Data Quality in Healthcare EMRs vs. Population Health

Vermont Blueprint for Health – Sprint Data Quality Program Vermont Blueprint – Sprint Program 13 Health Service Areas 147 Practices 21 different EMRs 325k managed patients Chronic Care Information Registry All Payer Claims Database Health Information Exchange Support fot State Wide Pop Health Program – Community Health Teams – All Payer Model Intensive Team Based Approach was used to attack the problem Development of the Sprint Data Quality Program based on failures seen in the capabilities of data aggregation and reliable reporting on core measure sets

Overview of Data Quality Program Population Health Management vs. Episodic Care Sources & Types of Healthcare Information Issues and Causes of Data Anomalies Practice Management and ADT Systems Issues EMR Clinical Data Issues Data Transport Issues Claims Data Issues Remediation of Problem Data Data Blocking Problem Keeping Systems and Data Healthy What we are going to talk about today

Population Health Management vs. Episodic Care Episodic Care Teams Utilize a Single Patient’s Records Regularly Permits Global Anomalies in Data to Occur Population Health Teams Utilize the Entire Population’s Records Inpatient and Outpatient EHR data ADT Data External Lab, Pharmacy and Prescription Feeds Claims Data Exposes Anomalies in Global Data Core Measure Reporting in an Fee For Value Environment Anomalies – Developed due to single record use – issues are not readily seen as each record is viewed individually. these anomalies are exposed when EMR data is aggerated in a Pop health or data analytics system Core measure reporting in an FFV program can be greatly affected by the hidden data issues

Sources & Types of Healthcare Data Practice Management Systems (PM) & Admissions, Discharge & Transfer Data (ADT) Patient Demographics, Identifiers & Insurance Physician Demographics and Identifiers Provider/Patient & Site Attribution Patient Status Information Hospital ADT – Diagnosis, Results, Procedures & Discharge Summary Electronic Medical Records Systems Structured Clinical Data Coded vs. Uncoded Free Text Data Demographics are traditionally dirty – Records Matching is a problem due to missing Demographic components from various components Duplicates cause issues Different Identifier schemas create a one to many matching challenge The same occurs in Provider demographics

PM & ADT Patient Demographics & Identifiers Inaccurate/Misspelled Names Outdated Contact Information Multiple Instances of the Same Person Patient Identifiers Missing or Multiple Medical Record Number (MRN) Erroneous MRNs (Third Party Systems) Utilization of Modifiers Data Anomalies from Merged Systems Middle name initial missing causes issues with MPI matching EMPI Enterprise M Patient Index Enterprise Master Provider Index

PM & ADT Patient Panel & Attribution Active and Inactive Patients Deceased Patients Special Patient Types Dental Only Special Clinic (HIV) CFR 42 Part II Data Insurance Race/Ethnicity/Language

PM & ADT Patient Panel

PM & ADT – Provider Demographics & Identifiers Provider Data Outdated Provider List Patient Attribution Patients Attributed to Non-existing Providers PCP Assignments – Other Provider Types Site Attribution Mismatched System Nomenclatures Provider Identifiers Missing NPI Numbers (Interface Issue) Multiple NPI Numbers

Clinical Data Types & Issues Structured Data Despaired System Nomenclatures CPT, ICD-9/10, SNOMED, CVX Un-coded, Outdated Codes Updated Code Sets Free Text Data Human Nomenclatures Transport of Meaningful Data Continuity of Care Documents Machine Readable vs. Human Readable Flat File Interfaces HL7/FHIR Labs - Pharmacy Mapping and Translation Creating Meaningful Population HM Data use of different code sets - CPT vs CVX for Immunization; outdated loinc or CVX codes. Another issue is the lab compendiums from the site to the hospital labs and back to the EMR must stay in sync

CCD Coding Issues

CCD Human Readable vs. Machine Readable Human Readable Side – Observations – BMI and Height Human Readable Side Has the Data – Good for viewing in a PDF as a document attached to a patient record

CCD Human Readable vs. Machine Readable Machine Readable Side – BMI Here is the machine readable side of the same CCD – The BMI code is not known – This data will fall on the floor and not be included in the pop health system and may or may not report as an error

CCD Human Readable vs. Machine Readable Machine Readable Side – Height Here we note that in the same message the height is included. – When we examined this entire CCD we found 15 such issues pertaining to important clinical data

Data Remediation Post Go-Live Remediation Significant Level of Effort Intensive Resources Multi-Level Problems Pre Go-Live Remediation Remarkably Easier to Accomplish Establishment of Clean ADT Data Cleaner Mapping of Clinical Data Working with the EMR Vendor and HIE/IT Team Catch it early – once data is committed to down stream systems – pop health or other EMRs and data analytics systems – it becomes much harder to deal with

Keeping Data Healthy Monthly Review of Provider List Active/Inactive Patients Deceased Patients Notification to Receiving Parties System Upgrades Changes in Data Collected Integration of PHRs & Portals Maintaining Best Practices Data Entry Policies and Support at the Practice Level CCD Coding Review Regular Review of Data Quality It is important to conduct system reviews quarterly – as well as after version upgrades of the EMR

Sprint Program Outcomes Data Quality Issues Reduced to <5% of All Data Collected Corrected 4 Different EMR – CCD Global Issues Developed a Reliable Work Around for Certain EMR Systems that Blocked Data Developed a Direct Path for Practices that Could/Would Not Connect Thru the HIE

Data Blocking Information blocking occurs when persons or entities knowingly and unreasonably interfere with the exchange or use of electronic health information. Interference. Information blocking requires some act or course of conduct that interferes with the ability of authorized persons or entities to access, exchange, or use electronic health information. This interference can take many forms, from express policies that prohibit sharing information to more subtle business, technical, or organizational practices that make doing so more costly or difficult. Knowledge. The decision to engage in information blocking must be made knowingly. No Reasonable Justification. Accusations of information blocking are serious and should be reserved for conduct that is objectively unreasonable in light of public policy. Public policy must be balanced to advance important interests, including furthering the availability of electronic health information for authorized and important purposes. Interesting Study by Julia Alder of the University of Michigan A New Look at Economic Barriers to Interoperability Julia Adler-Milstein, Phd University of Michigan

Information Blocking Survey: Frequency A New Look at Economic Barriers to Interoperability Julia Adler-Milstein, Phd University of Michigan

Frequency of Information Blocking Behaviors: EHR Vendors

Frequency of Information Blocking Behaviors: Hospitals/Health Systems