402 Intro to Clinical Thinking, Winter 2010 Final Group Presentation, Group 1

Transcription

1 402 Intro to Clinical Thinking, Winter 2010 Final Group Presentation, Group 1 Group 1, Members Michael Aguilar Hugo Fernandes Charles Kitzman Biljana Spasic Clinical Case Single visit There are prior visits recorded Paper based documentation Possibility of C6-C7 radiculopathy Uncertain diagnoses Goal: Use technology tools on a real post-mortem case to see if an outcome would have been different. March 1, 2010

2 Approach Steps Subjective Medical history History of the present illness Chief complaint Objective Physical exam Vitals Assessment Working diagnoses Plan Labs / diagnostic ordering Instructions for the patient Conclusion

3 PATIENT INTAKE 50 year old male Presence of Ulcer, Hernia & Back Pain Surgical Hernia 1980, Microdisectomy 1991 Smoker Started at age 18 Alcohol consumption Exercises No current drug use Tetanus shot

4 EMPOWERING THE PATIENT ELECTRONIC COMMUNICATION MEDICAL SUMMARY Personal Information Health Conditions Allergies Current Medications Immunizations Patient Contact Information Update Personal Information Prescription Refill Billing Questions ONLINE PATIENT BENEFITS Respect for privacy and patient confidentiality Instant access to patient medical history Minimized redundant data entry by saving information communicated through the portal directly into patient EMR Secure patient online registration

5 CAPTURE OF CHIEF COMPLAINT and HISTORY OF PRESENT ILLNESS Numbness and tingling in left hand and left leg Mild weakness in upper extremities

6 Sample EMR Interface

7 EMR DOCUMENTATION / CARE DELIVERY & ENCOUNTER MGMT Capture chief complaint / left side numbness, would have been more evident Enable retrieval of relevant data from previous visits, including stored diagnostic tests, diagnostic procedures and labs Create physician-specific review of systems or chief complaint lists Categorize clinical data for problem management Drill down into summaries for all patient problems, history, encounters, medications, orders and procedures Efficient management of problem list

8 Objective: Vitals and Physical Exam Vitals can be captured via equipment interfaces or manually entered. Over time, trending can be easier to detect through gridding or graphing of data. Triggers can tip off clinical teams to abnormal values in vitals, both for quality control and decision support.

9 Some Examples. The vitals in our particular case were unremarkable relative to the complaint.

10 Physical Exam: Discrete Data Complaints, Hx, and ROS drive the decision regarding which area to exam. EHR allows for capture of discreet data that can be used to (soon) autofeed decision support tools such as Dxplain.

11 The arrows indicate simple positive/negative indicators for signs and symptoms relative to the area of the exam. These are prime candidates for decision support values. Other areas are also eligible as discrete data is provided via picklist.

12 Our Case: Objective Vitals were unremarkable Complaint guided the provider to examine the neck, back, and eyes. No unusual findings, both eyes equal to light bilaterally, pt denied and pain in neck or back. lt. side numbness and weakness validated

13 Assessment Identify the clinical diagnosis for the chief complaint Create or update the patient problem list Developing diagnostic possibilities for the complaint and then testing each diagnostic possibility Clinical diagnostic decision support tools could help providers in this area Provider may have a lack of knowledge about the causes for certain symptoms. Provider wants to use the tool to identify diagnostic possibilities that the provider may not have thought of (Check List)

14 Isabel Second Generation CDSS tool Isabel is a Web-Based Clinical Diagnosis Support System developed by Isabel Healthcare ( Provider enters in the clinical symptoms of the encounter and other patient demographic information into the system System then uses natural language processing and search algorithms to search the Isabel database for diagnoses that match the clinical information entered Diagnoses are listed by body system but not by clinical probability. Diagnosis list can be customized. Medication module that shows drugs that can causes clinical symptoms Bioterrorism module A group of researchers tested the application using test cases and Isabel suggested the correct diagnosis in 48 of 50 cases (96%) with key findings entry. The correct diagnosis in 37 of the 50 cases (74%) if the entire case history was pasted in

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16 Isabel Application Provider Issues and other features Isabel does not allow the user to further limit the number of diagnosis returned by entering in negative data Sometimes Isabel can return too many diagnoses which make the list not very useable by providers Isabel PRO Knowledge Mobilizing System IKMS allows providers to search a database for clinical reference information. Isabel is designed to be used with a PDA and Isabel can be linked to applications like UpToDate and MD Consult. Isabel can be integrated into the EHR so that information entered into EHR modules can be used to create a query string that can be passed to Isabel.

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18 Diagnostic Uncertainty The common diseases with sufficient evidence to support the DX generated by DXPlain are listed below. + Sclerosis, multiple + Spinal cord, compression + Carpal tunnel syndrome + Herniated cervical disc + Spondylosis, cervical In order to rule out some of these possible diagnoses and help identify the correct diagnosis we need to use a test to clear up the diagnostic uncertainty

19 Problem List Numbness and tingling in left hand Numbness and tingling in left leg Mild weakness in upper extremities Leg numbness in thigh region Lower left leg weakness Unemployed Smoker since 18 Prior street Drug use

20 Sample EMR Interface

21 Labs / Diagnostic Ordering Physician s orders from the case file: Order MRI of the neck to rule out significant pathology and spinal cord compression. Note: Sliding fee so payment comes from out of pocket. Analysis of the Diagnostic Test MRI was the only suggested diagnostic procedure. There is not enough information to calculate sensitivity and specificity ratios. Tools to use Automate MRI ordering and processing.

22 Hospital Information System/PACS Workflow ADT Hospital Information System ADT ORDERS Order Entry System ADT PACS Broker Modality Worklist DICOM Images Modality Server / Deep Archive ORDERS Validation, Study Updates, Patient Updates, Pre-fetching, Reports ORDER RESULTS Images Reports Radiology Information System Workstation

23 Study Read Events Report is Marked as Read Workstation PACS Broker Broker Can Send Radiology Information System an HL7 ORM Message to Indicate a Read Status DICOM HL7 Workstation Updates Status on Server Workstation Updates Study Status to Read Radiology Information System Server

24 Instructions for the Patient Instructions from the case file Follow up in ER if symptoms worsen. Return to Clinic in one week to discuss MRI Results Follow up outcome: Patient did not keep the appointment. No call. No reschedule.

25 Suggestions: Automate Patient Follow-up Processes Automate identification of high risk patients integrate into EMR or other information system in use. Flags are set by the system or manually. Reminder to the patient via phone (if not in place already). Automate notifications via system alerts. Follow up via phone with critical patients in case of a missed appointment. Automate notifications via system alerts. When no response, then automatically reschedule and notify the patient. Produce reports of missed appointments on regular bases (weekly or monthly). Make follow up calls to the critical patients with missed appointments.

26 Suggestions: Communicate Diagnoses to the Patient The interpretation of all results can be made available to patients via a web portal. Communicate diagnostic uncertainty to the patient: be truthful to the patient at all times put it in context: what s been ruled out, what was inconclusive tailor delivery: different patients / different tactics explain risks of continued testing provide patient with resources to become better informed.

27 Conclusion(s) Pt Left side weakness and numbness worsened. Did not keep followup appt. Within a month he had crashed his car twice and fallen several times. Brought to ER and Admitted. Slurred speech, left side facial droop prompted CT of head. Operated on but never recovered. Despite our hopes of a different outcome, likely not a reality here

28 References Graber Mark,Mathew Ashlei (2008). Performance of a Web- Based Clinical Diagnosis Support System for Internists. Journal of General Internal Medicine. Issue Volume 23, Supplement 1,37-40 Improving Patient Care and Safety: Use of electronic diagnosis reminder systems (2009), Mack EH, Wheeler DS, Embi PJ. (2009), Clinical decision support systems in the pediatric intensive care unit, Pediatr Crit Care Med Vol. 10, No