Improving and Expanding Electronic Health Records in a Rural Setting

Improving and Expanding Electronic
Health Records in a Rural Setting:
Clinical Decision Support System &
Public Health Component
Urmi Cholera
Jessica Wolff
Urmi Cholera
Urmi Cholera is a MPH graduate of Columbia University's Mailman School of Public Health. Before interning
at ICTPH, Urmi was a research intern at the Mental Health Association of NYC, Inc. She has also
worked in Peru and Costa Rica in the area of community development and volunteered with Sakhi, a
NY-based organization that works with domestic violence issues in South Asian communities. Furthermore,
Urmi brings a varied background to her work at ICTPH as she studied business and accounting at
McCombs School of Business at the University of Texas at Austin and worked for KPMG, LLC., prior to
embarking on a research career in public health and policy.
Jessica Wolff
Jessica Wolff is a graduate of the MPH program at Johns Hopkins Bloomberg School of Public Health.
Jessica has also interned for the National Institute of Mental Health (NIMH), researching the genetics of
bipolar disorder. She has a wide range of volunteering experiences from working for the Appalachian
Service Project in Kentucky to leading educational programs in Guatemala. Jessica's focus on studying
global public health interventions at Johns Hopkins led her to pursue an internship at ICTPH to gain critical
field experience at the international level.
India’s rural population, comprising nearly two-thirds of the total population, faces sharp disparities in healthcare
compared to their urban counterparts. With most healthcare professions located in urban centers, Indians in rural
settings have limited access to care. Additionally, the majority of rural healthcare needs are financed out-of-pocket by
a private sector comprised of untrained providers. In this way, rural-based Indians face inequalities in both access and
quality of care.
IKP Center for Technologies in Public Health (ICTPH) aims to remedy these inequalities by designing and operating
nurse-run health clinics in rural Tamil Nadu, India. ICTPH has adopted novel tools for these clinics to aid in the delivery
of quality healthcare. One such technology is an Electronic Health Records (EHR) system used to monitor and evaluate
nurse-patient interactions. The objective of the current study is to research the expansion of the current EHR system to
include both a Clinical Decision Support System (CDSS) and a Public Health component. The CDSS has the potential
to aid in the accuracy of point-of-care diagnostics and in the delivery of guideline-adherent care, while the Public
Health component provides a preventive focus for improved long-term patient outcomes. Both additions employ a
similar methodology wherein the knowledge of experts is relied upon to create decision-rules, which are represented
graphically by decision-trees.
Section one of the paper reviews background literature relevant to the EHR public health component and CDSS; section two
outlines the paper’s objectives; section three expands upon the literature review to highlight the typical methodology behind
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both additions as well as the methodology specific to ICTPH’s model; section four describes data collection and use; and
section five concludes with limitations and future directions.
Background
Electronic Health Records
EHR is a growing concept in healthcare IT. The first EHRs began to appear in the 1960s. The International Organization for
Standardization defines EHR as a “repository of patient data in digital form, stored and exchanged securely, and accessible
by multiple authorized users. It contains retrospective, concurrent, and prospective information and its primary purpose is
to support continuing, efficient and quality integrated health care” (ISO definition, 2004). EHR is used primarily to assist the
institution in setting objectives, planning patient care, documenting the delivery of care and assessing the outcomes of care
(Hayrinen et al., 2008).
There are numerous EHR software programs that health organizations can utilize to document patient information. All
software differs somewhat in the information the EHRs may gather and maintain about patients. EHR software can contain
combinations of medical data components such as referral information, present complaint (symptoms), past medical
history, lifestyle, physical examination, diagnoses, tests, procedures, treatment, medication and discharge information
(Valdes et al., 2004).
The benefit of utilizing EHR in health care facilities is the belief that it will improve the quality of healthcare. EHR provides an
improvement in legibility of clinical notes, easier and quicker access to patient information during appointments, reminders
to prescribe drugs and administer vaccines, easier management of chronic diseases to name a few (Center for Health IT at
the AAFP, 2010). EHR systems propose to help reduce medical errors by providing healthcare professionals with decision
support. This can be achieved by various functions in the EHR system such as incorporating warnings alerting health
professionals regarding problems of incompatible drugs (Fraser et al., 2005).
Implementing EHR in a Rural Setting
Development of EHR systems is still a new and challenging task even in resource-rich developed countries. EHR
implementation requires extensive research regarding local needs and available resources and technologies. Developing
countries have additional issues such as a lack of infrastructure and trained and experienced staff, which prohibit them from
providing proper and needed healthcare to rural populations. It is believed that the implementation and utilization of EHR in
a rural setting will improve health care decisions made (Fraser et al., 2005). This is especially important for rural India, where
according to a 2001 census, 74% of the country’s population reside.
There are various concerns sensitive to rural regions that must be addressed before implementing EHR systems in
remote clinics. A lack of resources and technology is seen in many of these regions. There is also a greater lack of
experience in creating EHR systems for developing countries; rural priorities, constraints and requirements are less
well understood (Fraser et al., 2005). A few pilot projects have been conducted that test EHR implementation and
effectiveness in developing countries. Projects tested in rural regions of Kenya, Haiti and Malawi show positive results
in the use of these systems by healthcare workers in a poor country with virtually no infrastructure and with limited IT
skills (Fraser et al., 2005).
Clinical Decision Support System
CDSS are computer systems designed to impact clinician decision-making about individual patients at the point in
time that these decisions are made (Berner & La Lande, 2007). Characteristics of an individual patient are matched to a
computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the
clinician for a decision (Sim et al., 2001). Most CDSS exist as part of an EHR system or computer order entry system
rather than as a stand-alone entity (Berner & La Lande, 2007). CDSS have been developed to assist with a variety of
decisions, including diagnosis. In such a system, the patient’s signs and symptoms are entered either by the clinician or
are imported directly from the EHR (Berner & La Lande, 2007). The system contains a knowledge-base with information
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Improving and Expanding Electronic Health
Records in a Rural Setting: Clinical Decision
Support System & Public Health Component
about diseases derived from the medical literature or expert consultation, allowing the inference engine to map the
patients’ symptoms to those diseases and ultimately generate a diagnosis for the clinician to consider (Berner & La
Lande, 2007).
CDSS: Benefits and Drawbacks
The evidence for the role of these systems in improving the safety, quality and efficiency of health care is growing. CDSS
have been shown to improve the quality of medical care by helping clinicians comply with ever-changing guidelines and
standards (National Electronic Decision Support Taskforce, 2003). This type of “evidence-adaptive” CDSS is one in which the
clinical knowledge base of the CDSS is derived from and continually reflects the most up-to-date evidence from the research
literature (Sim et al., 2001).
Electronic decision support systems can also play a role in preventive care. A systematic review of 18 decision support systems
within the primary care setting concluded that preventive care reminder systems are a reasonably effective intervention
(Hunt et al., 1998). Likewise, of 21 trials evaluating reminder systems for prevention, CDSS was found to be beneficial in
76% of trials as measured by rates of screening, counseling vaccination, testing, medication use, or identification of at-risk
behaviors (Garg et al., 2005). In studies of cardiovascular prevention in which physician performance was measured by blood
pressure measurement, cholesterol assessment, identification of smoking, and use of cardio protective medications, 5 of 13
trials reported improvements with use of CDSS (Garg et al., 2005).
CDSS used to assist in diagnosis can lead to significant improvements in diagnostic accuracy and improvement in
decision confidence. One study that using a CDSS to diagnose acute chest pain reduced the number of unnecessary
referrals to the coronary care unit from 35 per cent to 19 per cent (Jonsbu et al., 1993). Similarly, Friedman et al (1999)
found that clinicians’ diagnostic performance improved from 39.5 per cent to 45.4 per cent when a CDSS was used to
assist in the diagnosis of 36 difficult cases with known diagnoses. However, in 6 per cent of cases the clinician actually
changed from the correct diagnosis to an incorrect diagnosis after consulting the system. Of 10 trials evaluating the
effect of a diagnostic system on practitioner performance, the system was found to be beneficial in 40% of the trials
(Garg et al, 2005). Among controlled trials testing CDSS performance in reducing unnecessary health care utilization as
measured by frequency of redundant testing and unnecessary hospital admissions, 3 of 4 trials reported improvements
(Garg et al., 2005).
The effectiveness of CDSS is highly dependent on documentation within the EHR, which can affect the functionality and
accuracy of the recommendations prompted by the decision support system. For example, one study found that due to
important gaps in patients’ medical records, a CDSS designed to improve safe prescribing would have failed to warn about
patients’ high-risk status over 77% of the time (Berner et al., 2005).
Most studies of CDSS concern physicians to the near exclusion of other clinicians. A rare review of CDSS use among nurses
reported that evidence-adaptive CDSS may be effective in assisting nurses with guideline-adherent care and in improving
patient outcomes (Anderson & Willson, 2008). This same review cited such nurse-identified benefits of CDSS as improved
interdisciplinary communication, increased access to information on best practice, and increased consistency in quality of
care. Barriers to use included deficiencies in the EHR system, lack of administrative support, and the time required to learn
and implement the new technology (Anderson & Willson, 2008).
Implementing CDSS in a Rural Setting
Controlled, systematic studies of the effectiveness of CDSS are a near anomaly in low-resource settings. The two CDSS that
have been implemented in rural India and undergone review include the Early Diagnosis and Prevention System in Salem,
Tamil Nadu (Peters et al., 2006) and RightChoice in Almora, Uttarakhand (Arora, 2010).
The CDSS in rural Tamil Nadu proved to be a conservative screening tool, with a high sensitivity (80%) and low specificity
(22%) (Peters et al., 2006). Utilization of services increased at intervention sites (those using CDSS) over control sites and
CDSS was associated with consistent and statistically significant improvements in patient satisfaction (Peters et al., 2006).
Use of CDSS seemed to have little impact on health workers (Peters et al., 2006).
The CDSS in Almora is populated by data collected from a baseline survey. The author described problems in implementation
related to the collection of this data, wherein most of the villagers’ chief complaints did not fit into pre-stated categories but
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instead fit into an “other” category (Arora, 2010). Furthermore, even if CDSS were able to help a non-physician correctly
diagnose symptoms, the patient would still have to seek a doctor for further tests and prescriptions for medications, meaning
that physicians are never completely removed from the equation (Arora, 2010).
Public Health
EHRs predominantly have had a clinical focus, providing patient appointment information and decision support for medical
professionals. EHR can be utilized to support public health, long-term preventative measures by incorporating additional
components. A widely accepted definition of public health centers around three core functions: assessment (assessing
the state of public health), policy development (developing policies to promote health) and assurance (assuring that
these policies are implemented) (Figure 1) (Kukafja et al., 2007).
Public health works to prevent disease rather than treating them after symptoms arise.
Figure 1
Public health practitioners place disease and disability in the context of societal,
behavioral, and environmental factors. The public health system seeks measures
that can benefit populations through social and legal policy, and behavioral and
environmental interventions (Kukafja et al., 2007).
Assessment
Assurance
In order to incorporate a public health focus to EHRs, new non-clinical information
about patients would need to be gathered in order to populate societal, behavioral
and environmental risk factors that would identify potential diseases patients would
be at risk for. This endeavor would follow the assessment function of public health.
Policy Development
Once this is incorporated into an EHR this information would go alongside family
history, past clinical information, lab test results, etc. and would be available at point of
care. The availability of this additional public health component in EHR would provide
medical practitioners the information necessary to create and promote needed
preventative initiatives tailored to individual patients depending on their identified risk
factors (Kukafja et al., 2007). This information could then be used to develop policies
that would work to change population level environmental factors and conditions.
As of yet there is little research regarding implementing a public health focus to EHR systems in rural regions. A holistic
public health EHR system would need to evaluate patient behavioral, psychosocial, and environmental variables. In rural
India environmental factors play a primary role in the main diseases affecting their populations.
According to recent estimates, premature death and illness due to major environmental health risks accounts for nearly
20 percent of the total burden of disease in India (Environment and Social Development Unit, 2001). Water, sanitation
and hygiene as well as indoor air pollution contribute greatly to this burden of disease and are the leading causes of
gastrointestinal diseases (Prüss-üstün et al., 2008) and respiratory diseases (Rehfuess, 2006), respectively. While the rising
incidence of diabetes and cardiovascular disease within India has shifted attention to behavioral and psychosocial risk factors
of disease, this shift should not come at the expense of environmental risk factors, which can be especially hazardous for
India’s rural population (Environment and Social Development Unit, 2001). These environmental risk factors are primed for
interventions at the individual, household, and community level; in fact, many evidence-based interventions already exist
for limiting these risk factors (Environment and Social Development Unit, 2001). For this reason, EHR systems with a public
health component should place an emphasis on collecting data relevant to these factors and then use these data to develop
targeted interventions.
Water, Sanitation, and Hygiene
The lack of adequate sanitation and safe water has significant health impacts. In 2002 the World Health Organization estimated
that around 700,000 Indians die each year from diarrhea (Planning Commission Government of India, 2002). Between 69 to
74 percent of India’s rural population take their drinking water from protected sources, leaving an unserved population of 26
to 31 percent (Panning Commission Government of India, 2002). Improved water supply, namely improvements in drinkingwater
quality through household water treatment such as chlorination at point of use, can reduce diarrhea morbidity by
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Improving and Expanding Electronic Health
Records in a Rural Setting: Clinical Decision
Support System & Public Health Component
between 6 to 25% (Prüss-üstün et al., 2008). The availability of potable water alone may not result in significant decline in
gastrointestinal diseases, especially diarrhea, unless it is coupled with improved sanitation and hygiene (Prüss-üstün et al.,
2008). Improved sanitation can reduce diarrhea morbidity by 32% (Prüss-üstün et al., 2008). Between 18 and 19 per cent of
India’s rural households have a toilet, with most studies showing an increase in health benefits once access to toilets within
a community reaches 50-60 percent (Environment and Social Development Unit, 2001).
Indoor Air Pollution
Respiratory diseases account for a greater share of mortality and morbidity in developing countries in general than diarrheal
diseases. However, the causal link between respiratory diseases and air pollution is less well characterized than is the
link between diarrhea and waterborne pathogens (Environment and Social Development Unit, 2001). Studies indicate that
particles of small diameter emitted from solid fuels can lead directly to respiratory infections, asthma attacks, and allergies
(Environment and Social Development Unit, 2001). Indoor air pollution has been linked to tuberculosis, interstitial lung
disease and laryngeal cancers (Rehfuess, 2006). Inhaling indoor smoke doubles the risk of pneumonia and other acute
infections of the lower respiratory tract among children younger than 5 years of age (Rehfuess, 2006). Women exposed to
indoor smoke are three times more likely to suffer from COPD, such as chronic bronchitis or emphysema than women who
cook with electricity, gas, or other cleaner fuels (Rehfuess, 2006).
Objectives
The current study aims to expand upon and improve the EHR currently in use by adding both a public health component
and a CDSS. To this end, the methodology behind such an expansion is explained, including previous work conducted
in these areas as well as the methodology specific to the system envisioned by ICTPH. The current EHR system in place
at ICTPH’s clinic in the Alakkudi village serves as a database of patient appointment and medical information. The EHR
system was introduced to the clinic in order to tackle the lack of medical documentation in rural regions and to facilitate
better patient care.
The motivation for expanding the EHR to include a CDSS is rooted in the lack of quality healthcare available to India’s rural
population. The shortage of doctors in India is one of the most critical barriers to expansion and quality of health care (Ranjan,
2008). Of the 21,490 doctors needed in community health centers across the country, only 5,910 are available (Ranjan, 2008).
Of the 66,059 nurses and midwives needed in these same centers, 41,313 are available (Ranjan, 2008). These numbers are
even starker in the rural village setting, where nearly two-thirds of India’s population resides (Ranjan, 2008). The doctor to
population ratio is six times lower in rural settings than urban, and villagers spend an average 1.5 times more on healthcare
compared to their urban counterparts for the same illness (Jhunjhunwala, 2008).
CDSS operated by non-physicians with the capacity for physician consultation via telemedicine may offer a way to alleviate
this inequality in healthcare. The CDSS will expand upon the information management capacities of the current EHR by
evaluating symptoms and other data to reach a diagnosis. In this way, ICTPH envisions the CDSS as a mechanism through
which healthcare will be standardized and evidence-based, not only increasing access to care but also quality of care.
A public health component utilizes non clinical patient information (i.e. behavioral, psychosocial, environmental factors) to
foresee what conditions patients will be at-risk for. This information can be used by a health professional in order to better
understand patients’ health profiles. While the CDSS addresses improving the immediate care of patients, a public health
component will allow the EHR system to address preventive care. Both these expansions work together to efficiently and
effectively improve the health of the rural populations served by ICTPH.
Methodology
CDSS
ICTPH aims to remedy the disparities between the healthcare needs of the rural population and the available resources by
establishing rural clinics run by nursing staff. Clinics are designed to serve a population of approximately 10,000 people each.
Nurses work under the supervision of a doctor, and follow a set of protocols designed to promote evidence based care and
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ensure uniformity across the network of clinics. These protocols take the form of a Clinical Decision Support System (CDSS)
that guides the nurse during the consultation process, aiding her to diagnose a patient. The EHR currently in use at the clinics
records patient data during what is a proscribed visit flow – SOAP (Bickley & Szilagyi, 2009). According to this, patient data
is gathered, yielding Subjective information, an Objective examination, and an overall Assessment and treatment Plan. The
CDSS aims to improve on the existing EHR by taking the collected data and structuring it in a manner that enables the nurse
to navigate through the SOAP process according to custom made protocols for diagnosis and treatment. More specifically,
the subjective and objective data are entered into the system in real-time, and the CDSS ultimately provides a diagnosis and
a clinical decision (treatment and/or referral).
The CDSS designed by ICTPH employs heuristic modeling, an approach used by many decision support systems (Greens,
2007). Heuristic modeling attempts to emulate human expertise and reasoning processes and is a particularly sound
approach in settings where there are insufficient data to be able to derive estimates for probabilistic approaches (Greens,
2007). One benefit of heuristic models is that because they are based on heuristics understandable to and made by humans
they are able to explain their reasoning (Greens, 2007). Heuristic modeling is ultimately used to create a rule-based system,
in which individual logical statements are obtained by observing or interviewing human experts in an attempt to emulate
their reasoning processes (Greens, 2007). A top-down rule-based system, such as the one envisioned by ICTPH, starts with
the goal of getting to a diagnosis. A bottom-up rule-based system builds rules based on data that are arriving on a regular
basis (Greens, 2007).
Decision-rules are represented as logical IF-THEN statements that may be followed to reach some conclusion (Jenders,
2007). They map the circumstances of a particular situation to a particular choice, whether it is a diagnosis, treatment
plan or an observation that in turn may lead to another decision (Jenders, 2007). Decision-rules are characterized by a
series of branching questions or logical statements that can be represented graphically as decision trees. Each node
may ask a different yes/no question and the appropriate branch of the tree is followed depending on the response to the
question (Jenders, 2007). The main purpose of the decision tree is to lay out the sequences of decisions and possible
outcomes at each step, so the decision maker can focus on the critical variables (Greens, 2007). The tree is expanded
until all the branches reach points that can be considered endpoints from the point of view of the analysis (Greens,
2007). One advantage of using decision-trees is that their structure allows for nearly any programming language to be
used to encode the clinical knowledge (Jenders, 2007). It is also easy to control the predictability of the results and
thus improves accuracy (Jenders, 2007). One drawback to the use of decision-trees is that it is difficult to acquire and
maintain their knowledge base because of the mixture of programming and clinical knowledge (Jenders, 2007).
Figure 2: CDSS Decision Tree
Patient symptoms
entered (Ex. Cough)
Highlights body
system to examine
(Respiratory System)
Physical exam
procedures outlined
(Chest Exam)
Patient lab test
results entered
Lab tests suggested
(Chest X-ray)
Differential diagnosis
given (TB,
Pneumonia, etc.)
Final Diagnosis
(Pneumonia treatment
suggested)
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Improving and Expanding Electronic Health
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Support System & Public Health Component
The decision-trees developed by ICTPH are in line with the SOAP consultation format used for patient visits at the clinics.
The starting point is a review of common symptoms a patient might be presenting (e.g. cough). The system then guides the
nurse into establishing the nature of those symptoms by asking about the presence of certain characteristics (e.g. site, onset,
character, radiation, associated factors, time, relieving factors, and severity) based on a series of Yes/No questions. This in
turn, directs the nurse to review the relevant body systems to be examined at the next stage, the physical exam (e.g. chest
exam). Once that is completed, it is envisaged that the CDSS would deliver a set of probable diagnosis (e.g. tuberculosis,
pneumonia), accompanied by a list of necessary lab tests (e.g. chest exam). The results of these labs tests are then fed back
into the system to yield a final diagnosis (e.g. pneumonia) (See Figure 2).
The decision-trees developed by ICTPH will be made for a limited list of diseases that are a) most relevant to a rural setting,
b) are reported as prevalent within the community by the clinic physician and c) are able to be diagnosed at the primary
care level. Diseases meeting these criteria are grouped and classified by body systems (respiratory, cardiovascular, etc).
The CDSS will only be able to diagnose this limited set of diseases, and it will recommend referrals for any disease that falls
outside of its scope.
EHR
A similar methodology to that used for the creation of CDSS protocols was employed for developing protocols that identify
individuals/communities at risk of developing diseases due to environmental conditions around them. The proposed starting
point for the inclusion of public health aspects is the collection of additional information regarding individual patients and
community level data (Kukafja et al., 2007). It is important that needed data be collected at a single time and then be reused
rather than collected repeatedly by different users (Chute and Koo, 2002). If data is collected at multiple points in time by
different groups of people certain amount of uniformity of data collection is lost. As a result it would become difficult to
compare the information collected. By gathering necessary data at a single point in time by one group, instances of data
entry errors and confusions would be reduced.
In order to gather appropriate data that would be easy to input into the EHR system, as well as easy to quantify, it is imperative
to minimize the instances of open-ended questions. As such, decision trees (Annex #3 & #4) will be utilized that would take
the user from various individual and community level environmental factors (i.e. cooking/drinking water, toilet use, trash
policy, etc.), ultimately down to diseases individuals and communities would be at risk for. The environmental public health
component will start with the water, waste system, sewage system, and indoor air pollution factors. After a series of levels
of questions, certain combinations of results will ultimately lead the respondent household or community to be susceptible
to gastrointestinal (GI) diseases and upper respiratory infection (URI), the two health problems greatly affecting Tamil Nadu
rural populations (ICTPH Baseline Epidemiological data, 2008).
The decision tree is formed with the aid of multiple choice type questions, prompting the user to choose from a list of
responses ranging from simple yes/no options to more complex choices (i.e. regarding cooking practices: in the home,
separate building, outdoors, or other). Certain answers will ultimately lead down to an at-risk conclusion whereas others will
not cause households or communities to be at risk for health problems. To be able to utilize these decision trees, two surveys
were created (Annex #1 & #2), one developed to be administered to communities and another to individuals. These surveys
will essentially direct respondents through the decision tree, and will provide ICTPH with information that will enable the
EHR system to quantify the results.
The community-level survey was created with the intention of having the local government, and/or focus groups in the
village as respondents. The community survey will identify problem areas in the hamlet (each village is divided into a
number of smaller regions called hamlets) (See Figure 3). This information will enable ICTPH the opportunity to see the
most predominant environmental concerns in each hamlet that put their respective population at-risk for GI and URI.
ICTPH will then be able to intervene at the hamlet level to implement initiatives that tackle these health concerns before
symptoms arise.
For example, an initial question would be asking the hamlet where they receive their main source of drinking water from:
surface water, piped water in home/yard, outdoor community water pump, well, or other. If the respondent community
answered surface water the decision tree would prompt them to a new series of questions and would then ask them if the
surface water is used for any other purposes. Response choice to that question would be: Defecating, bathing, washing
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clothes. If they then choose defecating, these total responses would lead to the conclusion that the hamlet population is at
risk for GI.
For the household level survey, questions were designed to be directed towards the main woman of the household, who
would have more in depth knowledge of the situation at their home. These household level surveys would be conducted by the
SughaVazhvu guides who will take the surveys door to door in the villages and administer them to households. This
information will be inputted into the EHR system and will be displayed in the patient profiles as a separate tab, alongside
family history, prescription history, etc. The survey results will provide the clinic with a list of villagers that would be at
risk for GI and URI. This information will allow them the opportunity to implement preventative interventions to needed
individuals (See Figure 4).
Figure 3: Public Health Component of EHR: Hamlet-Level Decision Tree
Survey conducted at
hamlet-level
Yes/No questions
asked regarding hamlet
environmental factors
(Ex. Main source of
drinking water?
SURFACE WATER)
Response to question
will lead to relevant
series of new questions
(Is surface water used
for any other purpose?
DEFECATION)
Results will allow ICTPH
profile hamlets for
environmental area of
concern
Total responses will lead
to conclusion regarding
hamlet at-risk status for
GI/URI (Hamlet at-risk
for GI)
For example, one question would be asking the household where they cook their food: In the home, separate building,
outdoors, or other. If the household responded in the home, the decision tree would prompt the household to a new series
of questions and would then ask them if there is any ventilation system. If the household does not have a ventilation system,
wood is used, and people are frequently around when cooking takes place, this culmination of responses would lead to the
conclusion the household is at risk for URI.
Figure 4: Public Health Component of EHR: Household-Level Decision Tree
Survey conducted at
household
Yes/No questions asked
regarding household
environmental factors
(Ex. Do you cook
indoors? YES)
Response to question
will lead to relevant
series of new questions
(Any ventilation
system? NO)
Results will be
incorporated into patient
profile and will provide
clinic with list of at-risk
villagers
Total responses will lead
to conclusion regarding
household at-risk status
for GI/URI (Household at
– risk for URI)
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Improving and Expanding Electronic Health
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Data collection/use
CDSS
Based on consultations with the clinic doctor, it was decided that the initial decision-trees for the CDSS should focus on
the cardiovascular system, respiratory system, and gastrointestinal system as most patients presented to the clinic with
diseases pertaining to one or more of these systems. Data required for the formation of decision-rules represented by
the decision-tree were collected through interviews with the clinic doctor and an ICTPH staff doctor, as well as through
review of medical textbooks. For example, when designing the decision-tree for the cardiovascular system, the physician
first provided symptoms relevant to this system (e.g., chest pain, dyspnea, syncope). From there, the physician described
the physical exams (e.g., palpation, auscultation) and laboratory tests (e.g., echocardiogram, electrocardiogram)
required to probe into the nature of these symptoms. The potential results of this objective portion of the visit were
then described (e.g., mid-systolic ejection, widely split S2) and a diagnosis for each outcome was given (e.g., pulmonic
outflow obstruction). In this way, flow-charts for various cardiovascular diseases were created based on the answers to
yes/no questions.
Incorporating a Public Health Component into the EHR
Data required to assist us in developing the public health survey, and, more specifically, the decision tree for the environmental
factors was obtained through a series of interviews and conversations with the clinic doctors, nurses and the local panchayat
office. The discussions with the clinic alerted us to what predominant health concerns should be the focus of our protocols.
We conducted literature searches regarding the linkage between various environmental factors present in rural India and the
at-risk health concerns.
Our discussion with the local panchayat office provided us the knowledge to expand on the questions for the environmental
survey, as well as what other issues/factors need to be looked at, such as trash burning practices and water quality reviews.
We conducted numerous field visits to Alakkudi to view and observe the various environmental factors in the village which
also gave us a better understanding of what additional information would need to be incorporated into the proposed surveys,
such as the local indoor cooking practices.
Conclusion
Expanding EHR to include both public health and CDSS components has the potential to improve healthcare in rural
settings. The dearth of physicians in these areas as well as inadequate medical training offered in rural settings creates
a need for an enhanced EHR system. CDSS with diagnostic capabilities can increase standardized care and assist nurses
in making evidence-based decisions. The public health addition to the EHR, coupled with environmental interventions
at the community and household levels, adds a preventive focus that could have an impact on long-term patient
outcomes.
Limitations
Implementing both a public health focus to an EHR system and a CDSS in a rural clinic is an endeavor not previously
attempted. As such, very little literature and background information is available that will lead to a definitive conclusion
regarding the effectiveness of these initiatives in rural settings. Technology is a key component to implementing these EHR
expansions, which often times is not available to the needed degree in certain rural areas. Similarly, the effectiveness of these
EHR additions hinges on the training of the nurses using the system. Nurses will need to be comfortable with computers
and will need a comprehensive understanding of the system’s use and utility. The public health component of the EHR, as of
yet, does not provide an at-risk score for households and hamlets. Its utility will only come to fruition once definitive at-risk
conclusions for all questions and response choices addressed on the surveys and decision trees have been given. These atrisk
scores will be needed in order to bridge the survey results to the EHR system. The surveys, in turn, should be piloted in
the village setting before being incorporated into the EHR system.
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IKP Center for Technologies
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Future directions
CDSS
Decision-trees have been constructed for the respiratory and cardiovascular systems, but will need to be expanded to
include other body systems. A panel of physicians--ideally physicians with knowledge of rural health issues— should review
the decision-trees that have been created to ensure their accuracy and comprehensiveness. These decision-trees will need to
be coded so that computer programmers may translate the yes/no options into CDSS software. Eventually, protocols for the
management of certain diseases, as well as protocols developed to guide nurses during the physical exam will be integrated
into the CDSS.
PH-EHR
The pilot environmental surveys created need to be verified and finalized by professionals with a more thorough environmental
health background who are well versed in the factors that plague rural India specifically. Once needed individuals with
appropriate backgrounds are assigned to the project an at-risk scoring system needs to be added to the decision tree that
will link all answer choices to at-risk conclusions. Ultimately scores will need to be assigned to all responses in which higher
scores will be assigned to factors that are more likely to cause individuals and communities to be at-risk for GI and URI. This
will lead to each household and hamlet being assigned a single at-risk score.
Once this step is taken ICTPH should conduct the surveys in the villages where they operate, both at the household level and
community level. After which the data gathered would need to be entered into the EHR system. A module, more specifically,
an algorithm that would translate the decision trees into risk scores needs to be created. The community level data will need
to be quantified to discover the most prevalent environmental factors occurring in villages that will cause the population to
be at risk for GI and URI. ICTPH can then began to brainstorm what effective and feasible policy changes they can reasonable
work on to implement with the local governments.
The hamlet survey will lead to an environmental profile of the hamlets which can be incorporated into the EHR system. So,
for example, if multiple patients from Hamlet X present with GI/URI symptoms at the clinic the system will alert them to this
increased incidence of GI/URI in Hamlet X. The clinic can then pull up the profile for Hamlet X on the EHR to see the prevalent
community environmental risk factors there. This information will assist ICTPH in developing large-scale community level
environmental interventions where needed.
The organization should work to create individual interventions that will tackle the various environmental factors that
are affecting households. These interventions need to be linked to responses to the individual surveys so once a patient
enters the clinic, their household survey responses will be displayed on the screen and the necessary interventions will
also be displayed. This will lead the nurses to conduct the individual interventions to patients. If ICTPH is able to change
and implement environmental policy at a community level, testing will need to be conducted to document changes in
water quality, water policy, sewage and waste practices, etc. Follow-up will also need to be conducted regarding patient
compliance with the created initiatives. As such, evaluation of all initiatives need to be carried out to document changes
in population health.
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Improving and Expanding Electronic Health
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Annex 3
Community-Level Environmental Survey
Pilot Survey
By: Urmi Cholera & Jessica Wolff
Community-Level Environmental Survey: WATER
1. Where do most villagers obtain drinking/
cooking water from?
Surface Water (River/Lake/Pond) (GO TO Q2)
Piped Water in yard/home (GO TO Q4)
Outdoor community water pump (GO TO Q4)
Well (GO TO Q8)
Other
2. Is this surface water used for anything else? NO (GO TO Q3)
YES (Check relevant boxes below then go to Q3)
Defecating
Bathing
Clothes Washing
3. Are there any trash piles located near the
surface water source?
NO
YES
4. What is the source of the piped water? Surface water (River/Pond/Lake) (Answer Q2-3)
Treated well water (GO TO Q5)
Untreated well water
5. How is well water treated? Chlorine tablets/powder
Bleach
Water filter (ceramic/sand/composite)
Electric purifier
Other
(GO TO Q6)
6. How often is water treated? Once a day
Once a week
Once a month
Longer
Unsure
(GO TO Q7)
7. Is well water quality checked by an official? NO
YES-How often? (Mark answer below)
Once a week
Once a month
Longer
8. What is the status of well? (Check all boxes that apply)
Protected/Covered
Unprotected
Treated water (GO TO Q5)
Untreated water
Trash thrown in well
Clean/Well maintained
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IKP Center for Technologies
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Community-Level Environmental Survey: SEWAGE
1. Where do villagers mainly defecate? Private toilet in household (GO TO Q4)
Public toilet (GO TO Q2)
Public outdoor space (GO TO Q3)
2. What is the public perception of the toilets? (Check all boxes that apply)
Clean
Dirty
Frequently Used
Rarely Used
Functioning
Non-functioning
Convenient distance
Inconvenient distance
(GO TO Q4)
3. Where is the public outdoor defecation space
located?
4. What is the status of the village sewage
system?
(Check all boxes that apply)
Near open water source
Near homes
Near public space (people)
Not near people or water
(Check all boxes that apply)
Covered
Uncovered
Located in front of houses
GO TO Q5
5. Is waste removed from the sewage system? NO
YES (if yes, check relevant boxes below & GO TO Q6)
Once per week
Once per month
Longer
6. Where is the removed waste deposited? (Check all boxes that apply)
In village
More than 1 KM outside village
(GO TO Q7)
7. Do villagers frequent the site with deposited
sewage?
8. Do villagers live near the site with deposited
sewage?
9. Is the deposited sewage site located near an
open water source?
YES
NO
(GO TO Q8)
YES
NO
(GO TO Q9)
YES
NO
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Improving and Expanding Electronic Health
Records in a Rural Setting: Clinical Decision
Support System & Public Health Component
Community-Level Environmental Survey: WASTE
1. Where do most villagers dispose of trash? (Check all boxes that apply)
Open ditch behind house
Designated trash disposal sites within village
Open ditch in front of house
Other (please specify)
(GO TO Q2)
2. Is trash ever collected and deposited in a
landfill?
3. How often is trash collected and deposited in
a landfill?
YES (GO TO Q3)
NO
Once per week
Once per month
Longer
(GO TO Q4)
4. Where is the landfill located? Within the village
More than 1 KM away from village
(GO TO Q5)
5. Do villagers frequent the site with deposited
waste?
6. Do villagers live near the site with deposited
waste?
7. Is deposited waste site located near open
water?
YES
NO
(GO TO Q6)
YES
NO
(GO TO Q7)
YES
NO
(GO TO Q8)
8. Do villagers ever burn trash in village? NO
YES (If yes, how frequently? Mark below)
Frequently, Once or twice a week
Sometimes, Once or twice a month
Infrequently less than once a month
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IKP Center for Technologies
in Public Health
Annex 4
Household-Level Environmental Survey
Pilot Survey
By: Urmi Cholera & Jessica Wolff
Household-Level Environmental Survey: WATER
1. Where do most villagers obtain drinking/
cooking water from?
2. How long does it take to go there, get water,
and come back in one trip?
3. For how many hours during the day is this
water source available?
Surface Water (River/Lake/Pond)
Piped Water in yard/home
Outdoor community water pump
Well
Other
(GO TO Q2)
15 Minutes
30 Minutes
On the premises
Don’t know/Can’t say
(GO TO Q3)
Entire day
Available only in the mornings
Available only in the evenings
Available sporadically/changes daily
Don’t know/Can’t Say
(GO TO Q4)
4. Is there ever a water shortage? YES (GO TO Q5)
NO (GO TO Q6)
Don’t know/Can’t Say (GO TO Q6)
5. When there is a shortage, where does your
family obtain water from?
6. Does your household treat your water in any
way to make it safer to drink?
7. What does your household usually do to the
water to make it safer to drink?
Surface Water (River/Lake/Pond)
Piped Water in yard/home
Outdoor community water pump
Well
Other
(GO TO Q6)
YES (GO TO Q7)
NO (GO TO Q8)
Don’t know/Can’t Say (GO TO Q8)
Chlorine tablets/powder
Bleach
Water filter (ceramic/sand/composite)
Electric purifier
Boil
Use alum
Strain through a cloth
Water filter (ceramic/sand/composite)
Electric purifier
Let it stand and settle
Other (please specify)
(GO TO Q8)
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Improving and Expanding Electronic Health
Records in a Rural Setting: Clinical Decision
Support System & Public Health Component
Household-Level Environmental Survey: WATER
8. What is the main source of water used by
your household for other purposes such as
washing and bathing?
Surface Water (River/Lake/Pond)
Piped Water in yard/home
Outdoor community water pump
Well
Other
9. Does your household grow their own crops? YES (GO TO Q10)
NO
10. What water is used for irrigation of these
crops?
Surface Water (River/Lake/Pond)
Piped Water in yard/home
Outdoor community water pump
Well
Wastewater
Other
Household-Level Environmental Survey: SEWAGE/WATER
1. What kind of toilet facilities do members of
your household usually use?
Private toilet within house (GO TO Q2)
Public toilet (GO TO Q2)
No facility/uses open space or field (GO TO Q5)
2. Where does the waste from this toilet go? Flushed to piped sewer system
Flushed to septic tank
Flushed to pit latrine
Flushed to somewhere else
Flushed, don’t know where
Unprotected well
Ventilated improved pit
Pit latrine with slab
Pit latrine without slab/open pit
(GO TO Q3)
3. Does your household share this toilet facility
with other households?
YES (GO TO Q4)
NO (GO TO Q5)
4. How many households use this toilet facility? Less than 10 households
More than 10 households
Don’t know/can’t say
(GO TO Q5)
5. Where does your household dispose
household waste?
Open bins within compound
Open bins outside compound
In open yard within compound
In open yard outside compound
Burn the waste
Other (please specify)
(GO TO Q6)
6. Is there an open gutter near the house? YES
NO
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IKP Center for Technologies
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Household-Level Environmental Survey: INDOOR AIR POLLUTION
1. What type of fuel does your household mainly
use for cooking?
2. In this household, is food cooked on a stove,
chullah or an open fire?
Electricity
LPG/Natural gas
Biogas
Kerosene
Coal
Charcoal
Wood
Straw/shrubs/grass
Agricultural crop waste
Dung cakes
Others (please specify)
(GO TO Q2)
Stove
Chullah
Open fire
Others (please specify)
(GO TO Q3)
3. Is cooking done under a chimney? YES
NO
(GO TO Q4)
4. Is cooking done in the house, in a separate
building or outdoors?
5. Are children and other family members
frequently present while cooking is taking
place?
In the house
In a separate building
Outdoors
Others (please specify)
(GO TO Q5)
YES
NO
(GO TO Q6)
6. Is anyone in your household a smoker? YES
NO
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