Appendix: Content Summaries of Selected Best Papers for the IMIA Yearbook 2018, Section
Health Information Management
Roehrs A, da Costa CA, da Rosa Righi R
OmniPHR: A distributed architecture model to integrate personal health records
J Biomed Inform 2017 Jul;71:70-81
The authors discuss a distributed architecture model, called OmniPHR, to integrate
personal health records (PHRs). The authors’ research goal is to answer how to have
a single view of a PHR that is up-to-date and interoperable for patients and providers.
The proposed model focuses on a distributed approach where patients can maintain a
unified view of their health history, from any device anywhere. The approach recognizes
current challenges since patients’ health data are collected throughout their lives,
across the care continuum, and come from multiple and diverse sources, including clinicians,
laboratories, clinics or hospitals, and data from sensors that monitor the patients’
health. The article summarizes the main concepts, challenges, and models that support
the authors’ proposal; explains the most significant related work; presents the foundational
technologies for model development; details the architecture model; provides the evaluation
and methodology of the study; summarizes the results and discusses the impacts, limitations,
and future directions; and presents the conclusions of their work.
Setting the stage for their proposal of a computer architecture model for PHRs based
on a distributed P2P (peer-to-peer) network system, the authors apply the International
Organization for Standardization’s (ISO) Technical Committee (TC) 14639 (Health informatics
– Capacity-based eHealth architecture roadmap – Part 2: Architectural components and
maturity model) definitions for EHRs and PHRs. The authors include discussions about
the limitations and the challenges of EHRs and PHRs. A summary of other models described
in the literature is also included. The authors discuss the technologies that complete
their proposed solution and how they are interconnected with the proposed model. These
technologies include: Blockchain, Routing Overlay, openEHR standard, Chord algorithm,
and Publish-Subscribe systems. Following the discussion of the model and technologies,
the authors provide an additional description of the model’s purpose (to allow a unified
view of health records which are distributed in several health organizations) and
they address the challenges regarding a distributed architecture that is scalable,
elastic, and interoperable.
The next section of the paper focuses on the modules and components of OmniPHR design
and includes descriptions of each. The authors describe the use of the modeling and
profiling methodology to evaluate mobile applications. Their goal is to describe and
evaluate scenarios of use where OmniPHR can be applied. The authors also describe
and depict the mathematical systems analysis that was undertaken and then provide
an extensive discussion of the findings and results. Limitations of the model are
described and the authors identify challenges and opportunities. For example, one
key challenge for the model is the need to verify the identity and authenticity of
the data informants (sources). The need to assure data validity, chain of trust, and
security and privacy are also discussed and the need for further testing for security
and privacy is noted.
Klein DM, Pham K, Samy L, Bluth A,Nazi KM, Witry M, Klutts JS, Grant KM, Gundlapalli
AV, Kochersberger G, Pfeiffer L, Romero S, Vetter B, Turvey CL
The veteran-initiated electronic care coordination: a multisite initiative to promote
and evaluate consumer-mediated health information exchange
Telemed J E Health 2017 Apr;23(4):264-27
This pilot study examines the potential of consumer-mediated health information exchange,
which gives patients access and control of their health data for promoting continuity
of care. Although veterans receive most of their care at the Veterans’ Affairs (VA)
facilities, many veterans, referred to as ‘dual use’, receive some care outside the
VA. The VA Office of Rural Health and the Health and Human Services Office of the
National Coordinator for Health IT partnered to promote the use of My HealtheVet’s
Blue Button capability to facilitate transfer of VA health information to non-VA providers
to improve care coordination for rural dual-use veterans. The VA launched the Blue
Button feature in My HealtheVet, the VA’s patient portal, in August 2010. In 2013,
a Continuity of Care Document (CCD) in standardized format became available. The VA
CCD includes essential information (allergies, medications, diagnoses, immunizations,
recent lab results, vital signs, history of procedures, and encounters) from the VA’s
electronic health record (EHR) that is accessible via the Blue Button.
In this study, VA facilities and rural community healthcare organizations collaborated
to develop optimal processes for information exchange. The researchers also engaged
and trained veterans in health information sharing (i.e., how to use the Blue Button).
The project developed methods for evaluating patient and provider impact of this sharing.
The goals of the project were to: (1) train dual-use rural veterans to use the VA’s
My HealtheVet Blue Button capabilities to promote consumer-mediated HIE of their VA
CCD with their non-VA care providers, and (2) evaluate if the availability of VA information
at a community clinical encounter impacted the care received.
The authors provided details about how these processes were undertaken and accomplished.
Approaches and methods available for veterans to share data with non-VA providers
varied and veterans were trained in these processes. Veterans were asked to complete
a brief questionnaire after training to evaluate their experiences. Non-VA (“community”)
providers were also asked to complete a questionnaire to help assess provider satisfaction
with the CCD and whether the provider believed the CCD had an impact on the care provided.
Detailed analyses were conducted in the following areas: patient characteristics and
perceptions of provider communication; patient training evaluation; and data sharing
at community non-VA provider visits. Study limitations (such as site variation for
patient engagement/training; lack of a comparison group; and potential for participant
selection bias (veterans’ level of interest in health and technology) were described.
The authors conclude that the pilot demonstrated the feasibility and value of patient
access to a standard CCD to facilitate information sharing between VA and non- VA
providers. With brief training, veterans were able to generate their CCD in My HealtheVet,
share it with non-VA providers, and benefit from improved communication about medications
and reduced laboratory test duplication. Thus, the authors found that there is patient
and provider support for consumer-mediated HIE and they noted that this type of HIE
requires outreach and targeted education.
Boockvar KS, Ho W, Pruskowski J, DiPalo KE, Wong JJ, Patel J, Nebeker JR, Kaushal
R, Hung W
Effect of health information exchange on recognition of medication discrepancies is
interrupted when data charges are introduced: results of a cluster-randomized controlled
trial
J Am Med Inform Assoc 2017 Nov 1;24(6):1095-101
The authors explored the effect of health information exchange (HIE) on medication
prescribing for hospital inpatients in a Veterans Administration hospital in a cluster-randomized
controlled trial and examined the prescribing effect of availability of information
from a large pharmacy insurance plan in a natural experiment. They recognized that
a key step in medication reconciliation is information-gathering from various sources
such as patients, family members, providers’ offices, health care facilities, pharmacies,
and prescription coverage plans and postulated that [regional] HIEs could improve
medication safety by facilitating reconciliation of medication information from multiple
sources at the time of patient care. The researchers hypothesized that HIE would raise
the impact of medication reconciliation for hospitalized veterans who utilize VA and
non-VA services on discrepancies between preadmission and inpatient medication regimens
(primary outcome) and reduction of ADEs (secondary outcome). Patients were assigned
to intervention or control groups according to the hospital unit(s) to which they
were admitted.
The study describes the methodology, protocols, and quality controls in detail. For
patients assigned to the intervention group (HIE-enhanced medication reconciliation),
an intervention pharmacist conducted HIE-enhanced medication reconciliation, following
a structured protocol. For patients assigned to usual care, the intervention pharmacist
performed the structured medication reconciliation protocol but without access to
the information available from HIE. The study defined medication discrepancies as
differences between a patient’s prehospital medication list and the medications received
in the hospital. The discrepancies were initially identified and recorded by the unblinded
intervention pharmacist at the time of admission medication reconciliation. The unit
of observation was hospitalization episode. For each study group, descriptive statistics
were used to describe patient and hospitalization characteristics, time from hospital
admission to medication reconciliation, and house staff rectification of medication
discrepancies.
Results indicated that there were no significant differences between intervention
and control groups in baseline characteristics. The mean time from hospital admission
to medication reconciliation in both intervention and control groups was the same.
The researchers also found that there were no differences between intervention and
control groups in numbers of verbal or co-signature alerts that the intervention pharmacist
provided to physicians. However, patients who received HIE-enhanced medication reconciliation
with pharmacy insurance data available had greater risk-weighted medication discrepancies
identified than those who received usual care. There were no differences in ADEs between
those assigned to HIE-enhanced medication reconciliation and those assigned to usual
care, or between those who received HIE-enhanced medication reconciliation with pharmacy
insurance plan data available and those who received usual care.
Study limitations were described and include: low house staff responsiveness to medication
discrepancy information; delayed mean time from hospital admission to the intervention
pharmacist’s medication reconciliation; and low level of medication information in
the HIE. The authors noted a strength of their study was that they tested the effect
of HIE in potentially high-impact circumstances (medication prescribing at the time
of hospital admission) and did not depend on voluntary HIE access by the user (the
intervention pharmacist was obligated to access HIE for all intervention patients).
The authors conclude that HIE may improve outcomes of medication reconciliation. However,
the authors raise concerns related to potentially harmful consequences of charging
for access to information (in this case payment data) and related to information blocking
practices.
Downing NL, Adler-Milstein J, Palma JP, Lane S, Eisenberg M, Sharp C; Northern California
HIE Collaborative, Longhurst CA
Health information exchange policies of 11 diverse health systems and the associated
impact on volume of exchange
J Am Med Inform Assoc 2017 Jan;24(1):113-22
Focusing on health information exchange (HIE) across 11 health systems that all used
the same electronic health record, the authors conducted a retrospective time series
analysis of the effect on the monthly volume of clinical summaries exchanged of automatic
querying and different processes for patient consent. The consent processes included
using the general consent for treatment to cover the consent for HIE vs. requesting
specific consent for each individual need for HIE. The researchers did not assess
degree of use or usefulness of the information exchanged (care summaries), organizational
decision-making processes, or generalizability to other vendors.
Given the policy levers and financial incentives available to providers, a variety
of approaches to health information exchange (including community-based exchange networks,
enterprise-based exchange networks, and electronic health record (EHR) vendor-based
platforms) have been implemented. While each approach reflects various technological
solutions, there are also operational, logistical, and management processes, and decisions
that are embedded within each exchange. The study objective was to examine the relationship
between electronic exchanges of patient health information across organizations and
organizational HIE policy decisions.
The researchers looked at data on organization- level HIE policy decisions and their
impact on HIE volume from a diverse set of health care systems using the same EHRbased
HIE platform. The focus of the policies was on whether to automatically search for
information from other organizations whenever a patient with data in those organizations
presented for care, and whether to require HIE-specific patient consent. Their research
questions were: 1) What proportion of organizations chose to engage in automatic querying
and what is the associated impact on the volume of clinical summary exchange? 2) When
automatic querying is enabled, what proportion of patient linkages are established
automatically (representing information at another institution that the provider did
not know to seek) vs. manually requesting the information (representing information
the provider knew to seek)? and (3) What proportion of organizations chose not to
require specific patient consent for HIE and what is the associated impact on the
volume of clinical summary exchange?
The study covered a 2-year period from January 1, 2013, through February 28, 2015,
and included linkages made and clinical summaries transferred across all clinical
settings within each institution (such as outpatient clinics or other settings, emergency
departments, and inpatient stays). Study limitations included: the inability to normalize
exchange volume to account for the volume of patient care; inability to determine
the extent to which clinical summaries were used for patient care; lack of information
on how providers decided to implement their approach (auto-query or consent); and
inclusion of only institutions using a single vendor-based HIE platform.
The authors found that automatic querying and not requiring specific consent for HIE
for each individual care episode appeared to substantially increase exchange volume.
They conclude that these organizational HIE policy decisions impact the volume of
exchange, and ultimately the information available to providers to support optimal
care.
