Population Health Surveillance of ABCS (Aspirin Therapy, BP
Control, Cholesterol Management and Smoking Cessation) and
Patient-Reported Data Utilizing Open Source Software Tools
Jeffrey L. Brandt
OHSU Practicum with Kaiser Permanente Sponsor Tariq Dastagir, MD and Homer
Chin, MD
Abstract:
The Center
of Disease Control and Prevention (CDC) and the Centers for Medicare &
Medicaid Services (CMS) have funded several initiatives to utilize
evidence-based, community, and clinical preventions to monitor and reduce heart
attacks and strokes with Population Health Surveillance, of ABCS (Aspirin Therapy/ Hemoglobin A1c, Blood
Pressure, Cholesterol Management and Smoking Cessation [i]. Kornish et al., concluded in a study
that Post Traumatic
Stress Disorder (PTSD) common in stroke and Transient Ischemic Attack (TIA),
i.e., “mini-stroke”, patients showed a rise in medication non-adherence [ii].
In
order to close the loop and provide a more complete population surveillance
program, medication non-adherence and depression in the patient-centric
environment, patient-reported data or Patient Generated Health Data (PGHD) and
ODL (Observations of Daily Living) must be included[iii].
These additions have been brought
to the attention of the Federal Advisory Committee, which is currently seeking
comments on three requests to add PGHD to Meaningful Use Stage 3 quality
measurements[iv].
Changes
in the healthcare 2009-2012 laws and proposed changes to payment models (i.e.,
a movement away from fee-for-service model), the Accountable Care Organizations
(ACO), introduced in the
Affordable Care Act (ACA) 2010[v] requires documented
efficacy, a major driver in the policy. Though the topic of ACOs is not
the focus of this paper, the additional ACOs surveillance and reporting along
with, the timely Supreme Court ruling on the ACA, (legal and constitutional), it appears to the author as a crucial
component of care management and will be included as a relevant component. The author aims to illustrate the
use of open source tools such as the Office of the National Coordinator of
Health Information Technology’s (ONC) popHealth project to facilitate the collection of provider’s
compliance quality measurement data and additional patient-reported data. The author intends to illustrate the potential for utilizing
aggregated patient-reported data, and provider-collected data in combination
with evidence-based health regiments to produce better outcomes. This paper explores extending the popHealth
project capabilities to include patient-reported data via the ASTM E2369 Continuity of Care
Record (CCR) in order to aggregate clinical data and ODLs to promote more
complete surveillance and outcomes.
Objective:
Produce
a white paper illustrating the benefits of the integration of patient-reported
data with clinical data and analysis the use of open source software such as popHealth.org[vi]
to provide surveillance of ABCS
population and patient-recorded ODLs. Facilitators and barriers to adoption are also provided.
Introduction:
Research has proven that managing patient
population and active patient interventions is an effective way to reduce cost
and support better outcomes. The Kansas NHDSP reported, “In 2009, 11 clinics
submitted data for 2,303 patients with high blood pressure, including 1,449
without diabetes and 854 with diabetes. The percentage of patients without
diabetes who had their blood pressure under control increased from 54.6 percent
in 2007 to 63 percent in 2009. This was accomplished by providing interventions
to identified high-risk population.” Interventions from Clinics referring
patients with high blood pressure to behavior-change and peer-support programs
increased from 69.2 percent to 75 per-cent, and clinics distributing guidelines
on physical activity and blood pressure control to patients increased from 69.2
percent to 75 percent. Furthermore,
98 percent of all project participants with high blood pressure had their blood
pressure measured during at least one clinic visit in 2009, and 84 percent were
taking medication to control their condition.”[vii]
A large number of deaths in the United States are attributable
to preventable lifestyle and behavioral choices such as tobacco use, poor diet,
and inactivity[viii]. Multiple
federal and state agencies along with the private sector have developed
programs for monitoring patients ACBS (Aspirin
Therapy, BP Control/ Hemoglobin A1c (HbA1c), Monitor and control
blood glucose, Cholesterol Management and Smoking Cessation) in order to help/intervene
to achieve better outcomes and monitor risk factors for stroke, heart disease,
and diabetes [ix].
The CDC’s National Heart Disease and Stroke
Prevention (NHDSP) program funded The Mississippi Delta Health Collaborative
(MDHC), which was designed to prevent heart disease, stroke, and related
chronic illnesses via the monitoring of the ABCS in underserved high-risk
areas, the “Delta” region of Northwestern Mississippi. The Delta project utilizes field
caregivers to visit patients in their homes and manually capture surveillance data
with ODL questionnaires.
Thomas Frieden, MD, director of the CDC
states,“Focusing on the ABCS, advances in information technology, particularly
expanded use of prevention-oriented electronic health records, and increased
use of team-based care will help clinicians make progress in the ABCS”.[x] Open source initiatives such as popHealth,
provide the monitoring tools for all physicians, even for those without “Prevention-Oriented”
EHRs, (i.e., include built-in population health monitoring tools) but, with the
basic EHRs.
Population Surveillance tools can provide patient
information to assist payers and providers in tracking, analyzing and
intervening patient behavior to reduce cost and provide better outcomes. These tools may also provide
accountability of evidence-based care administered by the provider and healthcare
facility.
Note: The
use of acronyms ODL, PGHD and the term patient-recorded data are used
interchangeably as the same meaning in this document.
A strategy has been established to
identify relevant publications, not excluding Websites and non-published
articles. Pubmed and other
online searches were utilized to collect the data for this publication. The
author utilized data collected from his sponsor’s Tariq Dastagir, MD, Intern
lectures and discussion on patient-reported data.
Population Surveillance
Currently, Population Surveillance is used primary to measure patient population’s health and is performed
by clinicians within the clinical settings. With the advent of the Smartphone, cell phones and smart
wireless devices, we are beginning to experience patient surveillance moving
outside of the walls of the provider’s office and without the direct participation
of a clinician.
Population Surveillance will play an integral part
of the ACOs, as well, provide Meaningful Use quality measurements[xi].
Surveillance, combined with
evidence-based interventions, will promote accountability, efficacy, and patient
engagement and provide the opportunity to exhibit better outcomes and lowered
cost.
popHealth.org is the primary open-source patient
population analytics surveillance software covered in this report. The author strives
to illustrate the benefits of using this type of software tool for patient
panel surveillance along with reporting of quality measures for the ACO for
eligible professionals as defined in the HITECH Act[xii].
Surveillance shall play an
important and demonstrative part of the patient-centered care model, putting
the patient at the center of the care.
Patients spend the majority of their time outside of the clinical
settings. Much of the patient
condition information as well the opportunity to intervene is missed. The ONC and Health 2.0 sponsored a
challenge that was won by the “popEYE”
team, the resulting “product” sends
reminders to patient that haven’t completed their recommended lab tests. This lack of patient adherence was identified
via popHealth analytics. This challenge
provides a glimpse of the possibilities soon to be available to both providers
and patients.
Tom James, MD and Michael Fine, MD stated in
their research of utilizing patient-reported data along with claims data that,
“Data derived from Healthcare
Effectiveness Data and Information Set (HEDIS) measures do not accurately
predict which individuals will become frequent users of health care resources. Instead, health care plans must incorporate
regular and careful monitoring of symptoms through the use of patient-reported
outcomes as part of an overall asthma-management strategy to identify patients
with disease that remains uncontrolled”[xiii].
ACO Measures
The following is a list of the ACO Quality Care
measures as they pertain to the ABCS as required by the ACA[xiv]
Preventive Health
|
Tobacco Use
Assessment and Tobacco Cessation
Intervention
|
NQF #28
|
Preventive Health
|
Proportion of
Adults 18+ who had their Blood Pressure
Measured within the
preceding 2 years
|
CMS
|
At Risk Population
|
Diabetes Composite
(All or Nothing Scoring): Blood
Pressure <140 span="span">
|
NQF #0729
|
At Risk Population
-
|
Diabetes Composite
(All or Nothing Scoring): Aspirin Use
|
NQF #0729
MN
Community
Measurement
|
Preventive Health
|
Proportion of
Adults 18+ who had their Blood Pressure Measured within the preceding 2 years
|
MN
Community
Measurement
|
At Risk Population
|
Hemoglobin A1c
Control (<8 percent="percent" span="span">
|
NQF #0729
|
At Risk Population
|
Diabetes Composite
(All or Nothing Scoring): Low Density
Lipoprotein
(<100 span="span">
|
NQF #0729
|
At Risk Population
|
Diabetes Composite
(All or Nothing Scoring): Blood Pressure <140 span="span">
|
NQF #0729
|
At Risk Population
|
Diabetes Composite
(All or Nothing Scoring): Aspirin Use
|
NQF #0729
|
At Risk Population
|
Diabetes Mellitus:
Hemoglobin A1c Poor Control (>9 percent)
|
NQF #59
|
At Risk Population
|
Hypertension (HTN):
Blood Pressure Control
|
NQF #18
|
Figure
SEQ Figure \* ARABIC 1 ACO Quality Measures
Data Collection and Quality Assurance
Until recently, patient-reported data was collected exclusively
via a clinical encounter utilizing tools such as the Patient Reported Outcomes Measures
(PROM) i.e., a questionnaire to collect patient’s feelings about a disease or
condition.
A patient registry is defined as an “organized
system that uses observational study methods to collect uniform data (clinical
and other) to evaluate specified outcomes for a population defined by a
particular disease, condition, or exposure, and that serves one or more
predetermined scientific, clinical, or policy purposes.”[xv] Registries are used to measure quality
of care, determine efficacy, and illustrate cost effectiveness. The HIH has
added new measures to Patient Reported Outcomes (PROS) (i.e., self-reported
questioners) such as pain,
emotional distress and other ODLs that may impact quality of life. The PROMIS (Patient-Reported Outcomes Measurement
Information System)
overview states that clinical lab test and clinical measurements have very
little relevance on the chronically ill patients everyday life[xvi].
One of the many issues of collecting patient data
is the fragmentation of logistics and formats[xvii],
not to mention, the lack of clinical vocabularies to support PGHD. The Patient-Centric model has brought
pressure to bring about change to many of these issues. The following sections examined some of
the issues and solutions, forthcoming.
EHR Reported Patient Data
The software, popHealth receives patient data
via the ASTM
Continuity of Care Record (CCR)[xviii] and
HITSP C32 V2.5 [xix], Summary
Documents Using HL7
Continuity of Care Document (CCD) that support a standard and acceptable way to
transfer clinically captured patient surveillance data. EHRs must produce and be able to export
a CCR or a CCD to become certified and fulfill ONC’s “Meaningful Use”
requirements[xx]. Currently, the CCR and CCD are
the defacto standard for patient data exchange but it would be prudent of
vendors and builders of patient data systems to plan and design for changes and
additions to this standard. The standards
organization, HL7, has acquired the FHIR (Fast Healthcare Interoperability Resources) project pronounced "Fire", which utilizes Website
and mobile lightweight protocols, RESTfull and human and machine readable, JSon
(JavaScript
Object Notation) objects (name/value pairs) e.g., “First_Name” :”Jeff”
. The addition of FHIR to HL7s
tool chest is a giant step forward, including modern Health 2.0 health websites,
modern software techniques and mobile Apps.
Vocabularies
Medical
vocabularies and nomenclature provide unambiguous terms to describe medical
diagnosis, drugs, and treatment (e.g. LOINC, SNOMED, RxNORM). Vocabularies provide discreet data that
can be easily data-mined, analyzed, and used for quality reporting. Currently, there are no existing vocabularies
that represent specific patient-reported nomenclature and ontology.
Layman patient-reported data (free-text)
normally would not contain vocabulary data. This presents a challenge to data systems when discrete import
data is desired, expected or demanded.
One solution to this issue is to convert free-text data to discrete data
utilizing Natural-Language Processing (NLP) for machine-to-machine
communication. The other is to
store the data as free-text for human consumption. The second is less desirable because it limits the
ability to apply analytics to the data.
Patient Self-Reported Data
Patient-reported
and captured data helps to close the loop on managing a patient population
outside of the medical facility.
Paul Estabrooks, et al, states, “Successful health promotion and disease
prevention requires patient-reported data reflecting health behaviors and
psychosocial issues.” Estabrooks mentions, that rarely are these observance
included in an EMR.[xxi] Wen,M. 2012 paper reported some of the
issues of patient self-reported data, e.g., BMI can be misleading because of wrongly self-reported height
and weight depending on ethnicity, age and gender [xxii]. Some of these errors can be eliminated
with tools such as WIFI scales that automatically report patients weight to a
PHR or cross checking the data with clinical setting captured data.
Patient
health data manifest in a diverse set of media, from hand-written paper notes,
spreadsheets, to electronic PHRs such as Microsoft HealthVault or Dossia. Hospital and payer’s patient portals
may also be utilized. One barrier of PGHD is that health
care facilities are reluctant to accept or utilize patient-reported data. If a provider accepts patient provided
data then the provider takes on the responsibility and liability of that data. A provider may also have concerns about
data validity.
An
example of a current ABCS surveillance project is the CDC funded Mississippi
Delta Health Collaborative (Delta Health Initiative), in which the author participated. The project’s plan was to utilize field
healthcare personnel to manually collect ABCS data and other health indicators
such as physical activity and nutrition, smoker in the home etc. The field
personnel upload the data from their laptop upon return to their home office.
An alternative to manual collection is
the utilization of the Internet, Smartphone Apps, SMS/TEXT or PAD/TABS (Pads and tablets)
based PROS questionnaires. Patients
could be monitored for these extensions to the ABCS and possibly reduce the
number of field caseworker needed.
Patient-centric Message Structure
To date, standards committees only
had the need to support provider-reported data. In
the Health 2.0 world, the advent of mobile devices has quickly changed how data
is collected and reported.
Standards committees such as HL7 are currently working on incorporating “Patient-Authored
Document” into the HL7 CDA documents in order to support Patient provided data. These patient-authored documents such as registration
forms and health history shall be incorporated into the patient’s medical record[xxiii],
which in-turn may be accessed via popHealth.
The ASTM CCR currently doesn’t
support specific elements for patient-centric data but many vendors including
Microsoft HealthVault utilize the “Vitals” (signs) element to store patient
collected and patient devices data, e.g., FitBit fitness recorder. The IHE (Integrating the Healthcare Enterprise) is working on a
specification for interfacing with Web and mobile devices, IHE ITI mHealth
Profile also known as the “Mobile access to Health
Documents” (MHD)[xxiv]. This
document also advocated the use of modern lightweight protocol (e.g., RESTful,
JSon) and authentication scheme (e.g., OAuth) currently used by developer of
Apps and devices. This will make
it possible for EHR to retrieve much more patient data to be used to achieve
better outcomes and more real-time surveillance of the patient.
Observation of Daily Living
The
term Observation of Daily Living (ODL) coined by the Project HealthDesign
program of The Robert Woods Johnson Foundation. ODLs are patient-collected observations that are not
normally found in a clinical patient health record but can identify significant
influences on a patient’s health, e.g., smoking, eating, exercising, depression,
sleep quality and quantity and diet, to name a few. The
Personal Health Record and some Patient Portal were designed to store patient’s
ODL data. For many reasons, most of this data is not
accessible by the patient’s doctors or caregivers. First and foremost, the
majority of EMRs are not designed to utilize patient-reported data, so it can
be difficult to incorporate the data into the provider’s workflow. There are
also issues of the abundance of unfiltered data being directed at the
doctor. The author of this
paper proposes the use of analytic tools to filter, aggregate, and synthesize
the data into valuable reports, and content that can be integrated into the
provider’s workflow.
Tools
such as popHealth have the ability to analyze PHR data as well as clinical EMR
data. PHRs, such as Microsoft’s HealthVault,
supports the exporting of an ASTM CCR and a HL7 CCD. Much of the data currently being stored in a PHR is ODL,
including device data such as glucose monitoring. The CCR and CCD currently does not fully support specific
fields to store ODLs however, the Vitals Sign elements/fields may and are
currently being used for ODL storage. This allows for
the PHRs in their current form to collect and store ODL data and for popHealth
to connect and process that data with the current standards.
Clinical
vital signs are normally observed and recorded in a doctor’s office on a
periodic basis depending on a patient’s health. This provides a “thin slice” or “epoch” of data of the
patient’s health condition at that moment. ODLs whether collected by the patient or automatically
uploaded from a device could provide the doctor with a contiguous, less
episodic view of a patient’s health. “Tomorrow’s new vital” as coined by Susan Promislo on the
RWJF blog in 2008[xxv] is
available today. We have the ability
to record, store, and consume patient’s ODLs data directly into a PHR. This makes it possible for vendors of EMR/EHR
(Electronic Medical Record/Electronic Health Record) to consume PHR data via
current protocol standards. We can
utilize open source tools to store and analyze the data and present it to the
provider within the EHR and integrate it into provider’s workflow.
Some
vitals tests present more accurate results when taken outside to the clinical
environment. Blood pressure,
a noted example of a test that presents elevated levels contributed to “White
Coat Syndrome”. The “White Coat Hypertension is not a benign entity in our
population” stated Godi, et al[xxvi]. In this situation the provider would
receive more accurate data with the PGHD, reducing the chance of
miss-diagnosis.
Open Source Health Tools
Open
source healthcare software has come center stage as government agencies,
startups, and mainstream health facilities look to reduce costs. Though, there are many obvious savings
associated with open source software there are also hidden cost (e.g., support,
reliability, internal knowledge) that must be accounted for when making a
platform decision for projects.
The
following, non-inclusive list of open source healthcare tools is provided as an
additional artifact of the research performed for this paper. The focus of this paper is the ONC
sponsored popHealth project.
popHealth
The popHealth.org is an ongoing Open Source
project which, is sponsored by the Office of the National Coordinator for
Health IT (ONC). This project
originated as the “Primary Care Information
Project “in
New York City, NY under the leadership of the ONC’s Dr Fazad Mostashari. The
project enhanced to assist EMR vendors and health organizations in supporting
Stage 1 Meaningful Use requirements. The tool supports 44 Stage 1 clinical
quality measure for eligible professional. There are also plans to support Stage 2 quality measures in
2012. [xxvii]
Though the project intended user
was a EHR vendor, under open source licensing it is open for anyone to
use. popHealth has the ability to
process both the CCR and the CCD documents. The CCR is typically used
in Personal Health Record for patient use and the CCD is utilized to
export/import data to/from an EMR.
Part of popHealth solution is the incorporation of
the MongoDB open source database.
Which provides a user with a significant cost savings over conventional
databases such as Oracle or Microsoft.
Though the focus of the popHealth project is to
provide an easy and inexpensive facility to track and achieve Meaningful Use
requirements. The project being Open Source, provides
anyone the license to modify or add to the source code as needed. A user may track and report on any clinical
major deemed needed for their project, e.g., ABCS.
According to MITRE, the primary non-for-profit
contactor for the ONC, there were three pilot projects using the popHealth
System, New York integrating with eClinical Works, North Carolina integrated
with AthenaHealth and one in Chicago.[xxviii]
One of the lessons learned during the pilots is “Providers must use required
standards for the tool to capture all the measures.” said Maggie Lohnes, healthcare principal
at Mitre[xxix]
(e.g., LOINC, SNOMED-CT as required by Meaningful Use measures). If the captured data is not available
in the required format, semantic normalization will need to be performed before
the data is sent to popHealth.
Though there is not a substantial amount of quantitative
data on the performance of popHealth,
there are published results of a pilot integration between Beth Israel
Deaconess Medical Center (BIDMC) and Massachusetts eHealth Collaborative
(MAeHC)[xxx]. The reports main issues that they
encountered were integration and normalization of data, especially vocabulary
code mismatches of the provided data within the CCD (C32)
INDIVOTM
Indivo is a “Personal Health Platform” which
includes a PHR developed by core Indivo X source
code is licensed under the GPLv3,
Indivo is a Children's Hospital Informatics Program (CHIP) project at Boston’s
Children's Hospital. “Indivo inspired both Microsoft HealthVault and Google
Health, and a good deal of its code underlies HealthVault”, stated Andy Oram of
O'Reilly Media [xxxi]
Indivo like popHealth utilizes RESTful interface with OAuth
authentication, the interface of choice of many of the Health 2.0 innovation companies
developing today. Indivo supports
XML based documents CCR, CCD or CDA standards, as well as non XML docs such as
PDF and MPEG for exporting and importing patient data. They have also integrated with the
SMART Project (see below) via W3C RDF (Resource Description Framework) and XML
Schema.
SMART Platform
The Substitutable
Medical Apps, Reusable Technologies (SMART) program is funded by the Office of
the National Coordinator of Health IT (ONC). It has been developed as Open Source software tool to enable
users to quickly develop patient facing apps with reusable software. Though based on XML it does not
currently embrace the CCR, CCD or CDA standards for exporting and importing
patient data. SMART project does utilize SNOMED, RxNORM and LOINC vocabularies. The technology for communication OAuth and REpresentational State Transfer (REST) Webservices[xxxii]. Indivo and SMART are integrated to
provide an easy facility to incorporate User Interfaces for Indivo projects.
I2B2
Informatics
for Integrating Biology and the Bedside (I2B2) informatics computational framework.
It is funded by the NIH National
Center for Biomedical Computing and based at Partners HealthCare System. Provided via Open Sourced license that
provides a framework for retrieving research and genetic data through data mining
and query tools. It also supports
systems to provide security and
data governance.[xxxiii]
I2B2
utilizes the following vocabularies[xxxiv]:
Data Type
|
Terminology
|
Diagnoses
|
ICD-9
and IMO
|
Procedures
|
ICD-9
& CPT
|
Medications
|
Medications
are classified based on the Medispan hierarchy used in the CCHMC Epic build.
|
Laboratory
Results
|
Laboratory
tests are identified by a mixture of LOINC codes and internal Cerner numbers.
The tests are listed under the same hierarchy used in Epic.
|
Figure
SEQ Figure \* ARABIC 2 I2B2 Supported
Vocabularies
Care Management
Clinical
Care Management is no exception to the extensive changes in today’s healthcare. From new technologies, tools,
patient-centric care, to patient intervention and Decision Support Systems
(CSS). Even with all that is new, evidence-based
medicine protocols are still the bases of the decision processes in Care
Management.[xxxv] One of the primary goals is to provide
the tools and guidance to navigate the healthcare system and provide conduit between
patient, family, caregiver and services.[xxxvi]
Tools
like popHealth have the power to enable proactive care management and patient interventions
by combining data mined from the EMR and monitoring the patient outside of the
office.
Patient Value Proposition
Laura E. Santurri defines patient empowerment as “individual being
an active member of his/her disease management team”[xxxvii].
The data collected for quality measures and
ODL may help to empower the patient.
The AHRQ states that “Research shows that patients who focus on
communicating with their doctors and participate in decision making, experience
fewer medical errors.”[xxxviii]
Overall patient engagement and awareness of their health leads to better
outcomes.
Quality
of Life and/or Health-Related Quality of Life (HRQL) as described by the CDC, “An
individual’s or group’s perceived physical and mental health over time”[xxxix]
A better quality of life reduces depression with can lead to better medication
adherence and better outcomes .
Provider Value Proposition
“Creating
additional touch points between patients and physicians beyond the intermittent
clinical visits creates practice loyalty and significant brand equity”[xl]
Other
industries have utilized the customer to reduce their cost for many years, e.g.,
ATM and self-checkout. Healthcare
and it’s providers can also benefit the same way by having the patient take on
the duties of clinical health data collection.
Patient
perceived quality of their healthcare is very important in their effect on
clinical outcomes [xli]. Self-perception of patient health quality can also provide
insight into the future burden on the society healthcare delivery system[xlii].
Payers Value Proposition
Payers, government and employers have the most to
gain from wellness; healthy patients utilize less healthcare and lowers cost.
Heart disease and stroke, the principal
components of cardiovascular disease (CVD), are among the nation's leading
causes of death and disability and the most expensive medical conditions for
businesses[xliii][xliv]. Research has shown that intervention
based on PGHC can reduce strokes, the value proposition is evident.
Conclusion
The research indicates that utilizing clinical and patient-recorded ABCS surveillance data to support interventions leads to better outcomes.
ONC popHealth project software has the ability to ingest CCR documents from PHRs and provides a structured gateway to patient-reported data. popHealth source code could easily be adapted to display patient-reported data alongside of clinical data from the EHRs. The software currently provides only a manual/visual facility for clinicians to analyze the data. Data Collection is the first step of population surveillance; the data will then need to be analyzed for specific diseases, conditions and research. Additional development would be needed to provide automatic actionable intervention, which is not covered in this paper.
As mentioned, the visual results could be presented to the clinician via the popHealth User Interface or provided in the form of a print or presentation format, e.g., PDF format, SMART Apps.
John Halamka, MD, points out in his blog post[xlv] “More popHealth Lessons learned” that there are multiple integration issues with the imported data that must be taken into account when utilizing popHealth to provide seamless quality measurements. Halamka is speaking to the normalization of data vocabularies. Halamka also mentions issues with the current HL7 CCD standards to export patient data from the EHR, that is, the CCD provides only episodic encounter data and there is a need for longitudinal health record summary to provide better overall view of a patient’s health. Since the patient only visits the doctor periodically, patient-reported data is necessary to provide a more complete more contiguous longitudinal health record.
Systems that claim to be “Standards” compliant do not ensure that integration can be avoided. Data streams must always be tested and normalized to insure complete compatibility. Normalization issues such as incomplete data or optional data, which may be considered compliant, will compromise end-to-end communication[xlvi].
The following is a proposed high-level component architecture of a proposed patient-centric surveillance system:
Figure SEQ Figure \* ARABIC 3 Patient-Centric System Overview
Author’s notes:
We
are only beginning to utilize surveillance in health/healthcare as a tool to engage
the patient and providing meaningful personal interventions. During the keynote at the 2010 Oregon
HIMSS chapter’s annual meeting, Oregon Governor, John Kitzhaber, MD told a
story of an elderly woman with congestive heart failure living in a sweltering hot home. Dr. Kitzhaber stated that under our current medical system (CMS), that we have in United States
today, Medicare will pay for the ambulance to get her to the an Emergency Room.
The cost of the ER visit, $50,000 of care when she is admitted to the hospital
and thousands of dollars in ongoing care (e.g., rehabilitation) once she is
released but will not pay for a $200 air conditioner which is all she needs to
not have this costly episode[xlvii]. Dr Kitzhaber story illustrates the need
for reform and that something as simple as an ambient thermometer that
communicates via WIFI or Bluetooth could be part of a patient surveillance that
could save lives and payers cost.
The popHealth project shows great potential for providing analytics
from both clinical and patient-reported data. There are still many issues to solve concerning
normalization of the data, e.g., codeset.
This should be less of an issue in an environment such as Kaiser
(sponsor of this paper) where all facilities utilize the same EHR and vocabularies
throughout the organization.
Open source has the potential to jump-start many new health 2.0
type healthcare products and provide cost-effective initiatives that previously
would not have been possible. One
discerning point, stakeholders of
any open source project, must realized that there are hidden cost that must be
considered when budgeting a project, e.g., support, maintenance, reliability,
loss of corporate sponsor.
The author was quoted by the AMA online Magazine “Open Source can be
like an Open Sore”[xlviii], If you
do not know how to take care of it (i.e., Open Source), it can kill you i.e.
your project.
Acknowledgments
The author would like to thank Dr. Tariq
Dastagir, Post-Doctorial Fellow Kaiser Permanente, Dr. Homer Chin Medical Director for Clinical Information
Systems at Kaiser Permanente and Justin Fletcher PhD. Assistant Professor in the Department of
Medical Informatics and Clinical Epidemiology at OHSU,
for their assistance in the preparation of this manuscript.
Disclosure
The author’s company Communication Software,
Inc. is a development/integration partner of Microsoft HealthVault.
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