implementation for UHA and WVUH: “soup to nuts.”
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
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
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?
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
Insert slide of coded template
Free Text vs. Coded Data
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?
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
Anil Jain, MD & Holly Miller, MD, MBA
WVU Decision Support
Contact info for data requests: – Nancy Vest, director – [email protected]– Kim Evans, - [email protected]– Barbara Haddix, [email protected]
Turnaround time will vary IRB approval required for projects with an
intent to publish.
Prospective Studies
Subject identification / patient enrollment Improving care through embedded clinical
decision support
Michael G. Kahn; The Childrens Hospital, Denver
Michael G. Kahn; The Childrens Hospital, Denver
Clinical Decision Support
Switching from Merlin for finding data or eligible patients to
Merlin as the tool to improve care
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
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.
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
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%
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.
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.