Infrastructure for Clinical Data Exchange

Transcription

1 Infrastructure for Clinical Data Exchange Amy Abernethy, MD, PhD FEBRUARY

2 Instrument Data Highly Structured

3 Administrative Data Highly Structured

4 Clinical Research Data Intentionally Structured

5 4% vs. 96%

6 Electronic Health Record Data Sometimes Structured?

7 A conceptual model for EHR data

8 Demographics Diagnosis Structured Data Unstructured Data Outside Practice Visits Hospitals Labs Labs e-prescribing Electronic Health Record Physician Notes Radiology Reports Pathology Reports Discharge Notes

9 Demographics Diagnosis Structured Data Unstructured Data Outside Practice Visits Hospitals Labs Labs e-prescribing Electronic Health Record Physician Notes Radiology Reports Pathology Reports Discharge Notes

10 Albumin [Mass/volum g/d e] in Serum or L Plasma

11 Demographics Diagnosis Structured Data Unstructured Data Outside Practice Visits Hospitals Labs Labs e-prescribing Electronic Health Record Physician Notes Radiology Reports Pathology Reports Discharge Notes

12 Specific data points of interest: KRAS testing status KRAS test result Date sample was collected Date sample was received in lab Date result was provided to physician

13 Technology leverages people & capabilities

14 Demographics Diagnosis Structured Data Unstructured Data Outside Practice Visits Hospitals Labs Labs e-prescribing Electronic Health Record Physician Notes Radiology Reports Pathology Reports Discharge Notes De-identification of all data Data processing Aggregated EHR data

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16 Mortality data Genomic data Progression data Processed EHR Data Claims data Immunology data Patient reported data

17 Organizing data around the patient

18 Diagnosed with Stage II NSCLC A dataset is an amalgamation of many patient stories Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death

19 Organize datasets around patient stories Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death

20 Organize datasets around patient stories Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Patient age Gender Race Insurance Stage Regimen name Duration of therapy Dosage Concomitant meds Date of death Structured EMR data

21 Organize datasets around patient stories Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Patient age Date of Gender surgery Race Insurance Stage TNM staging Structured EMR data Unstructured EMR data Sites of metastases Biopsy date Type of test conducted Turnaround time for test Number of unsuccessful tests Test result, if successful Type of EGFR mutation Regimen name Duration of therapy Dosage Concomitant meds Response Biopsy date Lab name Test result PDL1 expression T790M mutation Date of death

22 Organize datasets around patient stories Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Patient age Date of Gender surgery Race Insurance Stage TNM staging Structured EMR data Unstructured EMR data External datasets Sites of metastases Biopsy date Type of test conducted Turnaround time for test Number of unsuccessful tests Test result, if successful Type of EGFR mutation Regimen name Duration of therapy Dosage Concomitant meds Response Biopsy date Lab name Test result PDL1 expression T790M mutation Immune profile Date of hospitalization Cost of care Date of death Date of death

23 Organize datasets around patient stories Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Patient age Date of Gender surgery Race Insurance Stage TNM staging Structured EMR data Unstructured EMR data External datasets Algorithms Sites of metastases Time to recurrence Biopsy date Type of test conducted Turnaround time for test Number of unsuccessful tests Test result, if successful Type of EGFR mutation Regimen name Duration of therapy Dosage Concomitant meds Response Line of therapy Biopsy date Lab name Test result PDL1 expression T790M mutation Immune profile Date of hospitalization Cost of care Line of therapy Date of death Date of death Consensu s date of death

24 Curating the complete picture Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Stage Dx Date Immune profile Patient Demographics Biomarkers Treatment Response Hospitalizations/Cost Mortality A B C D

25 Documenting source, quality & provenance Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death Stage Dx Date Immune profile Patient Demographics Biomarkers Treatment Response Hospitalizations/Cost Mortality A PDL1 B C D

26 Documenting source, quality & provenance Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Abstracted by Sue Smith Progresses on 1/5/16 on at 10:10am Biomarker documents 1L, were tested reviewed for Tested for Starts PD-L1 and/or EGFR Medical and 1L record from West re-tested Florida for Cancer Clinic ALK Quality of therapy PDL1 abstraction EGFR Completeness is 99% Sue Smith is 96% accurate at last testing Inter-abstractor agreement 97% Kappa 0.93 Audit trail for any changes Dataset freeze and storage Starts 2L therapy, deteriorates and is hospitalized Death A Stage Dx Date PDL1 Immune profile Patient Demographics Biomarkers Treatment Response Hospitalizations/Cost Mortality B C D

27 Data quality must be a focus Variable Structured data only Flatiron data completeness Smoking status 0% 1 94% Histology 37% 99% 2 Stage 61% 95% EGFR results (of those tested) PDL1 results (of those tested) 11% 99% 0% 99% Site of Met Inter-abstractor Agreement Kappa Bone 97% 0.93 Brain 96% 0.91 Liver 92% 0.83 Lung 94% 0.87

28 Population based Always on & organized Up to date

29 The impact of having highly organized data

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31 25% 12%

32 3.5% 5.2% 46%

33 1.4% 22% 641/2826

34 1 st line <5%

35 2 nd line 2.6% 44%

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38 Combo, July %

39 1 st line, BRAF WT 19%

40 Putting it all together

41 EHR data as the backbone for clinical data exchange Diagnosed with Stage II NSCLC Undergoes surgery for early-stage disease Develops metastatic disease Tested for EGFR and ALK Starts 1L therapy Progresses on 1L, tested for PD-L1 and/or re-tested for EGFR Starts 2L therapy, deteriorates and is hospitalized Death

42 Connect EHR data to other sources Mortality data Genomic data Progression data Processed EHR Data Claims data Immunology data Patient reported data

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44 Potential Policy Solutions Policy solutions should demand interoperability But interoperability uncovers the unstructured data problem Recognize that unstructured data processing is costly Like crude oil it must be refined to be useful Reward sharing and working together Support efforts in data linkage HIPAA & re-identification risk Data clean zones Develop the workforce

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46 Chart abstraction Expensive Inaccurate EMR Source Data Pathology report Radiology report Physician notes Technology-enabled abstraction Use a machine wherever appropriate Human review to ensure quality Extremely efficient Always improving Abstractor feedback and continuous QA