EMR and Public Health Ninad Mishra MD, MS 07/09/2009

Anatomy of the Presentation (1) EHR: functions, definitions, potential EHR: functions, definitions, potential Current state: adoption, stakeholders Current state: adoption, stakeholders Future state: drivers, barriers Future state: drivers, barriers Public health and EHR interoperability Public health and EHR interoperability An immunization example Preventive assessment & quality Our work: Our work: Obesity and co-morbidity detection from medical discharge summaries Disease prevention and automatic classification of medical records

EMR Vs EHR EMR (Electronic Medical Record): Electronic record with full interoperability within an enterprise (hospital, clinic, practice) EHR (Electronic Health Record): Generic term applied to electronic patient care systems Original Source: an article entitled EHR vs. CPR vs. EMR in the May 2003 issue of - Healthcare InformaticsEHR vs. CPR vs. EMR

EHR Functions Health info & data Health info & data Result management Result management Order management Order management Decision support Decision support Electronic communication Patient support Administrative reporting Population health & reporting IOM Report: Key Capabilities of EHR system, July 2003

Only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records Only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records Those who use electronic records are generally satisfied with the systems and believe that they improve the quality of care that patients receive Those who use electronic records are generally satisfied with the systems and believe that they improve the quality of care that patients receive Source: Jha & DesRoches N ENGL J MED 359;1 Status of EHR Adoption

Setting2006(%)2007(%)2008(%)2009(%) PO (basic) PO ( full) 344 Hospitals (basic) NANA8 Hospitals (full) NANA2 Source: CDC National Ambulatory Medical Survey (NAMC) of ~2700 physicians RR 62% AHA~3037 hospitals; RR 63%

EHR componentsBasicFull Health Info & Data** Order Entry Medication Orders ** Lab Orders * Radiology orders * Rx sent electronically * Orders sent electronically * Results Management View lab results ** View imaging results ** Images returned * Clinical Decision support* Public Health

Effect of Adoption of EHR Systems DesRoches CM et al. N Engl J Med 2008;359:50-60

Barriers to Adoption of EHR Systems COST Financial ROI Privacy and security of electronic health information Clinical workflow disruption

Bottom Line EHRs are not bring used the way IOM had hoped EHRs are not bring used the way IOM had hoped Physician’s report limited availability of key functions (order entry, clinical decision support) Physician’s report limited use of most of the functions Many institutions with an EHR cannot produce patients list (registry function) Public health/population health related measures are lacking

Executive Sponsorship “Within ten years, every American must have a personal electronic medical record….The federal government has got to take the lead..” Pres. GWB, April 26, 2004, AACC, Minneapolis “Within ten years, every American must have a personal electronic medical record….The federal government has got to take the lead..” Pres. GWB, April 26, 2004, AACC, Minneapolis “To improve the quality of our health care while lowering its cost, we will make the immediate investments necessary to ensure that within five years, all of America’s medical records are computerized…” Pres-Elect Barack Obama, Jan 8, 2009 “To improve the quality of our health care while lowering its cost, we will make the immediate investments necessary to ensure that within five years, all of America’s medical records are computerized…” Pres-Elect Barack Obama, Jan 8, 2009

The in IT: Recovery Act of 2009 The Investment in Health IT: Recovery Act of 2009 $ 19 billion over 10 years $ 19 billion over 10 years Promote the adoption and use of health information technology and electronic health records Promote the adoption and use of health information technology and electronic health records $17 billion of that $17 billion of that Financial incentives for physicians and hospitals Financial incentives for physicians and hospitals Early adopters (individual physicians) can collect over $44,000 over the 5 year period starting 2011 Early adopters (individual physicians) can collect over $44,000 over the 5 year period starting 2011

Other Health IT Measures $2 billion for ONC to put HIT support systems in place $2 billion for ONC to put HIT support systems in place $300 million to support the development of health information exchange capabilities $300 million to support the development of health information exchange capabilities Grants to create regional technology centers to help physicians and hospitals install EHRs Grants to create regional technology centers to help physicians and hospitals install EHRs Funds to train a workforce Funds to train a workforce Grants and loans to states to assist with adoption and interoperability Grants and loans to states to assist with adoption and interoperability

ONCHIT ONCHIT stands for Office of the National Coordinator of Health Information Technology ONCHIT stands for Office of the National Coordinator of Health Information Technology Located within the Department of Health and Human Services Located within the Department of Health and Human Services Currently exists under executive authority but the new Law expands its roles Currently exists under executive authority but the new Law expands its roles 2 committees to advise NCHIT 2 committees to advise NCHIT

ONCHIT Health Information Policy Committee Health Information Standards Committee NCHIT

ONCHIT New Coordinator: David Blumenthal, MD New Coordinator: David Blumenthal, MD Dr. Farzad Motashari Dr. Farzad Motashari New Focus New Focus Meaningful use of EHRs by 2011 Meaningful use of EHRs by 2011 Primary care providers are the first target Primary care providers are the first target Regional health information technology extension enters as the driver for dissemination of EHR Regional health information technology extension enters as the driver for dissemination of EHR A policy-based approach A policy-based approach Modified Source: Dr. Leslie Lenert National Center for Public Health Informatics

Meaningful use of Health IT Key desired policy outcomes: Key desired policy outcomes: efficiency, patient safety, care coordination Drivers: Medicare and Medicaid incentive payments Being formulated: “measurement of key public health conditions, measuring health care efficiency, and measuring the avoidance of certain adverse events.”

Certified EMRs The Certification Commission for Healthcare Information Technology (CCHIT®) is a private, 501(c)3 nonprofit organization The Certification Commission for Healthcare Information Technology (CCHIT®) is a private, 501(c)3 nonprofit organization CCHIT recommendations need to be certified by National Institute of Standards and Technology (NIST) CCHIT recommendations need to be certified by National Institute of Standards and Technology (NIST)

Opportunity for the Public Health It seems we would be reaching an EMR adoption tipping point It seems we would be reaching an EMR adoption tipping point It would be a good opportunity for public health to engage with all the other stakeholders in the process It would be a good opportunity for public health to engage with all the other stakeholders in the process ‘Meaningful use’ ‘Meaningful use’ ‘Certification criteria’ ‘Certification criteria’ Using EMRs for population health Using EMRs for population health

Who Has What? Clinical Care  Patients  Resources for Dx, Rx, Prev.  Personnel (MD’s,RN’s, educators)  Facilities (labs, OR’s, etc.)  Programs (control measures, screening, education)  Outcomes Public Health  Cases  Resources for Dx, Rx, Prev.  Personnel (MD’s,RN’s, epidemiologists, educators)  Facilities (labs)  Programs (Rx recommendations, control measures, screening, education)  Outcomes Modified source : Jeff Perry’s presentation

EHR-PH Data Exchange Potential Registry data (immunization registry) Registry data (immunization registry) Reportable disease surveillance data Reportable disease surveillance data Case management data Case management data Vital statistics data Vital statistics data Acute event detection data Acute event detection data Chronic disease and injury surveillance data Chronic disease and injury surveillance data

Considerations for EHR-Based Population Health Applications Data has to be defined and captured in uniform ways Data capture has to be simple and integrated into the workflow System must be modifiable as measures and recommendations change over time Population level analysis, and algorithms for measures require more complex analysis or queries Source: Alliance of Chicago: Community Health Services

National Objective for Registries Increase to 95% the proportion of children aged <6 years who participate in fully operational immunization registries (Healthy People 2010, objective 14.26)

US Participation in IIS – 2007 Group Percentage Children <6 (2+ doses) 71% Children (2+ doses)64% Adults >19 (1+ dose)20% Public provider sites73% Private provider sites48% Source: Alan Hinman, Public Health Informatics Institute

Barriers to IIS Cost and/or time of data entry and retrieval Practices are too busy to consider a new procedure and implement change Concerns about privacy, confidentiality, and HIPAA Provider does not see any value to their practice of the new information they can get from the registry. Coordination required between clinical, administrative and information systems departments Source: AIRA/CDC report “Turning barriers into opportunities” Dec 2005

Public Health Programs <1 Year Old

Integration Status of Specific Programs (N=31) Source: Alan R. Hinman, MD, MPH

Source: PHDSC EHR-PH Interoperability System Prototype

Source: PHDSC

EHR-PH Interoperability System Prototype

An Example from Indiana Network of Patient Care An Example from Indiana Network of Patient Care

PH-EHR Integration Immunization Registry Electronic Medical Record System Patient ID: 123LMNOP Name: Jane Doe DOB: 01/01/04 SSN: N/A Address: 555 Johnson Road City: Indianapolis State: Indiana ZIP: Patient ID: 6789XYZ Name: Jane Ellen Doe DOB: 01/01/04 SSN: Address: 555 Johnson Road City: Indianapolis State: Indiana ZIP: Global Patient Index Concept Dictionary Global ID:45678 Name: Jane Ellen Doe Lots of Demographics.. MRF1 ID: OU81247 MRF2 ID: PH MRF ID: 123LMNOP MRF3 ID:6789XYZ DTaP Dose Count: HIB Dose Count: IPV Dose Count: VZV Dose Count: MMR Dose Count: HepB Dose Count: Jane Doe’s Immunizations: 3/1/04DipTetaPur 3/1/04HemInfB 3/1/04PolioVir 3/1/04HepaB Jane Ellen Doe’s Shots: 5/1/04DTaP Imm 5/1/04HIB Imm 5/1/04IPV Imm 7/9/04DTaP Imm 7/9/04IPV Imm

Population Health, Preventive Assessment and Informatics

Population Health “The health outcomes of a group of individuals, including the distribution of such outcomes within the group.” 1 “The health outcomes of a group of individuals, including the distribution of such outcomes within the group.” 1 Is at the cross section of medicine and public health Is at the cross section of medicine and public health 1 1 Kindig D, Stoddart G. What is population health? American Journal of Public Health 2003 Mar;93(3): Retrieved What is population health?

Population Health Disease management Disease management Preventive health Preventive health Cancer screenings Cancer screenings Childhood immunization gap Childhood immunization gap Quality improvement Quality improvement Aggregate population data exchange/ statistical reporting Aggregate population data exchange/ statistical reporting Data mining and predictive modeling Data mining and predictive modeling

Population Health

Data Sources Patient management systems Patient management systems EHRs EHRs RHIOs RHIOs Labs Labs Registries Registries Pharmacies Pharmacies

What is needed Develop algorithms to appropriately identify cases Billing data is usually not enough – consider addition of free text data, medication data, medical summary abstraction etc. Develop statistical measures for aggregated summary and analysis for public health use Modified Source: Arndt et al (WREN)

Example: Diabetes Measurement Set (foot exam) Measure: Percentage of patients who received at least one complete foot exam (visual inspection, sensory exam with monofilament, and pulse exam)  Numerator = patients who received at least one complete foot exam (visual inspection, sensory exam with monofilament, and pulse exam)  Denominator = All patients with diabetes years of age Source: Alliance of Chicago: Community Health Services

Technical Specifications Denominator All patients with diabetes years of age All patients with diabetes years of age Codes to identify patients with diabetes include: Codes to identify patients with diabetes include: ICD-9-CM codes: 250, 357.2, 362.0, , 648.0) (DRGs) 294, 205 ICD-9-CM codes: 250, 357.2, 362.0, , 648.0) (DRGs) 294, 205 Prescriptions to identify patients with diabetes include: Prescriptions to identify patients with diabetes include: Insulin prescriptions (drug list is available) and oral hypoglycemic/ antihyperglycemics prescriptions (drug list is available) Insulin prescriptions (drug list is available) and oral hypoglycemic/ antihyperglycemics prescriptions (drug list is available)

Data Analysis Best Practices (Example: Diabetes patients with A1c >7) Analysis Type ExampleUtility 1 – Case series 60% in clinic have A1c >7 Lowest 2 – Simple comparison Clinic rate of 60% is higher than statewide rate of 50% Low 3 – Comparison + Test Medium 4 – Adjusted comparison + Test (ie, adjust for principal determinant) Age adjusted clinic rate of 60% is significantly higher Higher 5 – Multivariate model + Test (ie, adjust for all important risk factors / determinants) Clinic rate of 60% adjusted for age, gender, race, and insurance status is significantly higher Highest Source: Wisconsin Research Education Network

Benefits Providers (physicians): patient alerts, decision support, work flow assistance, evidence based practice Providers (physicians): patient alerts, decision support, work flow assistance, evidence based practice Management: case management, cost control, quality control, outreach Management: case management, cost control, quality control, outreach Patients: quality of care Patients: quality of care Public health: reduce disparities, increase quality, better research data Public health: reduce disparities, increase quality, better research data

Our Two Cents We are working at small POCs to establish methods for data capture and algorithm development We are working at small POCs to establish methods for data capture and algorithm development We have been primarily focused on unstructured data analysis but combination of structured with unstructured is the goal We have been primarily focused on unstructured data analysis but combination of structured with unstructured is the goal Two examples: Two examples: I2b2 obesity challenge I2b2 obesity challenge Family health history analysis Family health history analysis

I2b2 Obesity Challenge NIH-funded National Center for biomedical computing based at Partners HealthCare System NIH-funded National Center for biomedical computing based at Partners HealthCare System i2b2 issues ‘challenges’ to correctly classify health records based on conditions and co-morbidities and invites various institutions/teams to compete i2b2 issues ‘challenges’ to correctly classify health records based on conditions and co-morbidities and invites various institutions/teams to compete

Results

JAMIA: Jul-Aug 2009

Next: Family Health History & Screening Previous work (i2b2): detect occurrences of specific morbidities in medical discharge summaries Previous work (i2b2): detect occurrences of specific morbidities in medical discharge summaries Future directions: Future directions: Extract other information Extract other information Experiencer (sic): Who is experiencing the condition (patient or other family member)? Experiencer (sic): Who is experiencing the condition (patient or other family member)? Temporality Temporality Other co-information: lab results, screening test results, medications, etc. Other co-information: lab results, screening test results, medications, etc. Apply rules to extracted information to make recommendations Apply rules to extracted information to make recommendations

Data capture and sharing Advanced clinical processes Improved outcomes Bending the Curve : Achieving Meaningful Use of Health Data “Phased-in series of improved clinical data capture supporting more rigorous and robust quality measurement and improvement.” Modified after: Connecting for Health, Markle Foundation “Achieving the Health IT Objectives of the American Recovery and Reinvestment Act” April 2009 Meaningful Use Workgroup Presentation : Paul Tang & Farzad Mostashari Better preventive care assessment and public health functions