Does Data Reported from EHRs Really Reflect the Health of a Population? APHN Conference May 6, 2014
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1 Does Data Reported from EHRs Really Reflect the Health of a Population? APHN Conference May 6, 2014
2 Objectives The participant will be able to: 1. Recognize three (3) barriers to accurate data when reporting from a certified EHR 2. Critically analyze data from certified EHRs before accepting data as reliable 3. Propose ideas to implement systems changes that will improve population data collection
3 Population Data (The Old Way) Claims Data The amount of times a test or service was provided for a patient No results of tests No data for patients with no insurance Sampling Data The results are collected, but only from a small number of charts No data for the whole population No exclusions by payer type
4 Advent of the Electronic Health Record (EHR)
5 Systems Intervention Focused on Community Health Centers (CHCs) in NM Goal: To improve the ABCs of diabetes FY : Assessment questionnaire FY : Work with one CHC with a high % of patients with diabetes and an EHR FY : Add another CHC with a different EHR vendor FY : Continue to work with the two CHCs. Spread learnings to other CHCs with the same EHRs (11 total)
6 Focus of the Work (We Thought) Communicate with Providers Standards of Practice / Clinical Guidelines Show them the data & compare it to the standards
7 Focus of the Work (In Reality) Data Communicating About Data Data Recovery Data Reporting
8 Communicating About Data Learning a whole new language Hgb A1c A1c Hemoglob
9 Pt. DOS Test Name Results Vitals 1 9/27/2010 HgbA1c /27/2010 LDL /60 2 7/15/2010 A1c /72 3 8/24/2010 Hemoglob 7.7 9/30/2010 A1c /30/2010 Hemoglob /56 4 1/19/2010 HgbA1c /22/2010 A1c 7.3 7/23/2010 A1c /24/2010 A1c /24/2010 LDL /80
10 Pt. DOS Test Name Results Vitals 1 9/27/2010 HgbA1c /27/2010 LDL /60 2 7/15/2010 A1c /72 3 8/24/2010 Hemoglob 7.7 9/30/2010 A1c /30/2010 Hemoglob /56 4 1/19/2010 HgbA1c /22/2010 A1c 7.3 7/23/2010 A1c /24/2010 A1c /24/2010 LDL /80
11 Pt. DOS Test Name Results Vitals 1 9/27/2010 HgbA1c /27/2010 LDL /60 2 7/15/2010 A1c /72 3 8/24/2010 Hemoglob 7.7 9/30/2010 A1c /30/2010 Hemoglob /56 4 1/19/2010 HgbA1c /22/2010 A1c 7.3 7/23/2010 A1c /24/2010 A1c /24/2010 LDL /80
12 Gender Diagnosis Time Frame Test Results Age
13 Layering 5. Test Result 4. Diagnosis 3. Gender (both) 2. Age (DOB) 1. Time Frame
14 The Real Systems Intervention? Do not leave the data query parameters to chance, when communicating to IT staff: Be very clear about report parameters List the parameters in the order they need to be requested in the data query (may take some experimentation)
15 Data Recovery The EHR as a Black Hole Structured Data Closing Orders Paper Reports Proper Coding
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21 Lab Test Report
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23 EHR Set Up Security Issues Test Name A1c a1c Hgb A1c Hemoglobin A1c Loinc Codes 10 different codes Required for billing If no Loinc Codes, not picked up on a report
24 The Real Systems Intervention? Look for holes in the raw data Do spot checks on charts that had missing data on the report, to assess why the data is missing (wasn t documented correctly vs. wasn t done) Re-educate staff to correctly document information Re-run reports to check that the documentation is continuing to be done correctly Find the manpower to get the results of focused tests into the structured fields of the EHR Don t allow untrained providers to do system set up
25 Data Reporting Getting a Clean Report Pulling Data by Provider Scrubbing the Data
26 Reporting Data by Provider
27 Pt # DOS A1c LDL BP Prov Jan 3, JM 65.1 Apr 4, JM 65.1 Jul 1, JM 65.1 Aug 26, AG 65.1 Jan 18, AG 65.1 Feb 27, /60 AG Pt # DOS A1c LDL BP Prov Jan 24, MM 91.1 Mar 28, MM 91.1 May 23, MM 91.1 Dec 1, JF 91.1 Jan 5, /76 JF 91.1 Feb 3, /65 MB
28 Scrubbing the Data Analyzing for Compliance to Standards Right care at the right time. A1c: < 7.0 every 6 months, 7.0 every 3 months BP: every visit (last BP reported) - <130/80 LDL-C: < 100 every year 100 or a change in meds every 6 months
29 Quantity through A1c BP LDL Pt. # A1c Date A1c Date A1c Date A1c Date A1c Date S-BP D-BP Date LDL Date LDL Date LDL Date /6/ /11/ /2/ /2/ /2/ /13/ /3/ /6/ /13/ /3/ /22/ /28/ /11/ /30/ /1/ /22/ /28/ /30/ /17/ /31/ /31/ /7/ /31/ /11/ /30/ /11/ /12/ /9/ /18/ /22/ /24/ /13/ /10/ /24/ /15/ /3/ /27/ /19/ /10/ /13/ /27/ /13/ /2/ /5/ /5/ /2/ /18/ /16/ /25/ /10/ /29/ /18/ /25/ /22/ /26/ /30/ /2/ /28/ /1/ /28/ /12/ /27/ /2/ /12/ /11/ /15/ /2/ /6/ /11/ /15/ /8/ /3/ /31/ /12/ /18/ /25/ /19/ /14/ /27/ /18/ /25/ /19/ /30/ /7/ /2/ /2/ /9/ /15/ /27/ /9/ /15/2011
30 Timing A1c BP LDL Pt. # A1c Date A1c Date A1c Date A1c Date A1c Date S-BP D-BP Date LDL Date LDL Date LDL Date /6/ /11/ /2/ /2/ /2/ /13/ /3/ /6/ /13/ /3/ /22/ /28/ /11/ /30/ /1/ /22/ /28/ /30/ /17/ /31/ /31/ /7/ /31/ /11/ /30/ /11/ /12/ /9/ /18/ /22/ /24/ /13/ /10/ /24/ /15/ /3/ /27/ /19/ /10/ /13/ /27/ /13/ /2/ /5/ /5/ /2/ /18/ /16/ /25/ /10/ /29/ /18/ /25/ /22/ /26/ /30/ /2/ /28/ /1/ /28/ /12/ /27/ /2/ /12/ /11/ /15/ /2/ /6/ /11/ /15/ /8/ /3/ /31/ /12/ /18/ /25/ /19/ /14/ /27/ /18/ /25/ /19/ /30/ /7/ /2/ /2/ /9/ /15/ /27/ /9/ /15/2011
31 Results A1c Pt. # A1c Date A1c Date A1c Date A1c Date A1c Date /6/ /11/ /2/ /13/ /3/ /22/ /28/ /11/ /30/ /17/ /31/ /11/ /12/ /18/ /22/ /24/ /13/ /15/ /19/ /10/ /13/ /2/ /5/ /18/ /16/ /25/ /10/ /22/ /26/ /30/ /2/ /28/ /12/ /27/ /11/ /15/ /2/ /8/ /31/ /18/ /25/ /19/ /14/ /30/ /2/ /9/ /15/2011 < > 9.0
32 Provider Meeting
33 The Real Systems Intervention? Reporting data by Provider is more effective than reporting by organization Cleaning up the PCP assignment is a lot of work but worth it Reporting from raw data is time intensive But behaviors will change when data is presented, even if the Providers don t agree with the data
34 The Final Year Changed Data Reporting From time intensive reports and analysis To push of a button results from Meaningful Use Dashboards
35 Meaningful Use Dashboards
36 Meaningful Use Clinical Measures Percentage of patients years old with diabetes (Type I or II) who had: A1c > 9.0% (NQF0059 Diabetes Poor Control) BP < 140/90 (NQF0061 Diabetes BP Management) LDL-C < 100 mg/dl (NQF0064 Diabetes LDL Management)
37 Upside of MU Data Reporting The clinical measures are built into the system and can be easily accessed, as opposed to writing a special report. We can still get numerator & denominator information, to compile organizational benchmarks The health centers had the opportunity to practice reporting MU clinical measures before having to officially do so.
38 Downside of MU Data Reporting The Intergy product did not have 2 of the 3 measures accessible to the clinic staff. I required the clinic staff to pull data from the A1c measure, since that was the only one they had access to I requested that the HCN send the dashboard reports for all three measures. We got very different data in the A1c reports from the health center and the HCN
39 Downside of MU Data Reporting The eclinical Works product had all 3 measures, but the report had almost twice as many patients with diabetes than they had the previous year. The data are reported by provider and not by the patient s PCP. It is duplicative, if a patient saw more than one provider. Provider Date of Service A1c Result Provider A (PCP) May Provider B August Provider A (PCP) November
40 Meaningful Use Dashboards DO NOT USE FOR CLINICAL QUALITY REPORTING
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42 Auto Mobile
43 The Real Systems Intervention? Be flexible. Sometimes things don t go as planned Discovered that the MU Dashboards don t measure patient outcomes data, accurately tell people about this flaw Honest relationships can be as important as providing data Start over again
44 Comments? Questions? Contact information: Karen Sakala, RN BSN, PCMH CCE Clinical Quality Coordinator, NM Primary Care Assoc
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