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VITL\Blueprint for Health Quality Data, Quality Patients 1.

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Presentation on theme: "VITL\Blueprint for Health Quality Data, Quality Patients 1."— Presentation transcript:

1 VITL\Blueprint for Health Quality Data, Quality Patients 1

2 Reasons for Good Data 2 “Without good data, healthcare systems simply cannot accurately measure and assess performance. …the practice of continuous measurement and public reporting creates a feedback loop that improves patient care.” - National Quality Forum, The ABCs of Measurement

3 Data Quality Facts 3 Quality data is:  Accurate  Complete  Timely  Actionable

4 Data Quality Facts 4  The quality of the data in your source system affects the information sent to the reporting entities.  Quality data can reduce duplicative effort and enhance reporting and outreach.

5 Data Quality Facts 5 Bad data:  Can affect quality of care  Increase costs  Put organizations in liability risk

6 Better Health is the Goal 6  Improve health outcomes by providing the highest quality care  Objectively see where care deviates from clinicians’ intentions  Collect data at the point of care in the EMR  Feed data into statewide registry so practices can benchmark against peers  Identify who is doing well so organizations can share best practices

7 Better Health is the Goal 7 How do we know that care is improving, and that health is getting better? We need to measure it. Measurement isn’t the goal; better health is. - Dr. Kevin Larsen, HHS

8 Registries and External Reporting The Health Information Technology structure in Vermont is designed to help practices with reports for: 8  The Vermont Blueprint for Health  Meaningful Use Measurement/Reporting  ACO Patient Records  Uniform Data System  Physician Quality Reporting System  National Committee for Quality Assurance  The Birth, Death, & Immunization Registry  Public Health Reporting

9 Data flow from Practice to Registry 9 Practice enters patient data into EHR/PM System VITL - Vermont Health Information Exchange (VHIE) Medicity ADT CCD Vermont Blueprint for Health ADT CCD Practice views reports in DocSite Covisint Vermont Department of Health VXU* VXU*: Future Data Flow VXU*

10 Data flow from Practice to Registry 10 Practice enters patient data into EHR/PM System ADT CCD  Accurate  Up-to-date  Complete  Highest Quality

11 Benefits of Clean Data 11  IMPROVE PATIENT CARE  Expedite clinical decision making  Prevent duplication of patient records  Achieve Meaningful Use  Enhance efficiency  Reduce costs  Heighten accuracy of reports  Increase amount of information transferrable to other systems  Ensure accuracy within patient charts  Improve outreach  Enhance the use of patient portal and consumer access

12 Common Data Quality Challenges 12 Provider Panel   Patient Attribution     Active/Inactive Status   Deceased Management   Clinical Data Issues   Patient Matching

13 Provider Panel Challenges 13 PROBLEMREMEDIATIONOUTCOMES Inactive providers are still ACTIVE in source system. Downstream systems do not know that providers are not inactive or that new providers have been assigned. NPI is not exported with Provider information, causing incomplete data. Fake providers are created for Out-of-Town patients and then exported cause confusion. Workflow or system does not allow the practice to deactivate a provider or add a new provider. Review panel and remove inactive providers. Notify downstream systems of new or inactive providers. Verify with VITL/vendor that NPI is exported in interface. Determine how a fake provider is affecting the downstream system. Replace it with a standardized provider type. Workflow or system does not allow the practice to deactivate a provider or add a new provider. Ensures that practices are paid for active providers, and do not receive payment for inactive providers. Allows there to be a synchronous accounting of providers. Facilitate the transition of patients to the correct provider, ensuring that patient panel reports are accurate and actionable within the practice.

14 Patient Attribution Challenges 14 Patient Attribution The assignment of a patient to a specific provider within a practice.

15 Patient Attribution Challenges 15 Patients no longer actively receiving care are not marked Inactive Vacationers only seen once are marked Active Patients are not assigned to a PCP Patients are attributed to providers who are no longer with the practice System has attribution besides PCP, such as Other Responsible Provider

16 Patient Attribution Challenges 16 Mark patients who have not been seen for 3 years Inactive Develop a system to mark patients who are temporary as Inactive Assign all patients a PCP and ensure that field is included in exports Ensure that the Patient and Provider panels in the EMR are correct Ensure export fields and mapping in the interface are correct

17 Patient Attribution Challenges 17 Accurate panel management reports and quality improvement measurements for the practices Properly assigned patients and providers in patient attributions Practices receive entire and correct payments

18 Active and Inactive Patient Status 18 Different rules apply to Active patient status. Sites that provide both primary and specialty care may have patients who are Inactive within a practice and Active in the organization. A patient is marked Inactive in the source system, but the EHR does not transfer an inactive flag. Understand requirements in both source and downstream systems for Activating / Inactivating patients and synchronize. Check with your organization regarding how to Inactivate a patient in one practice without doing so in other practices. Ensure that status flag on your system is passing onto the next system.

19 Deceased Patient Status 19 Practices are not aware of patient’s death. The Vermont death registry can supply a monthly list of people who die. Covisint can supply this information to practices in an Excel spreadsheet to be sorted by location. Providers are not pulling panels containing deceased patients, thus avoiding the very unfortunate situation where they are contacting the families of the deceased. Many practices rely on obituaries for death information. Multi-practice sites need to mark deceased at parent source system or the data may be over- written in the organization. Exports do not always support deceased indicator. Work with VITL to determine if your export supports a deceased indicator. If not supported, report to down- stream systems. Also essential for proper measurement, evaluation, and payment purposes.

20 Clinical Data Challenges 20 EHR, labs, and other external sources use different codes. The EHR treats discrete numeric fields as text. Ensures that reports and exported information is actionable by providers. Some free text, delimiters, and combination fields in are not exportable. Customized EHR fields are producing non-standard results. Auto-fill used where more or different detail is needed. Use discrete fields or drop-down menus whenever possible. Free text fields do not capture discreet data. Ensures that the best quality care can be provided. Limit the use of customized fields. Customized data often do not pass in exports. Ensure that numeric values are used in discrete fields where calculations may be required. Essential for analysis and evaluation of the program and practice.

21 Patient Matching Challenges 21 OUTCOMES REMEDIATION PROBLEM Partial data is used to create a patient record Middle initial inconsistently provided Dummy names cause false positive matches Cell phone and home phone interchanged Invalid city- state-zip combination used Social Security Number not always used Understand patient match- ing in multiple systems Establish a process to ensure proper data collection Establish a policy of intermediate naming Establish a process for proper phone # collection Use drop-down menus for location information Force numeric values where calculations may be required Informed, impactful decisions at the point of care Panel management reports have all information for patient outreach Patient’s comprehensive medical history and set of conditions

22 Data Quality Sprints 22  Data quality sprints are used to clean up data within the host EMR or in the process of transmitting data to the Vermont Health Information Exchange or statewide registry.  All involved parties commit to working together and attending weekly meetings to review progress.  Participation from the practice or health system, VITL, Covisint/DocSite are essential to promote real time problem solving and immediate action.  The end result is better quality data in the EMR and registry, which leads to accurate actionable reports coming from either system.

23 Who Can Help? 23

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