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Next generation electronic medical records and search a test implementation in radiology

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Presented by David Piraino, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
& Daniel Palmer, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic

Most patient specifc medical information is document oriented with varying amounts of associated meta-data. Most of pateint medical information is textual and semi-structured. Electronic Medical Record Systems (EMR) are not optimized to present the textual information to users in the most understandable ways. Present EMRs show information to the user in a reverse time oriented patient specific manner only. This talk discribes the construction and use of Solr search technologies to provide relevant historical information at the point of care while intepreting radiology images.

Radiology reports over a 4 year period were extracted from our Radiology Information System (RIS) and passed through a text processing engine to extract the results, impression, exam description, location, history, and date. Fifteen cases reported during clinical practice were used as test cases to determine if ""similar"" historical cases were found . The results were evaluated by the number of searches that returned any result in less than 3 seconds and the number of cases that illustrated the questioned diagnosis in the top 10 results returned as determined by a bone and joint radiologist. Also methods to better optimize the search results were reviewed.

An average of 7.8 out of the 10 highest rated reports showed a similar case highly related to the present case. The best search showed 10 out of 10 cases that were good examples and the lowest match search showed 2 out of 10 cases that were good examples.The talk will highlight this specific use case and the issues and advances of using Solr search technology in medicine with focus on point of care applications.

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Next generation electronic medical records and search a test implementation in radiology

  1. 1. Next Generation Electronic Medical Records andSearch: A Test Implementation in RadiologyDavid Piraino,MD Daniel Palmer, PhDCleveland Clinic John Carroll University
  2. 2. Introduction• Most patient specific medical information is document orientedwith varying amounts of meta-data.• Most of patient medical information is textual and semi-structured.• Electronic Medical Record Systems (EMR) are not optimized topresent textual information• EMRs currently show information in reverse time order only.• This talk describes the construction and use of Solr searchtechnologies to provide relevant historical information at the pointof care while interpreting radiology images.
  3. 3. Grand challenges (2008)in clinical decision support• Improve the human–computer interface• Disseminate best practices in CDS design, development, and implementation• Summarize and prioritize patient-level information• Prioritize and filter recommendations to the user• Create an architecture for sharing executable CDS modules and services• Combine recommendations for patients with co-morbidities• Prioritize CDS content development and implementation• Create internet-accessible clinical decision support repositories• Use free text information to drive clinical decisionsupport• Mine large clinical databases to create new CDSDean F. Sittig et al, Journal of Biomedical Informatics 41 (2008) 387–392
  4. 4. Too Much Information (2012)• In the time-pressured clinical setting,clinicians faced with large amounts of patientdata in formats that are not readilyinterpretable often feel ‘informationoverload’.Ketan Mane et al, Journal of Biomedical Informatics 45(2012) 101-106
  5. 5. What is out of place?• Blue• Green• Cleveland• Red• Yellow
  6. 6. What is out of place?• Boston• new york• Cleveland• Chicago• Denver• San Diego• atlanta• Toronto• Mexico City• Columbus• Nashville• Paris• Seattle• Vancouver• Washington DC• Miami• dallas• Houston
  7. 7. Large number of images,varying levels ofapplicability, incompletehistories, data stored inmany different locationsChaos in Primary Care(2011)InformationOverloadInformationScatterUnrelatedInformationMentalWorkloadSituationAwarenessFurther Cognitive InfluencesProblem solvingProblem identificationDecision makingDiagnosisTreatmentModeratorsInterruptionsExpertiseTimeInformation Chaos in Primary Care: Implications for PhysicianPerformance and Patient SafetyJohn W Beasley, MD1,2, Tosha B. Wetterneck, MD, MS3, Jon Temte, MD, PhD1, Jamie ALapin, MS2, Paul Smith, MD1, A. Joy Rivera-Rodriguez, MS2, and Ben-Tzion Karsh, PhD*,1,2Journal of the American Board Family Medicine. 2011 November; 24(6): 745–7511Department of Family Medicine, UW-Madison School of Medicine and Public Health2Department of Industrial and Systems Engineering, UW-Madison3Department of Medicine, UW-Madison School of Medicine and Public Health
  8. 8. Existing Information ConfusionED visitTelephoneOfficeEDOfficeAdmissionSurgeryOpthoED visitTelephoneOfficeEDOfficeAdmissionSurgeryOpthoLabsCBCPSAGlucosePotassiumGlucoseUrinalysisPatient Image history presented as a listKey components missing
  9. 9. Inspiration• Boston 2012And we hope to be inspired again this week with your help
  10. 10. Warning 28 Days Later• One person with other full time job• Running on moderately high end workstation• Indexed 7 million radiology reports• Providing types of searches that wouldotherwise be “impossible”
  11. 11. MRI shoulder without contrastRelevant Previous Reports
  12. 12. MRI shoulder without contrastThere is evidence for a full thickness tear of the supraspinatus tendonUpdated relevance
  13. 13. MRI shoulder without contrastThere is evidence for a full thickness tear of the supraspinatus tendonThere is a partial tear of the subscapularis tendon with anterior medialdislocation of the long head of the biceps tendonAdditional Update to Relevance
  14. 14. Evaluation• 15 cases reported during clinical practice were used as testcases to determine if "similar" historical cases were found.• For these 15 cases all searches completed within 3 seconds• Considered only the top 10 matches returned by search• Number of cases that illustrated the questioned diagnosis asdetermined by a bone and joint radiologist.
  15. 15. Results for the 15 cases• Average performance:– 7.8 out of the 10 highest rated reports showed asimilar case highly related to the present case.• Best performance:– 10 out of 10 cases relevant• Worst performance:– only 2 out of 10 cases relevant
  16. 16. In Practice• An example case:– Medical image: vascular mass in the hand– LucidWorks search considered first 10 results• Based on text, eliminated unrelated cases– Found and studied 2 pertinent cases• Showed similar masses with similar uncertainty• Used to generate data sets for other researchprojects
  17. 17. Input FlowInput StreamHL7 streamorDelimited FileSolr XMLwithnewfieldsSolrIndexandrepositoryPreprocessalgorithmSolr processing
  18. 18. Input Stream (HL7 Protocol)XXXX|Date|XXX-01-01|XXXX|XX:17:00.0|14||XXX-XXX-RADIOLOGY-CCF|XXX|XXX|CCF|I|XXXX|LMBR|XXXX|A|MRA OF HEAD|MR||||||* * *Final Report* * * DATE OF EXAM: XXXXX12:07AM LMM 0432 - MRA OF HEAD /ACCESSION # XXXXX PROCEDURE REASON: cva* * * * Physician Interpretation * * * * RESULT: MRA OF THE HEAD WITHOUT CONTRASTHISTORY: Subarachnoidxxxx TECHNIQUE: Time of flight MRA of the cervical circulation wasperformed. COMPARISON: none FINDINGS: Examination is xxxxxxxx. IMPRESSION: Smallxxxxxxxx. Transcriptionist: PSC Transcribe Date/Time: Jan 1 XXXX 10:14P Dictated by :XXXXXX, MD This examination was interpreted and the report reviewed and electronicallysigned by: XXXXX, MD On Date|
  19. 19. <add><doc><field name="department">Radiology</field><field name="category">report</field><field name="pid">EXXXXXX</field><field name="sex">Male</field><field name="id">XXXXX</field><field name="did">XXXXX</field><field name="modality">CT</field><field name="title">MRI of the HEAD</field><field name="date">XXX-01-09T09:34:00Z</field><field name="year">XXX</field><field name="month">01</field><field name="day">09</field><field name="hour">09</field><field name="history">Subarachnoidxxxx</field><field name="site">WRC</field><field name="physician">XXXXX</field><field name="body"> On the head XXXXXXXXXX on the base of the neck. </field><field name="impression"> 1. XXXX. 2. XXXXXXX. 3. XXXXXXXX </field><field name=“positive">XXXXXXXX</field><field name=“negative">XXXX</field><field name=“neutral">XXXX</field><field name=“anatomy”>skull</field><field name=“side”>none</field></doc></add>Solr Input XML stream
  20. 20. Search FlowExtracted textSolrQueryRelevantDocumentsPreprocessalgorithmQuery SolrClinical encounter Radiology reportData Extractor Data ExtractorMore informationProcessed TextSolr queryconstructor
  21. 21. Similar Imaging DiagnosisPatient: Anatomy, Modality, Diagnosis, and TimePatientPathologyLabPatientClinicalnotes(ProviderDiagnosisTime)SpeculativeInterface
  22. 22. Solr – currentimplementationSimilar Imaging DiagnosisPatient: Anatomy, Modality, Diagnosis, and TimePatientPathologyLabPatientClinicalnotes(ProviderDiagnosisTime)
  23. 23. ImageSphere
  24. 24. Challenges to Building Prototype• Time vs. Data• Sensitivity of queries• Automating human scan/evaluation step• Lack of a non-radiologist fitness function• Migration from development-only LucidWorksplatform to embedded Solr API queries
  25. 25. Time vs. Data• 2-3 cases max viewed(10 considered)• High relevance required• Potentially 10’s ofthousands to select from
  26. 26. Sensitivity of Queries• Many query parameters– proximity, boost, not• Yields range of results– 10/10 through 0/10• 2 orders of magnitude inquery times• (wrist fracture)• (wrist fracture)~2• (wrist fracture)~10• wrist^3 fracture• -(no near fracture)
  27. 27. Queries: Good News/Bad News• Basic queries provide great results– Better than expected– Top 10 results quickly yield cases to view• Query refinement proves to be difficult– Little or no correlation between querymodifications and changes in results– No consistent direction to investigate
  28. 28. Human in the Loop• Top 10 results displayed in text form• Human quickly scans and selects best• Must maintain this ability in visual GUI• Evaluation difficult because…
  29. 29. Fitness Function == Radiologist• Need expert to determine value of queryresults• Large impact on debugging…• “Live” statistics gathering and provisional datagathering techniques
  30. 30. Migration for Prototype• Manual process using LucidWorks provedconcept• Use Solr API to implement an automateddelivery/display system• Dependent on an intuitive user interface
  31. 31. Thank You and…any Answers?
  32. 32. CONFERENCE PARTYThe Tipsy Crow: 770 5th AveStarts after Stump The ChumpYour conference badge getsyou in the doorTOMORROWBreakfast starts at 7:30Keynotes start at 8:30CONTACT (optional)David Piraino MDpiraind@ccf.org

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