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Ulgu et al

Implementation Report

A Nationwide Chronic Disease Management Solution via Clinical
Decision Support Services: Software Development and Real-Life
Implementation Report
Mustafa Mahir Ulgu1, MD, PhD; Gokce Banu Laleci Erturkmen2, PhD; Mustafa Yuksel2, PhD; Tuncay Namli2, PhD;
Şenan Postacı2, MSc; Mert Gencturk2, PhD; Yildiray Kabak2, PhD; A Anil Sinaci2, PhD; Suat Gonul2, PhD; Asuman
Dogac2, PhD; Zübeyde Özkan Altunay1, MD; Banu Ekinci1, MD; Sahin Aydin1, MSc; Suayip Birinci1, MD
1Ministry of Health Turkey, Ankara, Turkey

2

Software Research Development and Consultancy Corporation, Ankara, Turkey

Corresponding Author:
Gokce Banu Laleci Erturkmen, PhD
Software Research Development and Consultancy Corporation
Orta Dogu Teknik Universitesi Teknokent Silikon Blok Kat 1 No 16
Ankara, 06800
TurkeyPhone: 90 3122101763
Email: gokce@srdc.com.tr

Abstract
Background: The increasing population of older adults has led to a rise in the demand for health care services, with chronic
diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministryof Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present
the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform(DMP).
Objective: This paper’s objective is to present the design decisions taken and technical solutions provided to ensure successful
nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration
with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, andadaptability.
Methods: The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate
effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase
the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional andnational health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare
Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensureextensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters.
The DMP is horizontally scalable in case of high load to ensure high performance.
Results: As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for
16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes,
and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk,
490,346 patients with diabetes, and 144,768 patients with hypertension.
Conclusions: It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousandsof family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT
solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing
patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision
support services by seamless integration with point-of-care electronic health record systems. As the system is built on international
code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The
nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening andmanagement of chronic diseases in adherence to evidence-based clinical guidelines.
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Ulgu et al(JMIR Med Inform 2024;12:e49986) doi: 10.2196/49986
KEYWORDS
chronic disease management; clinical decision support services; integrated care; interoperability; evidence-based medicine;
medicine; disease management; management; implementation; decision support; clinical decision; support; chronic disease;physician-centered; risk assessment; tracking; diagnosis

Introduction
As in the rest of the world, the aging population is increasing
rapidly in Turkey. A recent TurkStat report predicts that by
2030, the older adult population will be 12.9%, rising to 22.6%
in 2060 and 25.6% in 2080 [1]. Noncommunicable diseases are
the leading cause of death and disability in Turkey, posing asignificant burden [2]. The elevated health costs for older adults
strain Turkey’s health care system. To address this, the Turkish
Ministry of Health (MOH) has implemented a national strategyemphasizing multidisciplinary teams, led by family physicians.
The goal is to enhance early detection and manage complicationsof noncommunicable diseases through systematic screening
programs under the national Disease Management Platform(DMP) project launched in late 2018.
The growing use of digital health solutions such as electronic
health records (EHRs) presents an opportunity to enhancechronic disease management. Clinical decision support services(CDSSs) can assist in making patient-centered andevidence-based decisions [3,4]. Digital tools and systems thatcollect and use patient information to provide decision support
for health care professionals (HCPs), including patient-specificassessments and recommendations, can promote adherence tonational guidelines, ultimately resulting in enhanced quality ofcare [5-9]. Research demonstrated that computerized decision
support tailored to the patient successfully improved
decision-making [10,11]. Such tools enhanced thedecision-making abilities of HCPs in various domains, including
effective prescription decisions [12,13], adherence to guidelines
for cardiac rehabilitation [14], management of hypertension anddiabetes [15-21], cancer screening [22,23], and computerizedorder decisions [24,25].
Building on these results, the national DMP is designed as anintegrated care platform for chronic disease management inTurkey in a family physician–centered manner. It aims toeffectively implement clinical treatment protocols, ensuring
easy adherence with decision support services. These services
focus on early diagnosis, followed by structured treatment
recommendations during routine follow-ups. The DMP enhances
standardization of care, improving health care efficiency andquality. It also facilitates seamless transitions between primarycare and specialist services, reducing costs, minimizing risks,eliminating redundant tests, and easing the burden on patients.
To ensure successful implementation of a DMP aimed atachieving these strategic objectives, several technical challenges

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need to be addressed. Our design decisions consider the crucial
factor of integrating CDSSs seamlessly into clinicians’ daily
workflow [26,27]. Despite the potential of CDSSs forevidence-based medicine, significant effort is needed to realize
these benefits [28]. The DMP must smoothly integrate withphysicians’ workflows, necessitating interoperability withexisting health IT systems. CDSS guidance should beuser-friendly, ensuring a natural flow for clinical protocol
implementation. With a target audience of over 26,000
practitioners in Turkey, serving a population of over 85 million,
the platform must ensure high performance and scalability. Itshould easily expand to address additional diseases within areasonable timeframe and prioritize reusability and compliancewith international health IT standards for versatile deployment.
This paper outlines the design, development, nationwide
implementation, and initial performance results of the national
DMP in Turkey. The DMP can be categorized as a
“2.3-Healthcare Provider Decision Support System” in termsof World Health Organization “Classification of digital healthinterventions” [29]. This implementation report will focus on
the results of the deployment and implementation of the DMPin Turkey serving to more than 26,000 family medicine
practitioners (FMPs) in the country. The objective is to share
our experiences in building the DMP, as an implementation
report in line with iCHECK-DH: Guidelines and Checklist for
the Reporting on Digital Health Implementations [30]. We detailthe design decisions and technical solutions aimed at ensuring
interoperability with existing IT systems, integration withclinical workflow, enabling smooth transition of care,user-friendliness for HCPs, scalability, and adaptability in the
Methods section. The Implementation (Results) section presents
the outcomes of the nationwide implementation (number ofusers, number of screening and monitoring encounters, numberof patients covered via these encounters, number of patientsdiagnosed as a result of screening encounters, and treatment
goal achievements [such as blood pressure targets, hemoglobin
A1c {HbA1c}, and cholesterol targets]), demonstrating how theseobjectives were achieved. Additionally, we outline current
limitations and identify areas for future work to further enhance
the clinical impact.

Methods
Overall System Architecture and Design Decisions
The DMP has been designed and implemented to enable thefollowing 4 high-level features as summarized in Figure 1:

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Figure 1. Overall aims of the disease management platform architecture. EHR: electronic health record; EMR: electronic medical record.

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Screening and risk assessment for healthy population: a
web-based platform for FMPs facilitates screening for thehealthy population. For instance, diabetes screening isrequired every 3 years for citizens aged over 40 years
without a diabetes diagnosis. The full eligibility criteria forboth screening and monitoring are presented in Multimedia
Appendix 1. The system offers personalized risk
assessments, early diagnosis, individualized goals,
preventive treatment, and lifestyle suggestions aligned withnational care pathways. Diagnosed patients enter the diseaseprogress monitoring program, whereas undiagnosed
individuals receive intensified screening based on risk andlifestyle recommendations.
Disease progress monitoring: for diagnosed patients, theplatform facilitates creating and updating personalized careplans during regular follow-up encounters, aligning withevidence-based national care pathways. It assesses
laboratory results, conducts risk assessments, recommends
personalized treatment goals and medications, suggests
follow-up appointments, and refers to specialists whennecessary for consultations and complication management.
Patients in the monitoring program are categorized based

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RenderX••on their control of clinical parameters, symptoms, and goal
achievement status, guiding decisions on follow-up
frequency, secondary care referrals, and medication plan
updates.
Self-management support for patients: a care plan with
instructions for FMPs, specialists, and patients is sharedwith Turkey’s e-Nabiz platform, the national EHR andpersonal health record (PHR) system. Patients can thenaccess details about care plan activities, including
medications, educational materials, self-measurement
activities, and lifestyle recommendations.
Population tracking: each FMP manages 2000 to 4000
patients based on their region’s population. The population
tracking module allows them to filter and manage patientsfor upcoming or overdue screening and monitoring
encounters, access statistics on the screened population,
send SMS invitations to patients, and monitor goal
achievement for clinical parameters such as fasting plasma
glucose, HbA1c, and blood pressure.

The overall system architecture of the DMP is depicted in Figure
2.

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Figure 2. High-level system architecture of the disease management platform. API: application programming interface; CDS: clinical decision support;
EHR: electronic health record; FHIR: Fast Healthcare Interoperability Resources; HL7: Health Level Seven; PHR: personal health record.

Seamless Integration and Interoperability With
Existing Systems
The DMP is designed and implemented for seamless integration
with existing regional and national health IT systems. To achievethis, we have designed the core data model and data processing
architecture of the DMP based on Health Level Seven (HL7)
Fast Healthcare Interoperability Resources (FHIR) Release 4[31]. FHIR has gained widespread adoption in the health careindustry [32-36] and endorsed by country-wide implementationsin the United States [34], United Kingdom [37], and Germany[38].
The DMP core data model conforms to HL7 FHIR Release 4
to encompass basic EHR components as well as resources forrepresenting a patient’s care plan. An open-source HL7 FHIR
Repository, namely onFHIR.io [39], serves as the main
component of the data management layer (Figure 2). onFHIR.io
uses MongoDB as a database and provides real-time datasubscription with the help of Apache Kafka. The DMP web
application directly accesses patient and care plan data through
RESTful interfaces provided by onFHIR.io, enabling
fine-grained access control over all FHIR resources incompliance with the SMART on FHIR authorization guidelines
and scopes [40].
In Turkey, the MOH operates e-Nabiz, a central national EHR
infrastructure [41]. This system collects patient records as
encounter summaries from nationwide health care providers,with patients also inputting vital signs and activity data. e-Nabiz
codes data using international and national medical terminology,
such as International Classification of Diseases, Tenth Revision(ICD-10). It is a document-based repository accessed througha Representational State Transfer application programming
interface [42], and interoperability adapters in the DMP project(EHR exchange and PHR exchange services in Figure 2)
communicate with it to retrieve patient data. These adapterstransform proprietary XML formats to HL7 FHIR-based data
models and store them in the Patient Data Repository. This
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transformation includes both structural and semantic mapping,incorporating a strategy of incremental synchronization. On
initial DMP access, the patient’s longitudinal EHR is mapped
to FHIR, and subsequent encounters retrieve and transform only
new, unsynchronized data.
To secure patient data access, clinicians authenticate to the DMP
through the MOH’s central authentication and authorization
services using the OpenID Connect protocol. The DMP uses arole-based access control mechanism, catering to different
disease management roles. Before data access andsynchronization, a check ensures that the user has the required
access rights via the MOH’s central authentication service. If
authorized, the DMP generates a patient-specific JavaScript
Object Notation Web Token with corresponding permissions,serving as an OAuth2.0 bearer token for all interactions within
the DMP.
In the DMP, FMPs perform screening and monitoring encounters
based on predefined eligibility criteria. For instance,
hypertension monitoring is required every 3 months for patientswith a hypertension diagnosis and on antihypertensive
medications. These criteria are executed in the e-Nabiz data
warehouse, and both DMP and family medicine information
systems retrieve target population lists through target population
services (Figure 2). FMPs can easily identify if a visiting patientis on the screening or monitoring list via family medicine
information systems, initiating a DMP encounter directly witha single sign-on integration.
The care plan created with the help of the DMP is stored as an
HL7 FHIR CarePlan resource in the Patient Data Repository.
It is shared with the e-Nabiz system via the Care Plan Exchange
Service (Figure 2), enabling it to be accessible to the patient via
e-Nabiz interfaces.
The DMP uses Elasticsearch technology for storing user
information, basic patient attributes, and their current screening
and monitoring statuses for each disease. Elasticsearch also
serves as a system log repository. We have developed a Kibana
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interface for monitoring system performance and geographicalstatistics. Redis is used as a caching system to temporally store
information about ongoing encounters and user authorization
access tokens.

Automation of National Care Pathways as a Clinical
Workflow for FMPs
The interfaces of the DMP have been designed with ease of use
in mind to allow for seamless integration into the regular clinicalworkflow. It is implemented as a cocreation activity with the
involvement of system analysts, software engineers, and aclinical reference group set up by the MOH Department ofChronic Diseases and Elderly Health including multidisciplinary
HCPs.
The national evidence-based care pathways have beencollaboratively analyzed, leading to the identification ofcommon steps, such as physical examination, medical history
review, risk assessment, medication review, lab results review,
diagnosis, clinical goal setting, pharmacological treatment
planning, and nonpharmacological treatment planning. Eachcare pathway is designed modularly within the DMP as a seriesof pages corresponding to these common steps. These areorganized as a flow of pages that is followed automatically
based on patient parameters.
Each page is meticulously designed, specifying patientparameters for assessment. Most data come from the national
EHR system, enabling clinicians to review prefilled pages with

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the latest parameters and make adjustments as needed. Validity
periods for each parameter are identified, emphasizing recency,
and they are enforced by the system and reminded to FMPs.
Additionally, scaled assessments (eg, Mini Nutritional
Assessment), risk assessments (eg, cardiovascular risk), andassociated algorithms (eg, SCORE-Turkey) are also identified.
Business rules within the pages are designed for personalized
suggestions aligned with evidence-based care pathways.
All of these are thoroughly documented after discussions incocreation workshops. Mock-up screens are designed, and flow
diagrams are created to identify transition criteria between pages.
These materials undergo further review and finalization insubsequent cocreation workshops. As an illustrative example,
Figure 3 depicts a sample flow for hypertension screening.
After cocreation, each step’s design becomes a web-based
interface in the DMP application, developed with the Angularframework. A “Pathway Execution Service” state manager
automates the flow diagram for disease screening or monitoring,
adapting to patient parameters. This allows FMPs to use awizard-like interface for encounters, facilitating adherence tonational clinical pathways.
Transitions between disease modules are also modeled andimplemented. For example, in hypertension screening, if apatient’s fasting plasma glucose exceeds 110 mg/dL, the systemprompts FMPs to consider a diabetes screening if not alreadymonitored for diabetes. In response, the patient’s diabetes
screening schedule is automatically updated.

Figure 3. Hypertension screening flow. BP: blood pressure.

CDSS Implementation
CDSSs are a core component of DMP to enable patient-tailored
recommendations. On the basis of the documented businessrules from the design phase, we have designed CDSSs as
automated processes. These processes link patient-specific datawith evidence-based knowledge from national care pathways.
We can categorize the CDSS implemented based on theirfunctionality as follows:
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Risk assessment via scored algorithms (eg,SCORE-Turkey): FMPs are provided with explanatory
guidance about scoring, referencing validated scoringassessment algorithms (see Figure 4).
Diagnosis recommendations based on the patient’s current
condition and risk assessment: in screening operations, theCDSS recommends diagnoses to FMPs using predefinedICD-10 codes.

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Guidance for lab test ordering and interpretation: apersonalized list of required lab tests is determined basedon the patient’s disease state, risks, and other comorbidities.
The CDSS also provides notifications for when these lab
tests should be renewed on expiration.
Diagnosis and referral suggestions are recommended basedon patient parameters such as lab results. For example,
referral to a nephrologist is recommended when theestimated glomerular filtration rate result is below 60
mL/min/1.73 m2.
Treatment goals (eg, low-density lipoprotein cholesterol)
are recommended based on the patient’s risk, disease stage,
and comorbidities. In Figure 5, an example screen for goalplanning is presented. The physician can always manually
update these targets based on their assessments.
Medication suggestions are recommended for treatment
planning based on disease stage, response to previous
medications, existing medications, and comorbidities.
Certain medications are marked as contraindications basedon the existing comorbidities of the patient.
Referral suggestions for preventive consultation visits arerecommended, especially for complication management.
For instance, a yearly retinopathy check with an

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ophthalmologist is advised during diabetes monitoring
encounters.
Follow-up visits are recommended based on the current
status of the patient. For instance, screening in each 2 years
is suggested for patients with low cardiovascular diseaserisk, whereas once a year screening is suggested forhigh-risk patients.
Automated care pathway transitions for patients withmultiple morbidities are personalized based on specificdisease criteria. For instance, if a patient aged over 40 years
has not had their cardiovascular risk score calculated, theDMP guides FMPs to continue with the cardiovascular risk
module during hypertension or diabetes monitoring.

In the DMP, all CDSS implementations adhere to the CDS
Hooks specifications [43]. As a standard published by HL7, it
provides an API specification for CDS calls. Both input
parameters and output suggestions are defined in reference to
HL7 FHIR resources, facilitating plug-and-play interoperability
with platforms that support HL7 FHIR. The CDS
Hooks–compliant approach allows easy expansion with CDSSs
created by external entities and to simplify deployment indifferent settings already adhering to HL7 FHIR.

Figure 4. An example screenshot from the Cardiovascular Risk Screening Module presenting individualized risk calculation. (The system is implemented
in a multilingual manner supporting Turkish and English by default.) CVD: cardiovascular disease.

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Figure 5. An example screenshot from the disease management platform presenting personalized lipid goals for the patient. HDL: high-density
lipoprotein; LDL: low-density lipoprotein.

Ensuring Performance of the System
The DMP is designed for high horizontal scalability, using 2
servers for the web application and 3 for the Patient Data
Repository, forming an onFHIR.io server cluster. Nginx acts
as both a reverse proxy and a load balancer to distribute traffic
across these backend servers. onFHIR.io servers connect to ahorizontally scalable MongoDB cluster for data distribution
and replication. Elasticsearch log and data store operate on acluster hosted on 6 servers.

Testing, Piloting, and Deployment of Disease Modules
The system is developed by SRDC on behalf of the Turkish
MOH with the support of Türksat and Innova. The final productis owned by the Turkish MOH. The initial version of the DMP,
including modules for screening and monitoring of type 2
diabetes, hypertension, and cardiovascular risk management,
was extensively tested by the clinical reference group. It
underwent a 3-month pilot phase in 4 cities in late 2019. The
pilot phase involved 14,351 encounters conducted by 219 FMPs
for 5521 patients. Two more modules for obesity screening andmonitoring, as well as older adult monitoring, were added tothe system during this period. After the feedback is addressed
and the system is retested, the system has been operationalized
in whole Turkey by January 2020. On June 30, 2021, the MOH
has published a directive incentivizing FMPs to conduct
screening and monitoring for diabetes, hypertension,
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cardiovascular disease risk management, obesity, and older
adult monitoring via the DMP. The system with incentivization
calculations has been operational in whole Turkey since July
1, 2021.
Beyond existing modules, the system now includes monitoring
modules for coronary artery disease, chronic kidney disease,stroke, chronic obstructive pulmonary disease, and asthma. The
cocreation process, covering requirement analysis, mock-up
design, implementation, and testing, took 3 months for each
module, showcasing the process’s repeatability for swift module
additions. These new disease modules are not yet public in theoperational system.

Implementation (Results)
The system is being used extensively throughout the wholecountry. As of September 18, 2023, a total of 73,715,269
screening and monitoring encounters have been performed by
25,568 users (24,627 FMPs and 941 FMP nurses) for 16,058,904
unique citizens. Among these citizens, 56.2% (n=9,025,104)
are female and 43.8% (n=7,033,800) are male. The average
number of DMP encounters per patient is 4.59. The distributionof encounters per DMP module and the breakdown between
screening and monitoring is provided in Table 1.
In Turkey, there are 26,600 FMP units, with each unit using 1
FMP at a time. As of September 18, 2023, FMPs working at
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26,210 (98.5%) unique FMP units have logged into the DMP
at least once, and 22,982 (86.4%) FMP units have performedat least 1 encounter.
Table 2 details the nationwide coverage rates per disease moduleand encounter type as of September 18, 2023. It includes the
cumulative target population size and the unique number ofpatients screened or monitored at least once. During this period,
DMP screenings led to new diagnoses: 144,768 for hypertension,
490,346 for diabetes, 534,423 for high cardiovascular risk, and3,545,573 for obesity. These individuals were diagnosed withthese chronic diseases for the first time, following
evidence-based clinical guidelines.
Age histograms of DMP patients who have been screened ormonitored at least once are provided per sex in Figure 6.
Piloting studies occurred from October to December 2019, andthe system has been fully operational nationwide since January
2020. Use notably increased with FMP salary incentivization
calculations on July 1, 2021 (Figure 7), showing monthly
encounter numbers by module from the start of 2021. Since
then, encounters have steadily risen, with minor drops during
summer holidays, and the distribution among DMP modules
has remained consistent.
Figure 8 displays the distribution of total DMP encounters percity in Turkey, with colors intensifying as encounter numbers
rise. Although higher numbers generally align with city
populations, outliers exist, as seen in the top 10 performing
cities outlined in Table 3. Despite Istanbul having Turkey’s
largest population, it only slightly surpasses Ankara in DMP
encounters. This is mainly due to the high patient load per FMP
in Istanbul. FMPs overseeing over 4000 citizens are exempt
from DMP use due to their heavy workload. Table 3 also
provides patient average age and encounter duration information.
The performance of the FMPs is assessed monthly. The
cumulative targets and realized achievement rates for January2023 are provided in Table 4. An achievement rate of 23.1%(4,508,841/19,546,041) for the entire population represents

significant advancement compared with the(511,198/13,117,900) achievement rate in July 2021.

3.9%

The DMP system recommends personalized treatment goals
such as systolic blood pressure, low-density lipoproteincholesterol, and weight based on clinical guidelines. After atreatment goal is set, the DMP also assesses progress toward
the goal in subsequent encounters. As of September 18, 2023,
approximately 12.4 million of these treatment goals have beenassessed, and the achievement rates are presented in Table 5.
These assessments provide valuable information for FMPs
caring for their patients.
At present, the performance of the FMPs is quantitatively
calculated based on the number of performed encounters.
However, the MOH envisions transitioning to a qualitative
performance evaluation in midterm, where treatment goals andtheir achievement rates will play a significant role.
The system is highly performant and scalable. On a selectedworking day, February 14, 2023, the onFHIR.io HL7 FHIR
Repository handled a total of 105.7 million FHIR interactions
with an average response time of 31.3 milliseconds. During
peak times of the day, the system can effortlessly manage up
to 5000 FHIR interactions per second. Multimedia Appendix 2
illustrates the distribution and average response time of FHIR
interactions on this day.
Among all FHIR requests, 57.4% (60.7 million) are searchinteractions, which are extensively used by the DMP web app
to find, display, and forward specific clinical concept values to
CDSS. Following search interactions, update interactions makeup 33.2% (35.1 million) of the requests and are also used forresource creation when a provided resource ID is available. The
average response times for read and search interactions are only
3.9 and 6.4 milliseconds, respectively. In the case of transactions
and batch interactions, the average response times are evenlower than update interaction alone, thanks to the parallelizationof contained requests within onFHIR.io. As of September 18,
2023, onFHIR.io maintains a repository of 16.3 billion FHIR
resources, totaling 22.4 terabytes in size, including care planning
data by DMP and EHR/PHR data synchronized from e-Nabiz.

Table 1. Total screening and monitoring encounters per module.
ModuleScreening (n=45,166,536), n (%)

Monitoring (n=28,548,733), n (%)

Total (n=73,715,269), n (%)

Hypertension

13,857,594 (30.7)

12,046,449 (42.2)

25,904,043 (35.1)

Obesity

18,029,994 (39.9)

800,480 (2.8)

18,830,474 (25.5)

Diabetes

8,914,193 (19.7)

5,071,646 (17.8)a13,985,839 (19.0)

CVDb risk

4,364,755 (9.7)

9,182,814 (32.2)

13,547,569 (18.4)

Older adult

N/Ac

1,447,344 (5.1)

1,447,344 (2.0)

aOnly the patients monitored in primary care are listed; advanced obesity cases (a BMI over 40 kg/m2 or a BMI between 30 and 40 kg/m2 supportedwith additional comorbidities) are monitored in secondary and tertiary care.
b

CVD: cardiovascular disease.

c

N/A: not applicable.

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Table 2. Coverage rate of citizens in target population lists.
Module and encounter typeAll citizens in target population, n

Screened and monitored patients, n

Coverage rate (%)

Screening

48,443,467

10,820,774

22.3

Monitoring

14,943,378

4,083,057

27.3

Screening

59,956,288

14,640,013

24.4

Monitoring

769,654a

383,920

49.9

Screening

27,450,172

6,486,947

23.6

Monitoring

7,588,543

2,472,585

32.6

Screening

17,276,617

3,319,070

19.2

Monitoring

17,759,500

5,078,665

28.6

8,770,474

1,056,766

12.0

Hypertension

Obesity

Diabetes

CVDb risk

Older adult
Monitoringa

Only those in the primary care obesity monitoring list, as explained in Table 1.

b

CVD: cardiovascular disease.

Figure 6. Age histograms of disease management platform patients: female on the left and male on the right.

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Figure 7. Disease management platform encounters per month by module. CVD: cardiovascular disease.

Figure 8. Encounters by city on a map.

Table 3. Top 10 performing cities.

a

City

Total populationa

Rankb

Number of encounters

Number of patients

Average age of patients (years)

Average duration(minutes)

Istanbul

15,907,951

1

4,974,972

1,286,640

50.5

1.21

Ankara5,782,285

2

4,918,745

1,084,797

51.7

1.13

Izmir

4,462,056

3

4,395,653

937,607

53.4

1.18

Adana

2,274,106

7

3,531,441

709,075

51.3

0.99

Kayseri

1,441,523

15

2,633,349

479,849

51.7

1.06

Antalya

2,688,004

5

2,617,274

636,535

51.4

1.08

Konya

2,296,347

6

2,607,459

579,927

51.2

1.15

Bursa

3,194,720

42,408,817

525,016

52.3

1.18

Balikesir

1,257,590

17

2,322,111

425,171

55.7

1.13

Samsun1,368,488

16

2,237,385

422,438

54.2

1.06

2022 census data by the Turkish Statistical Institute (TurkStat).

b

The rank of cities in Turkey by total population count.

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Ulgu et al

Table 4. Screening and monitoring encounters per module in January 2023.
Module and encounter type

Monthly target

Number of encounters

Achievement rate (%)

Screening

3,868,662

789,699

20.4

Monitoring

4,795,117

709,004

14.8

Screening

4,849,306

1,083,414

22.3

Monitoring

53,77051,512

95.8

Screening

898,665

670,502

74.6

Monitoring

2,128,955

279,071

13.1

Screening

742,634

262,419

35.3

Monitoring

1,488,004

589,52339.6

720,928

73,697

10.2

19,546,041

4,508,841

23.1

Hypertension

Obesity

Diabetesa

CVD risk

Older adult
Monitoring
Totala

CVD: cardiovascular disease.

Table 5. Achievement rates of treatment goals.

aTreatment goal

Achievement rate (%)

Systolic BPa

88.8

Diastolic BP

94.2

Fasting glucose

52.0

HbA1cb

61.5

LDLc cholesterol

14.8

HDLd cholesterol

63.2

Triglyceride

52.6

Weight

5.6

BMI

6.3

Waist circumference

2.9

BP: blood pressure.

b

HbA1c: hemoglobin A1c.

c

LDL: low-density lipoprotein.

d

HDL: high-density lipoprotein.

Discussion
Principle Findings and Lessons Learned
We have demonstrated that as of September 18, 2023, the DMP
has been used by more than 25,000 users to conduct over 73
million screening and monitoring encounters for more than 16
million individuals. The national directive incentivizing FMPs
to conduct screening and monitoring for chronic diseases is oneof the contributing factors to this success.
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We demonstrated the platform’s efficient horizontal scalability,
serving thousands of HCPs daily without performance issues.
DMP screenings identified approximately 150,000 new
hypertension cases, over 490,000 diabetes cases, more than
500,000 high cardiovascular risk cases, and over 3.5 million
obesity cases. This allowed timely treatment in line withevidence-based guidelines.
We have shown that the system seamlessly interoperates withexisting national EHR via HL7 FHIR. It enables accessing andprocessing patient data from various sources to provide
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JMIR MEDICAL INFORMATICS
personalized care plan guidance, maximizing the effectivenessof evidence-based decision support services. The DMP has
achieved all 5 levels of the 5S Model as proposed by Haynes[44] for the successful implementation of information services
for evidence-based health care decisions. Continuous cocreation
activity involving members of the Turkish MOH has contributed
this success, along with the interoperability architecture basedon international standards. On the other hand, we have collected
feedback from FMPs to encourage us to also enable seamlessintegration with the national e-Prescription and national
appointment system. FMPs need to manually input prescription
and appointment recommendations into the other systems.
Future plans include integrating these national systems directly
to the DMP as well.
Although we have demonstrated that, through a repeatable andwell-defined cocreation methodology, the system can be easily
extended to address additional diseases, it still requires
implementation effort from developers. We plan to extend the
DMP system with administrative interfaces. This will enable
subject matter experts from the MOH to create new disease
screening and monitoring modules using form-based designinterfaces.
Finally, although FMPs conduct screening and monitoring,
specialists can view patient dashboards but cannot perform
encounters; this can be easily enabled with the DMP’s role-based
access control mechanism, pending organizational decisions
for national-scale implementation.
The system is operated as a part of national health IT ecosystemfunded by the budget of the Turkish MOH. Open-source
technologies have been used; hence, additional licensing fee
has not incurred. Approximately 80% of the budget is spent forsoftware development, 15% for project management, and 5%

Ulgu et al
for training costs. Initial development phase has lasted 2 years.
In the last 2.5 years, the system is under maintenance, and new
disease modules have been developed.

Prospective Benefits and Impact
The system paves the way forward value-based care, where
patient outcomes are monitored, and providers are incentivized
for improving health. Currently, the DMP sets individual clinicalgoals (eg, HbA1c and BMI) based on evidence-based guidelines.
It monitors FMP performance in achieving these targets throughclose screening and monitoring. FMPs are presently incentivizedbased on screening and monitoring visits, but the system isready to adopt value-based care by monitoring clinical targets.
DMP implementation opens opportunities to collect real-time
research data, measuring the effectiveness of nationwide disease
management protocols. Continuously gathering information
about patients’ disease status and recording outcomes fromscreening and monitoring visits, the generated data provide
valuable insights into disease management.

Conclusions
This paper introduces a nationwide DMP designed for effective
chronic disease screening and management, aligning withevidence-based clinical guidelines to enhance health carequality. With its user-friendly interfaces, it guides FMPs throughpersonalized care planning with checklists for medication orders,referrals, lab tests, and risk screening. The system has been
operational nationwide since January 2020. We havedemonstrated seamless EHR integration, scalability,
performance, and effectiveness in early diagnosis and meetingclinical targets. Future work includes a comprehensive study
to analyze the direct clinical and cost-saving effects of the DMP
on chronic disease management in Turkey.

Acknowledgments
The authors wish to acknowledge administrative and technical support by the following departments of the Turkish Ministry of
Health: Department of Public Health Informatics, Department of Chronic Diseases and Elderly Health, Department of Healthy
Nutrition and Active Life, Department of Data Management, and Department of Standards and Accreditation; Türksat, and Innova.

Data Availability
The data sets generated and/or analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions
GBLE and MMU carried out conceptualization of the paper; GBLE, MY, TN, SP, MG, YK, AAS, SG, AD, ZOA, and BE
established the methodology of the study; GBLE, MY, TN, SP, MG, and YK developed the software; ZOA, BE, SA, MMU, andSB contributed validation studies; GBLE, MY, and TN carried out formal analysis; MY, TN, SP, MG, YK, AAS, and SG
contributed to data curation; GBLE, MY, TN, SP, MG, and MMU wrote the manuscript; AAS, YK, SG, ZOA, BE, and SA
reviewed and edited the manuscript; GBLE, MY, TN, SP, and MG created the visualizations in the manuscript; AD and SB
supervised the study; GBLE, MY, BE, and MMU coordinated project administration. All authors have read and agreed to the
published version of the manuscript.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Eligibility criteria for target populations for screening and monitoring encounters.
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Ulgu et al[DOCX File , 25 KB-Multimedia Appendix 1]

Multimedia Appendix 2
FHIR interactions in a day: request count on the left and average response time on the right. FHIR: Fast Healthcare Interoperability
Resources.
[PNG File , 98 KB-Multimedia Appendix 2]

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Abbreviations
CDSS: clinical decision support service
DMP: Disease Management Platform
EHR: electronic health record
FHIR: Fast Healthcare Interoperability Resources
FMP: family medicine practitioner
HbA1c: hemoglobin A1c
HCP: health care professional
HL7: Health Level Seven
ICD-10: International Classification of Diseases, Tenth Revision
MOH: Ministry of Health
PHR: personal health record

Edited by C Perrin; submitted 16.06.23; peer-reviewed by J Galvez-Olortegui, J Pevnick; comments to author 13.09.23; revised
version received 21.09.23; accepted 29.11.23; published 19.01.24
Please cite as:
Ulgu MM, Laleci Erturkmen GB, Yuksel M, Namli T, Postacı Ş, Gencturk M, Kabak Y, Sinaci AA, Gonul S, Dogac A, Özkan Altunay
Z, Ekinci B, Aydin S, Birinci S
A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life
Implementation Report
JMIR Med Inform 2024;12:e49986
URL: https://medinform.jmir.org/2024/1/e49986
doi: 10.2196/49986
PMID: 38241077©Mustafa Mahir Ulgu, Gokce Banu Laleci Erturkmen, Mustafa Yuksel, Tuncay Namli, Şenan Postacı, Mert Gencturk, Yildiray
Kabak, A Anil Sinaci, Suat Gonul, Asuman Dogac, Zübeyde Özkan Altunay, Banu Ekinci, Sahin Aydin, Suayip Birinci. Originallypublished in JMIR Medical Informatics (https://medinform.jmir.org), 19.01.2024. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, isproperly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as wellas this copyright and license information must be included.

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