1.
Introduction
Social determinants of health-SDOH
help healthcare providers comprehend the social context of their patients and
tailor interventions accordingly (Ahmadi et al., 2022). SDOH has
important implications for the design and implementation of health information
systems using EMR. Incorporating a comprehensive set of SDOH features in EMR
can improve risk prediction models and enhance patient care and outcomes (Yang
et al., 2023), (Shane et al., 2023). By implementing a national EMR system,
countries can have real-time access to valuable information, enabling better
patient record management, clinical care, and health
policy-making (Gauchan et al., 2022). Acknowledging all the benefits of
incorporating, the SDOH in EMR, still, the adoption of one of the SDOH
frameworks is essential to improve the health outcome. One of the well-known
frameworks that could be used to study the social determinants of health in
digital interventions is the World Health Organization-WHO Commission on Social
Determinants of Health-CSDH conceptual framework. This framework emphasizes the
development of new and refinement of existing digital health interventions that
target intermediary, social, and/or structural determinants of health
(Stephanie et al., 2022). Another important model, the social-ecological model
recognizes that social risk and protective factors exist across different
domains and levels, such as physical disability, household context, and
neighbourhood environment (kim et al., 2022). The lack of standardized data
about SDOH in EMRs hinders efforts to coordinate care and identify
SDOH (Mira et al., 2022). Additionally, there is a deficit in recording
SDOH in computerized medical histories, with professionals tending to focus on
clinical diagnoses rather than social determinants (Kollapally et al., 2022).
These limitations highlight the need for improved integration of SDOH features
in EMRs and standardized documentation practices to better address the impact
of SDOH on health outcomes.
In Palestine, the EMR was
implemented in 2011 in the West Bank including 15 public hospitals: Two
Hospitals in Ramallah: Palestinian Medical Complex and Hugo Chávez, one
hospital in Jericho, one hospital in Salfit, two hospitals in Nablus: Alwatani,
Rafedia surgical hospital, one hospital in Qalqelia Darwish Nazzal, one
hospital in Tubas, one hospital in Jenin, one hospital in Tukaram, three
hospitals in Hebron- Alia, Doora, Yetta, Halhul, Bethlehe-Biet Jala. There are
also 12 Primary healthcare clinics-PHC (Ramallah directorate, Biet Reema and
Nileen, Nablus Directorate and Balatta clinic, Qalqelia directorate and Azzun
clinic, Hebron: Middle Hebron Center, Karantena, Tarqumia, Um Ad-Dalia). The
EMR covers the health services in the hospitals including the following
departments in the hospitals: Admission, inpatient, outpatient, Emergency
department, operation room, pharmacy, radiology department, laboratory
department, haemodialysis unit, and accounting department. The EMR connected
all 15 Palestinian hospitals and 12 PHCs through using a unique identifier, the
Palestinian Identification number. Many technical reviews were performed to
evaluate the quality of data, but none of these focused on evaluating SDOH
framework availability. Therefore, it’s essential to map the availability of
SDoH based on a clear framework rather than embedding ad-hoc SDOH DE within the
EMR. However, this study aims to review and map the existing EMR DEs in the
Palestinian public hospitals and PHC clinics in the West Bank based on the WHO
CSDH conceptual framework. By proposing a new SDoH DE, that by the addressing
and capturing of patient data the health research will be augmented.
2.
Method
A descriptive-analytical study was performed by
reviewing the DEs, datasets, and metadata at governmental EMR implemented in 15
hospitals in the West Bank. These metadata were mapped and reviewed in the
context of the WHO CSDH. The mapping process started with ethical and
administrative approval of the MOH scientific committee where all procedures comply
with relevant data protection rules and regulations. The metadata was extracted
from the EMR forms and software screens. All forms and screens were listed in
an Excel file to be mapped with SDoH. The review process followed Walt and
Gilson's policy analysis framework (Walt et al., 2008). A policy review
analysis was conducted by examining the data elements, datasets, and metadata
within government-implemented EMR systems across 15 hospitals in the West Bank,
focusing on content, context, and process.
Context: The metadata were
mapped and reviewed in line with the WHO CSDH framework.
Process: The mapping
process began with obtaining ethical and administrative approval from the MOH
scientific committee, ensuring all procedures complied with relevant data
protection rules and regulations.
Content: Metadata was
extracted from EMR forms and software screens. All forms and screens were
catalogued in an Excel file for mapping with social determinants of health
(SDoH).
2.1
Study Design
A cross-sectional study was implemented to
systematically review, and map metadata extracted from the backend of EMR. The
focus is on examining the availability of metadata that is linked with SDoH.
The data extraction was performed in June-July 2023 from Avicenna Hospital
Information system version number (2.3.10r_SEHA) which was developed by
DataSell company-Turkiye. Figure 1 shows one of the screenshots taken from the
EMR for review and mapping.
2.2
Data Extraction
The metadata extraction process involves retrieving
metadata related to patient records, encompassing a range of variables such as
patient demographics, diagnostic codes, treatment history, and other relevant
details. The metadata has been strictly compiled and assessed with a focus on
the various departments and functions within the hospitals and PHC centres.
These encompass registration and insurance, inpatient services, outpatient
services, emergency room, operating room, pharmacy services, radiology
procedures, and laboratory procedures including haemodialysis. The blood bank,
pathology department, referral procedures, and cashier department have been
thoroughly considered, along with their associated sub-screens.
The extracted data are compiled into a comprehensive
master file for further analysis according to the following stepwise approach:
(1) Categorization: According to SDoH, the extracted
metadata are categorized into upstream and downstream of SDoH. Upstream factors
include broader societal and structural determinants, while downstream factors
encompass individual-level influences. This categorization is crucial for
understanding the contextual layers of health-related information within the
EMR. (2) Mapping Process: Two independent reviewers are engaged in the review
and mapping process. Each reviewer assesses the extracted metadata and data
elements based on their relevance to SDoH. The reviewers work separately to
ensure unbiased evaluations. (3) Confirmation of Lists: Each reviewer collates
the identified metadata data and data elements into provisional lists. These
lists are then cross-verified and confirmed through a consensus-building
process. (4) Confirmed items are considered for further analysis, while any
discrepancies or conflicts are flagged for resolution. (5) Conflict Resolution:
When conflicts arise between the two reviewers regarding the inclusion or
exclusion of specific metadata, a joint discussion is facilitated. Both
reviewers engage in a comprehensive dialogue to resolve disagreements,
considering the context, relevance to SDoH, and any additional information that
may contribute to the decision-making process. (6) Final List Generation:
Following the resolution of conflicts and the confirmation of metadata, data,
and data elements, a final list is generated. This list represents the mapped
metadata and data associated with SDoH within the EMR system. Data mapping
(Matching) is employed to analyse the final list of mapped metadata data. This
includes summarizing the frequency and distribution of metadata within the upper
and downstream of SDoH. (7) Ethical Considerations: The study adheres to
ethical guidelines, ensuring the confidentiality and privacy of patient data.
(8) Limitations: Potential limitations, such as the generalizability of
findings and the accuracy of metadata extraction, are acknowledged. Steps are
taken to mitigate biases and inaccuracies throughout the study.
Fig. 1:
Screenshot from the Palestinian EMR (PRS001) Patient registration screen.
Version 2.3.10r. June 2023
3.
Conceptual framework
The conceptual framework in this study was based on
the Commission on Social Determinants of Health conceptual framework (Solar
& Irwin, 2010). This model helps to generate and develop new data elements
with the EMR design. The Commission’s report recommended three principles of
action: (1) Improve the conditions of daily life – the circumstances in which
people are born, grow, live, work, and age. (2) Tackle the inequitable
distribution of power, money, and resources – the structural drivers of those
conditions of daily life – globally, nationally, and locally. (3) Measure the
problem, evaluate action, expand the knowledge base, develop a workforce that
is trained in the social determinants of health, and raise public awareness
about the social determinants of health. These three recommendations require
patient data that is generated from EMR and could be used in several
interventions and policies. A list of independent variables was introduced to
help understand and capture the SDOH data within the EMR.
Fig. 2: Proposed
variables for EMR design derived from social ecology of health model SDOH
4.
Main Findings
Table 1: Example from
Mapping process from EMR Registration screen
|
Patient Info.
|
SDOH Availability
|
|
Patient #
|
0
|
|
Patient Name
|
0
|
|
ID
|
0
|
|
Age
|
0
|
|
Gender
|
0
|
|
Phone
|
0
|
|
Blood Group
|
0
|
|
Visit Info.
|
0
|
|
Visit #
|
0
|
|
Visit Date
|
0
|
|
Status
|
0
|
|
Referral Place
|
0
|
|
Sponsor
|
0
|
|
Agreement
|
0
|
|
Visit Type
|
0
|
|
ER Status
|
0
|
|
Agreement Status
|
0
|
|
Visited Place
|
0
|
|
Expected Discharge Date
|
0
|
|
Note
|
0
|
|
Referral Source
|
0
|
|
Mode of Visit
|
0
|
|
Receiving Patient No.
|
0
|
|
Physician
|
0
|
|
Ref. Physician
|
0
|
|
Department
|
0
|
|
Date
|
0
|
|
appointment type
|
0
|
|
Total SDOH Data
Elements
|
0
|
After
reviewing over four hundred DEs across the EMR screens, a subset of SDoH DEs
emerged, primarily encompassing variables related to patient addresses and
health insurance types. The examination uncovered several notable findings: a
notable absence of DEs dedicated to capturing and monitoring SDoH, an absence
of a clear classification for downstream SDoH variables, and a concentration on
medical and administrative DEs. Specifically, the medical-oriented variables
centred on patients' medical, surgical, and medication histories, with a
conspicuous lack of SDoH DEs in crucial sections like patient history,
treatment plans, nurses' notes, physicians' notes, medical consultations, and
orders. Another noteworthy gap was observed in public health and SDoH aspects
during patient admission and registration, where the focus was solely on
insurance details, financial information, residency, telephone numbers, and
general demographic information. The existing EMR metadata were predominantly
oriented toward medical and economic aspects rather than encompassing public
health DEs.
Table 1 unmistakably illustrates the absence of SDOH
DEs across the reviewed screens in the EMR. The review extended to mapping the
health data dictionary used for developing the metadata. Findings indicated
that the metadata definitions were sourced from the Palestinian health data
dictionary, established in 2005. However, these metadata were not internally
defined or described within the EMR system. Furthermore, the metadata lacked a
clear classification or taxonomy. Documentation of metadata existed minimally
within the system, with no accompanying support documents explaining metadata
definitions, purposes, codes, sources, uses, types, and future feasibility of
interoperability. The primary results are organized based on the structure of
metadata as outlined in the Palestinian National Health Data Dictionary,
Version 2, MOH (2005), serving as the sole local source in Palestine (MoH,
2005). The main characteristics of EMR DEs were as follows: (1) Metadata
Definitions: lack of clearly defined metadata elements used in the EMR
system including lack of terminology, units of measurement, and any relevant codes.
(2) Meanings and Context: lack of explanation of the meaning and
significance of each metadata element. The considerations include a lack of
information about how the data is collected, its clinical relevance, and any
specific guidelines for interpretation. (3) Relationships between Metadata
Elements: Lack of explanation of the relationships and dependencies between
different metadata elements. This includes illustrating how certain elements
are linked or how changes in one element may affect others—in addition, to
providing clear information on how metadata elements interact with each other
to support a holistic understanding of the data within the EMR system. (4) Data
Standards and Formats: Lack of standards or formats for each metadata
element, including information about the permissible values, data types, and
constraints for each metadata element. (5) Updates and Versioning: Lack
of documentation of any changes or updates made to the metadata definitions
like maintaining a version history to track modifications over time. (6) User
Guidance: Lack of guidance for users on how to interpret and use the
metadata effectively. (7) Accessibility: The EMR documentation is
Partially accessible to the stakeholders, including healthcare providers,
administrators, and IT personnel.
5.
Recommendations:
- Develop metadata definitions,
meanings and context, relationships between metadata elements, data
Standards and formats, updates and versioning, user guidance, and
accessibility documents.
- Develop and incorporate the following
SDOH Data Elements within the registration, medical history, and admission
screens with the EMR:
2.1 Housing Stability: Housing
Type (e.g., apartment, house, shelter, camps), Living Conditions (e.g.,
overcrowded, safe), Change in Housing Status (yes/no), Date of Change in
Housing Status, Housing Assistance Program Referrals.
2.2 Food Security: Food
Security Status (e.g., food-secure, food-insecure), Nutritious Food Assessment
(yes/no), Automatic Referral to Food Assistance Program (yes/no), Nutrition Counselling
Referral (yes/no).
2.3 Income and Employment: Employment
Status (e.g., employed, unemployed, underemployed), Monthly Income, Workforce
Development Agency Referrals, Income-Related Health Disparities Trends.
2.4 Education and Literacy: Education
Level/ Literacy Level, Tailored Health Education Materials, Adult Education
Program Referrals.
2.5 Transportation: Transportation
Barriers Data, Coordination with Transportation Services (yes/no), Missed
Appointments Due to Transportation (yes/no).
2.6 Social Support Systems: Social
Support Network Details, Identified Need for Social Services (yes/no), Support
Group or Caregiver Assistance Referrals.
2.7 Cultural Competency and Language: Preferred
Language, Cultural Background, Interpreter Services Requirement (yes/no), Cultural
Competency Training Records.
2.8 Behavioural Health and Substance Abuse: Behavioural
Health Assessment Results, Substance Abuse Risk Assessment, Behavioural Health,
and Substance Abuse Referrals.
2.9 Analytics and Reporting: SDoH
Dashboard, Patterns and Disparities Identification, Anonymized SDoH Data
Sharing with Public Health Agencies.
2.10 Patient Demographics: Patient
Name, Date of Birth, Gender, Address, and Contact Information.
6.
Discussion
The findings of this study underscore the critical
importance of addressing SDoH EMR to enhance healthcare delivery and contribute
to the achievement of SDGs. The identified gaps and recommendations offer
valuable insights into optimizing EMR architecture for a comprehensive
understanding of patients' social and economic contexts. Contrary to
expectations, this study did not find enough SDoH DEs within EMR screens, it is
only focusing on the patient address and health insurance type. This limitation
suggests a narrow scope in capturing the multifaceted determinants influencing
health outcomes. It is imperative to expand the range of data elements to
encompass a broader set of SDoH, as this will provide a more holistic view of
patients' lives and facilitate individual interventions. Such limitations of
SDOH might affect tracking the SDGs which is important for measuring the
development in the country. It is difficult to explain this result, but it
might be related to the lack of availability of an up-to-date health data
dictionary since the available copy of the local health data dictionary is from
2005. Therefore, it is essential to incorporate relevant DEs into EMRs for the
effective monitoring of health outcomes and individual interventions which lead
to the improvement of SDGs. While we are addressing these benefits, it is
important to consider the absence of such tracking mechanisms which hinder the
healthcare system’s ability to contribute effectively to improve health
outcomes. The study highlighted the importance of categorizing the SDOH not
only considering them in an ad-hoc manner, so the incorporation should consider
the systematic and organized SDOH. Establishing standardized classifications of
SDoH will enhance the consistency of data collection and reporting which will
improve the comparison and analysis across diverse healthcare settings. Mapping
the Social Ecological Model to EMR will provide valuable insight into various
determinants, and emphasise the need for dedicated modules. These DEs should
comprehensively capture information related to housing stability, food
security, income, education, transportation, social support systems, cultural
competency, and behavioural health. Incorporating such DEs aligns with the
recommendation for dedicated EMR sections for lower-stream SDoH. Ultimately,
the SDoH is a means, not the end, it’s a means of achieving health equity and
improving health equity. Incorporating SDHO DEs will improve capturing and
identifying vulnerable population groups and connecting patients with social
services, healthcare systems, and insurance systems as well.
Incorporating SDoH into the MER will improve referral
systems between healthcare facilities and connect patients seamlessly.
Designing the EMR architecture including SDoH will enable efficient and timely
access to relevant resources, ensuring a holistic approach to healthcare that
addresses broader socio-economic determinants. Furthermore, the proposed SDoH DEs
will improve the real-time alerts and Prediction that enhance the
identification of vulnerable populations. This proactive approach enables
healthcare professionals to intervene promptly, addressing potential health
disparities and promoting timely and targeted interventions. Finally,
it’s worth mentioning that the SDoH into EMR is an iterative process that
ensures that the EMR system remains dynamic and responsive to emerging
insights, technological advancements, and evolving global health priorities.
Several studies showed the effectiveness of
incorporation of SDoH within the EMR i.e. A study conducted by Lofters et al.
(2016) investigated the impact of including SDOH indicators in EMRs on cancer
screening rates among vulnerable populations. They found that identifying and addressing
social needs through EMRs significantly improved cancer screening rates among
disadvantaged patients (Lofters, 2017). The deployment of the Protocol for
Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE)
in an EMR revealed valuable information on SDOH prevalence and identified areas
amenable to intervention (Yan et al., 2022). Additionally, studies have
demonstrated that interventions targeting SDOH domains can lead to improved
access to resources for patients, potentially addressing social needs and
promoting health equity (Carrie et al., 2023). Furthermore, incorporating SDOH
screening into the EMR through dashboards and alerts has enabled healthcare
providers to consider SDOH factors in treatment planning, ultimately aiming to
enhance healthcare outcomes and patient-provider relationships (Braja et al.,
2021).
Future Research on Health Informatics and Social
Determinants of Health (SDOH) is crucial for shaping future population health
landscapes (Kelsey et al., 2022). By utilizing methods like data linkage and
text classification, advancements in Health Information Systems (HIS) can
capture a wide range of SDOH factors from various sources (Kelsey et al.,
2022). Understanding the impact of SDOH on health outcomes is essential, as
they play a significant role in disease development and health disparities
(Xiaoyi et al., 2023). Studies have shown that addressing SDOH barriers at the
county level can lead to effective interventions to reduce mortality rates from
conditions like cardiovascular disease and opioid use (Pavani et al., 2022).
Moreover, exploring the knowledge, attitudes, and practices of healthcare
professionals toward Community Health Workers (CHWs) can help integrate
strategies to mitigate SDOH and improve patient outcomes (Khan et al., 2022).
Overall, research in this field can inform evidence-based policies, enhance
health promotion efforts, and contribute to reducing health disparities and
improving overall population health.
Health informatics plays a crucial role in enhancing
medical research outcomes by enabling the evaluation of treatment
effectiveness, identification of drug side effects, and facilitating data
sharing for research purposes (Castro, 2009) (Peter et al., 2009). Biomedical
informatics and health information technology (HIT) platforms are instrumental
in collecting, managing, and utilizing healthcare information efficiently,
thereby supporting evidence-based practice and research initiatives (Peter et
al., 2009). Studies indicate that the adoption of health IT, such as electronic
medical records (EMRs) and computerized physician order entry systems, can
reduce mortality rates for severely ill patients, emphasizing the importance of
technology in improving patient outcomes (McCullough et al., 2001). Despite
challenges in EMR adoption, HIT tools like EMRs are designed to enhance care
quality, reduce errors, and promote efficiency in healthcare delivery,
underscoring the potential of health informatics to drive advancements in
medical research and practice (Patricia et al., 2021) (Dan et al., 2010).
However, we can’t ignore the importance of using
Health informatics solutions like EMR in capturing personalized health outcomes
and their related risk factors and SDoH. Tailoring interventions based on specific
patient characteristics and continuous tuning of treatments are essential for
individualized care (Bih, 2022). In managing hypertension, individualized
approaches are necessary, considering comorbidities like diabetes, chronic
kidney disease, and cardiovascular risks, with different treatment goals for
various patient groups, such as the elderly and young (Rajeev, 2023). These
factors collectively contribute to the customization of healthcare
interventions to meet the unique needs of everyone, enhancing treatment
efficacy and patient outcomes.
This study set out with the aim of mapping the
availability of SDoH within the structure of EMR to improve health outcomes.
The evidence proves that health outcomes improvement could be improved through
individualized intervention rather than public and community interventions.
Individualizing medication, treatment, and diagnosis in healthcare is a
fundamental aspect of precision health and personalized medicine, aiming to
tailor healthcare interventions based on an individual's unique genetic
composition, lifestyle, and environmental influences (Bih, 2023). This
approach enhances the effectiveness of treatment by predicting responses to
therapies, reducing adverse effects, and improving patient outcomes (Piyali
et al, 2022). Nurses play a crucial role in implementing precision health
initiatives by providing tailored interventions that match patients' specific
characteristics and adjusting treatments based on individual data (Bih,
2023). The development of personalized therapies is essential in addressing the
challenges posed by genetic mutations, enabling the customization of medical
treatments to suit the individual being treated (Arti et al, 2021). By
leveraging advanced technologies and a deep understanding of molecular and
genetic profiles, personalized medicine offers a promising avenue to improve
healthcare delivery and patient well-being.
In future investigations, it is highly recommended to
review the current design and architecture of the EMR. The design of the EMR
system for better health policy interventions and Sustainable Development Goals
(SDGs) alignment, is crucial to integrate embedded research methods (Abdul
Ghaffar et al., 2017) and Health Impact Assessments (HIAs) (Oriana et
al., 2029). By incorporating Health in All Policies (HiAP) principles, the EMR
should focus on interoperability (Jens et al., 2020), behavioural
economics for incentive design (Joan, 2023), and intersectoral strategies
for health promotion and sustainable development (Joy, 2020). The EMR
should facilitate data exchange between different health information systems,
prioritize research relevant to health systems, involve policymakers in
research processes, and address real-world implementation barriers. This
comprehensive approach will ensure that the EMR system not only supports
evidence-based decision-making but also contributes to achieving SDGs by
promoting health equity, policy coherence, and stakeholder engagement.
In conclusion, the findings and recommendations
underscore the need for essential reform of the EMR architecture through
prioritizing SDoH. By embracing these recommendations, healthcare systems might
contribute substantially to global health goals, advancing both individual
patient care and collective efforts toward achieving the SDGs. The integration
of SDoH into EMRs is not just a technological enhancement; it is a pivotal step
towards fostering health equity and improving the overall well-being of
populations.
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