The Last Mile: Using Fax Machines to Exchange Data between Clinicians and Public Health
Author(s) -
Stephen M. Downs,
Vibha Anand,
Meena Sheley,
Shaun J. Grannis
Publication year - 2011
Publication title -
online journal of public health informatics
Language(s) - English
Resource type - Journals
ISSN - 1947-2579
DOI - 10.5210/ojphi.v3i3.3892
Subject(s) - health information exchange , clinical decision support system , unique identifier , identifier , computer science , public health , health care , public health surveillance , medicine , medical emergency , decision support system , internet privacy , computer security , artificial intelligence , health information , nursing , programming language , economics , economic growth
There is overlap in a wide range of activities to support both public health and clinical care. Examples include immunization registries (IR), newborn screening (NBS), disease reporting, lead screening programs, and more. Health information exchanges create an opportunity to share data between the clinical and public health environments, providing decision support to clinicians and surveillance and tracking information to public health. We developed mechanisms to support two-way communication between clinicians in the Indiana Health information Exchange (IHIE) and the Indiana State Department of Health (ISDH). This paper describes challenges we faced and design decisions made to overcome them. We developed systems to help clinicians communicate with the ISDH IR and with the NBS program. Challenges included (1) a minority of clinicians who use electronic health records (EHR), (2) lack of universal patient identifiers, (3) identifying physicians responsible for newborns, and (4) designing around complex security policies and firewalls. To communicate electronically with clinicians without EHRs, we utilize their fax machines. Our rule-based decision support system generates tailored forms that are automatically faxed to clinicians. The forms include coded input fields that capture data for automatic transfer into the IHIE when they are faxed back. Because the same individuals have different identifiers, and newborns' names change, it is challenging to match patients across systems. We use a stochastic matching algorithm to link records. We scan electronic clinical messages (HL7 format) coming into IHIE to find clinicians responsible for newborns. We have designed an architecture to link IHIE, ISDH, and our systems.
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