Presentation Transcript

1. IBM Medical Records Text Analytics Solution Helps UNC Healthcare Improve the Quality of Hospital DischargesSession Number ECA-1419A

   Carlton Moore, MD UNC Healthcare Fiodar Zboichyk IBM
2. Overview Hospital Readmission Rates Medical and Economic Impact Reasons for High Readmission Rates Importance of discharge summary Proposed NLP solution Development issues (example, unstructured, inconsistent) Results (sensitivity, specificity) Future directions
3. 30-Day Hospital Readmission Rates by State Estimated annual cost to Medicare = $17.4B Jencks S, Williams M, Coleman E. N Engl J Med 2009; 360 (14): 1418-28
4. Economic Impact on Hospitals In 2013 Medicare will start applying financial penalties to hospital with higher than expected readmission rates Other health insurers are likely to follow Medicare’s lead!! Sample Hospital Potential Penalty = (# of patients with condition) x (Avg. reimbursement for condition) x (% Higher than expected)
5. Why are Hospital Readmission Rates So High?
6. Conceptual Framework Patient discharged with unresolved medical issues that need to be addressed after leaving hospital follow-up physician visits follow-up tests and procedures Discharge Instructionsa concise action plan describing what needs to occur after a patient leaves the hospital Poor communication of discharge instructions Only 50% of discharge summaries are ever received by patients’ physicians Discharge instructions not carried out Adverse Event Definition: condition worsens because of inappropriate or inadequate medical care Hospital Readmission
7. Discharge Instructions 50% not completed 27% no completed 15% not completed Moore C, McGinn T, Halm E. Arch Intern Med. 2007;167:1305-1311
8. Examples of Discharge Instructions not Completed Moore C, McGinn T, Halm E. Arch Intern Med. 2007;167:1305-1311
9. Adverse Events after Hospital Discharge 1 in 5 (20%) patients has an adverse event shortly after hospital discharge ADE: adverse drug event Other: incorrect treatment and/or missed diagnosis Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. Ann Intern Med. 2003
10. Example of an Adverse Event A patient with heart failure started receiving spironolactone in the hospital. The patient was sent home with a prescription for this medication in addition to previous use of ramipriland potassium supplements. Blood tests were not monitored after hospital discharge even though it was clearly documented in the discharge summary that the patient needed follow-up blood tests. Two weeks later the patient developed extreme weakness and went to the emergency room. Blood tests revealed a potassium level >7.5 mmol/L (normal = 4.5 mmol/L). Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. Ann Intern Med. 2003
11. Purpose of Project Extract key elements of the discharge instructions: Discharge medications Discharge diagnosis Follow-up appointments Convert the extracted data into structured format that can be: electronically transmitted to healthcare providers responsible for care after hospital discharge used to generate reminders and alerts to healthcare providers
12. Discharge Instructions
13. Study Design Discharge instructions (Name, Type/Location, Time Frame) were extracted from free-text hospital discharge summaries: Manual review (physician) IBM Content Analytics (ICA) Accuracy of ICA was calculated using manual physician review as the “gold standard” Sensitivity, specificity Positive predictive value, negative predictive value
14. Measurement Overall Accuracy = (TP +TN)/(Total) Sensitivity % of records containing follow-up elements that were identified via text analytics. Specificity %of records lacking follow-up elements that were not flagged via text analytics. Positive Predictive Value (PPV) % of records flagged as containing follow-up elements using text analytics that actually contained follow-ups
15. Results: Accuracy of Text Analytics in Identifying Follow-up Appointments and Diagnoses
16. UNC Health Care Solution Component Architecture Medical Terminology Health Language Inc. Language Engine UNC Health Care Terminology SNOMED, RxNorm, ICD-9 ICD-10, CPT-4 Document Server (UIMA Pipeline) ICA-LanguageWare Resource Workbench ICA Annotators JDBC UIMA CAS Consumer Medical Annotators Apache Lucene Search Engine IBM Content Analytics Lucene Index ICA-Text Miner Web Application Extended ICA JDBC Crawler IBM InfoSphere Guardium Data Redaction Discharge Follow-up Reporting UNC Health Care Clinical Data Warehouse Business Intelligence Tool Pathology Reports Discharge Summary Reports Echocardiogram Reports
17. Project Lessons Learned Medical texts are more complicated than we thought… again. Standard terminology (RxNorm, SNOMED CT, ICD9, …) Absolutely required, but not good enough for dictionary matches “tick-born disease”, but not “tick borne illness”. Diagnoses Negation is actually just part of the range – “rule out”, “possible”. “Left femur fracture” and “fracture, left femur”. “Discharge diagnosis: same as above”. Follow-ups. Sometimes just “fup”. Usually “Dr. Good”, but sometimes “her cardiologist”. Usually “Vascular Surgery Clinic”, but sometimes “heme-onc”.
18. Summary NLP will improve communication of discharge instructions: Improve patient care (reduce hospital readmissions) Reduce risk of Medicare penalties to the hospital
19. Future Directions Cohort identification for researchers and quality improvement specialists Cancer diagnoses in pathology reports Findings in radiology reports Extracting quality measure data for the hospital Researchers 156 current NIH-funded grants ($75M) utilizing NLP