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Patient Identification The Challenges Facing Community Hospitals Presentation to the Bipartisan Policy Center Collaborative on Health IT and Delivery System.

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Presentation on theme: "Patient Identification The Challenges Facing Community Hospitals Presentation to the Bipartisan Policy Center Collaborative on Health IT and Delivery System."— Presentation transcript:

1 Patient Identification The Challenges Facing Community Hospitals Presentation to the Bipartisan Policy Center Collaborative on Health IT and Delivery System Reform May 16, 2012 Indranil (Neal) Ganguly, CHCIO, FCHIME, FHIMSS Vice President / CIO CentraState Healthcare System Freehold, New Jersey 1

2 2 Patient Identification Background & History A Statement of Fact How Does It Work? Why Should We Care? The Challenge Is There Any Hope? Whats Happening?

3 3 About Me Community Hospital CIO for 13+ years Vice Chair, CIO StateNet, CHIME Member, Policy Steering Committee, CHIME Member, Board of Directors, HIMSS Past Chair, Public Policy Committee, HIMSS Active in Advocacy Efforts

4 4 About CentraState 282 bed Community Medical Center 143 bed Skilled Nursing Facility 82 unit Assisted Living Facility 430 unit Continuous Care Retirement Community 500 Board Certified Physicians Teaching program in Family Medicine

5 5 About CentraState Voluntary Medical Staff Private Health Information Exchange Installed Participate in Regional HIE Successfully Attested for Stage 1 Meaningful Use EMRAM Stage & 2011

6 6 Some History HIPAA requires a unique healthcare identifier for each individual, employer, health plan, and health care provider NCVHS hearings raise privacy concerns for individual patient identifiers Appropriations rider prohibits HHS study / leadership for a nationwide patient identity solution

7 7 Some History Development of National Health Information Network (NHIN) proposed ARRA Stimulus Bill provides incentives for EMR deployment and health information exchange ONC requires State HIT plans to address health information exchanges but does not address the UPIN leaving patient matching as only alternative

8 8 A (Problem) Statement A uniform, standard method of identifying patients does not exist in the United States at this time

9 9 A (Problem) Statement 12 years of data from Harris County, TX 3.4 million patients in hospital districts database 249,213 patients have same first & last name 76,354 patients share both names with 4 others 69,807 pairs share both names and birth date 2,488 patients named Maria Garcia 231 Maria Garcias have the same birth date Source: Houston Chronicle, 4/5/11

10 10 How Does it Work? Patient Matching Methodologies Deterministic – Key data must match exactly Fuzzy Logic – Key data must match established logic Probabilistic – Key data is weighted and scored

11 11 How Does it Work? Patient Matching Methodologies Deterministic Rapid Implementation Simple calculations Relies on accurate and consistent data Probabilistic Complex implementation Sophisticated algorithms Adjusts for minor data errors

12 12 Why Should We Care? Patient Safety Implications Reimbursement Implications Operational Cost Implications Privacy Implications

13 13 Why Should We Care? Patient matching methods are error prone Types of errors include: False positives - linking to the wrong records False negatives - missing the link between a patient and some part of the record Published analyses have found false-negative error rates of about 8 % in medical databases, trending higher in databases with millions of records. Identity Crisis : An Examination of the Costs and Benefits of a Unique Patient Identifier for the U.S. Health Care System, RAND Corporation 2008

14 14 All data elements are not always accurately available Data element capture subject to human error in transcription Matching methodologies can vary widely between organizations HIEs potentially increase spread of errors The Challenge Patient Matching Challenges

15 15 200,000 total patient visits per year Matching accuracy rate approximately 95% + False Negatives = 4% (0.2 hrs) = 1,600 hrs / yr to correct + False Positives = 1% (2.0 hrs) = 2,000 hrs / yr to correct No adverse patient impacts reported to date Risk of negative impact exists in both cases The Challenge A Community Hospitals Experience

16 16 False Negatives are considered the lower risk error but can yield sub-optimal care since clinicians can not take advantage of existing information False Positives are much more difficult to correct and can cause harm by having clinicians rely on inappropriate historical information The Challenge A Community Hospitals Experience

17 17 Private HIE introduces physician office data Error rates not yet known - Physician offices have fewer resources - Errors can rapidly disseminate - Error correction may exceed office capacity to handle Regional HIE further compounds potential issues The Challenge Going Beyond the Hospital

18 18 The Challenge CentraState HIE (P) Regional HIE (P) CentraState (D) MD (D) MD ED (P) OR (D) OP (D) D = Deterministic P = Probabilistic MD (P) MD

19 19 High costs of matching – MPI systems costly High risks of errors – False + / - Lack of patient matching standards makes regional exchange challenging Risk of dissemination of erroneous data and costs for correction Patients and public poorly educated regarding the benefits of positive identification Is There Hope? Challenges for CIOs

20 20 Hospitals have been dealing with the patient identification challenge for decades. 128 hospitals responded to CHIMEs brief survey and the following slides highlight the results Whats Happening? CHIME Surveyed CIOs

21 21 Whats Happening? CHIME Surveyed CIOs

22 22 Whats Happening? CHIME Surveyed CIOs In your experience, approximately what percent of health records have patient data- matching errors?

23 23 Whats Happening? CHIME Surveyed CIOs

24 24 Whats Happening? CHIME Surveyed CIOs Are you involved with any local, regional, or national organization(s), including an HIE, who facilitate interoperability among providers, states and other stakeholders?

25 25 Whats Happening? CHIME Surveyed CIOs

26 Questions? 26


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