International Journal of Community Medicine and Public Health

Harshitha N et al. Int J Community Med Public Health. 2026 Jun;13(6):3239-3246

https://www.ijcmph.com

pISSN 2394-6032 | eISSN 2394-6040

Review Article

DOI: https://dx.doi.org/10.18203/2394-6040.ijcmph20261823

Standardizing non-communicable diseases surveillance: a review of variables, tools, digital platforms and a proposed integrated HMIS framework

Nalmasu Harshitha1*, Bhupal Chidambar1, Deepa Lingadahalli Nanjundaswamy2

1Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence, Karnataka, India

2Department of Community Medicine, Sri Madhusudan Sai Institute of Medical Sciences and Research, Chikkaballapur, Karnataka, India

Received: 19 April 2026

Accepted: 15 May 2026

*Correspondence:

Nalmasu Harshitha,

E-mail: harshitha080503@gmail.com

Copyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

Non-communicable diseases (NCDs) account for a significant share of preventable morbidity and mortality, yet data collection on NCDs is carried out in separate, disconnected and often inconsistent ways. This review showcases the variables, tools and digital platforms used for NCD data collection, monitoring and implementation by collecting the data elements such as behavioural risks, physical measurements, biochemical markers, clinical diagnostic and treatment variables, outcomes and system performance indicators and to classify the tools used to capture and manage them. This work also proposes a baseline NCD dataset and a unified framework for integrating survey and routine care data within a single ecosystem-wide Health Management Information System (HMIS), enabling consistent measurement, programme monitoring and actionable decision support.

Keywords: Noncommunicable diseases, Public health surveillance, Health information systems, Health information interoperability, Speech recognition software

INTRODUCTION

World Health Organisation highlights that non-communicable diseases (NCDs) contribute 74% of global deaths worldwide, which is a big health concern.1 The results also report that lifestyle and behavioural factors like tobacco use, physical inactivity, unhealthy diet and the harmful use of alcohol are the major risk factors.1 To control these, we need to spot these habits early, track them across populations for effective prevention and management.2 Therefore, the Global Action Plan for the Prevention and Control of NCDs was initiated, and Sustainable Development Goal also aims to reduce premature mortality from NCDs by one-third by 2030.3,4 Hence, many countries start NCD prevention programmes across the world, which lead to the need for continuous monitoring of the data, taking decisions on it and integration with HMIS systems to track the progress of programme implementation.5 Therefore, countries start including risk factor prediction, screening and identification of diseases and revisit for further screening in their implementation programmes.6 To achieve these, continuous data collection plays an important role.7

So many countries start implementing structured approaches; the most implemented approach is using the WHO STEPS framework, which helps identify what data variables need to be collected. The framework includes NCD risk factors such as tobacco use, diet, obesity, physical activity, blood pressure and glucose etc.8 These structured survey methods help countries systematically collect the right kind of required data at population level for NCD risk factor evaluation and help to compare between countries in a standard way.9 Although the structured framework like STEPS helps identify what data to collect for NCD surveillance, the challenge lies in its implementation. All these approaches require a lot of human resources, planning and logistics as data collection requires manual entry.10 These traditional methods of data collection result in various limitations because the data which is collected is periodic, which cannot be used for continuous monitoring and as it is collected by manual process, it often results in delay in availability of the data.

So, these problems result in the development of digital systems which help for further diagnosis and follow-up.11 It also helps to maintain health records longitudinally, track patients over time and integrate different data sources. These developments represent a shift from periodic survey-based surveillance towards more integrated system-based data management.12 Even though digital health systems improve the way NCD data is managed, several challenges still exist in practice. In many settings, data entry still depends on health workers manually entering information into the platforms, which increases workload and may lead to in-complete or delayed records. Different systems may also work separately, making it difficult to combine information from multiple sources.13

These limitations show that while digital platforms help in organising information, there is still a need for smarter approaches that can assist in processing and understanding NCD-related data more efficiently.14 Given the evolution from survey-based methods to integrated digital platforms, there is a need to understand how different variables, instruments and systems con-tribute to NCD data collection.15

Many studies focus either on surveys or on digital health technologies separately, but a clear overview of how these approaches work together is still limited. Therefore, this review aims to focus on the variables required for NCD surveillance, tools used to capture the data and the platforms which manage the data across various health systems.

This study helps us to understand the data required for NCD management and mainly it brings to the point the importance of effective monitoring for better decision-making systems, because even though there is an evolution in how NCD data is collected, there is still a need for better systems.

This study initially focused on the operational implementation of NCD programmes across various countries to study the structure of implementation programmes. It then examined what variables are required for NCD data collection, because operational implementation influences the type of variables collected, followed by tools required for data collection, digital platforms, challenges and future directions. In this context, it proposes a baseline dataset and a unified framework which uses current technologies like OpenAI’s Whisper model and Large Language Models such as Gemini, Med Gemma to overcome existing operational barriers.

METHODS

The methodology for this review paper is used PUBMED and Google scholar for the data collection. Along with the research papers, articles and official websites which provide the implementation details for NCD were also studied. The study mainly focused on the papers which are aligned with the core part of the topic like NCD surveillance, data collection methods and digital platforms used. Conversely, the papers which mainly focused on the clinical part instead of data collections methods were excluded. the search was executed using the keys words along with combinations such as Noncommunicable Diseases, Public Health Surveillance, Health Information Systems and Health Information Interoperability.

Operational implementation of national NCD programmes

The operational implementation of NCD programme varies across the countries because of the structural differences in its implementation programme.5 Based on the place where screening happens, the way NCD programmes are implemented is conceptually divided into three categories: Community-based, Facility-based and Routine Care Surveillance model.

In the Community-based approach, frontline workers visit houses, conducts the initial risk scoring and direct them to the hospitals like Brazil’s family Health Strategy, India’s National Programme for Non-Communicable Diseases (NP-NCD) and Samoa’s Package of Essential Noncommunicable (PEN) Fa’a Samoa.17-19 In the facility-based approach, the individuals directly visit primary health center to get screened like Sri Lanka’s Healthy Lifestyle Centers and China’s National Basic Public Health Service.20-22 Finally, in the routine care surveillance approach, when an individual visits the hospital for a regular health checkup or for any other reason, they get screened for NCD as well like United Kingdom’s Making Every Contact Count (MECC).23,24 This categorization indicates the presence of these facilities in the country, rather than implying it is the only approach followed.

Variables collected in national NCD programmes

The way National NCD programme is implemented in different operational settings influences the type of variables collected.25 Community based models mainly prioritize behavioural and basic measurement variables as they are used for early disease risk identification. The facility-based screening models further add clinical assessment and diagnostic information. Thereafter routine care models include long term surveillance variables which are collected during their regular visits. Table 1 showcases the variables collected in different operational settings.

Tools used for NCD data collection

Several tools used across national NCD programmes to capture behavioural, clinical and biochemical data during screening and diagnosis. Behavioural risk factors are commonly collected using structured questionnaires and screening instruments.

Physical and anthropometric measurements are collected using clinical devices. Biochemical markers use glucometers and laboratory diagnostic equipment. Clinical diagnosis, treatment details and patient outcomes are documented using electronic clinical forms used in healthcare.

Table 1: Variables by operational setting.
Table reproduced from source PDF page 3

Note. BP=Blood Pressure; BMI=Body Mass Index; PA=Physical Activity; CVD=cardiovascular disease; NP-NCD=National Programme for Prevention and Control of NCDs; HLC=Healthy Lifestyle Centers; MBS=Medicare Benefits Schedule.

Table 2: Classification of NCD variables and data collection tools.
Table reproduced from source PDF page 3

Note: NCD=Non-Communicable Disease; MET=Metabolic Equivalent of Task. BP=Blood Pressure; BMI=Body Mass Index; HbA1c=Haemoglobin A1c; LDL=Low-Density Lipoprotein; HDL=High-Density Lipoprotein; CVD=cardiovascular disease; CKD=chronic kidney disease.

Programme monitoring data are captured using reporting formats and monitoring tools to assess system performance and service coverage. To understand better how these data elements are captured in practice, the commonly used tools can be grouped based on the type of data they collect, as shown in Table 2.

Digital platforms in NCD surveillance

Digital platforms represent the evolution from periodic survey-based NCD data collection to integrated, real-time health management systems that enable continuous monitoring, referral tracking and programme evaluation across health system levels.36 Table 3 demonstrates the digital platforms used in different countries and its functioning.

DISCUSSION

Challenges in NCD data capture and integration

Several challenges exist in the implementation of NCD programs across national level. Al though there is an evolution in the way data is collected, managed and used for clinical purposes, many challenges still affect the effective implementation of these programs. The key challenges include While using digital tools there were regular application crashes, inconsistent internet and server downtime. These issues lead to reliance on pen and paper methods and take extra time to re-enter the data into the portal.41 There are workload issues because of inadequate staff across the health system. Data entry into the portal during busy clinical hours 7 is often difficult. Managing multiple portals is time-consuming.13 The challenges further include the constrained integration across systems, Data interoperability, Fragmentation and lack of common terminologies.42

Overall, these challenges highlight gaps between data collection, clinical workflows and system-level analytics, emphasizing the need for stronger integration across the NCD data life cycle.

Proposed baseline NCD dataset

A baseline dataset is to be designed with minimum required variables required for risk scoring, early detection, and continuous surveillance of NCDs. The dataset consists of demographic, behavioural, anthropometric measurements, key biochemical markers, disease history, and environmental exposures.

Additionally selective genetic variables and air quality index can also be included. Based on STEPS and NPNCD questionnaires this Table 4 is developed.43

Table 3: Digital platforms for NCD screening and surveillance.
Table reproduced from source PDF page 4

Note: NCD=Non-Communicable Disease; ABHA=Ayushman Bharat Health Account; CDSS=Clinical Decision Support System; NEHR=National Electronic Health Record; CDMP=Chronic Disease Management Programme; CDC=Centers for Disease Control and Prevention; SMS=Short Message Service; IVR=Interactive Voice Response.

Table 4: Baseline NCD dataset variables.
Table reproduced from source PDF page 4

Note: ID=Identification; BP=Blood Pressure; AQI=Air Quality Index; PM2.5=Particulate Matter 2.5 micrometers or less in diameter; PM10=Particulate Matter 10 micrometers or less in diameter.

Proposed integrated framework

This framework proposes a singular voice-based HMIS architecture which can be used across primary, secondary and tertiary care for NCD data capture, structuring and clinical decision-making. It addresses the limitations of the existing digital health ecosystem where data capture, clinical documentation and health data management function as isolated systems which lead to fragmented information and duplicated effort. Supported by present-day technologies which combine multilingual speech recognition and translation tools, the framework enables unstructured conversations between patients and frontline workers. Voice samples also can be used to extract mental health aspects, generate structured electronic health records, and integrate with a unified HMIS. The framework shown in figure 1 presents a continuous lifecycle of NCD data management from capture to clinical decision.

Figure 1: Proposed voice-enabled integrated HMIS framework for NCD data capture and clinical decision support.
Figure 1: Proposed voice-enabled integrated HMIS framework for NCD data capture and clinical decision support.

The first stage involves multilingual voice data capture where the data of the recipient and the data collector is collected during the conversation in local languages. It mitigates the dual data entry problem and reduces the frontline workers’ burden. Automatic Speech Recognition (ASR) engines like Sarvam AI and Whisper-based transcription systems demonstrate practicality of converting real-time spoken data into machine-readable text in healthcare environments.44,45 Captured voice data undergoes speech-to-text conversion and language standardization where ASR converts spoken responses into digital text. Neural translation systems like Indic Trans or comparable multilingual frameworks then standardize output into English. This maintains a common format across the system without losing multilingual input flexibility during data entry. After transcription and translation, a structured Electronic Health Record (EHR) summary is generated which aligns with predefined NCD data elements. Standards such as Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) ensure consistency and interoperability of the produced electronic records. The structured EHR summary is stored in a singular HMIS system as a central repository for patient-level and program-level data. This eliminates duplication across screening and clinical platforms and supports system-wide continuity. In the final stage, the framework includes a clinical decision layer which operates on EHR records stored in the central HMIS. An algorithm can be developed and trained using collected EHR records to aid risk scoring. Based on the computed risk score, the system generates referral recommendations. As the HMIS gathers longitudinal data over time, the model is refined which improves prediction accuracy and forward-looking risk management.

CONCLUSION

Non-communicable diseases remain a serious global health problem. Solving this requires more than just awareness it needs smarter, practical approaches. Since prevention depends on catching diseases early, a reliable surveillance system becomes essential. Without it, early identification is simply not possible. This review looks at how NCD programmes are implemented across different countries. A key finding is that programme structure directly shapes what variables get collected. Countries with more resources tend to have more comprehensive systems, while others collect only what their infrastructure can support. The existence of these differences is not random; it actually represents the reality in current health systems. This study examined three major components, those are variables, tools, and platforms which are used for NCD data management. Frameworks such as STEPS tell what variables to collect, but there is a difference between knowing what to collect and actually collecting it. The usage of the type of variable depends on the implementation model. The evolution to digital systems has improved the availability of data, patient tracking, and monitoring, but the problems still persist with regard to fragmented systems, poor interoperability, and the workload on frontline workers, which weakens the usage of NCD data for decision-making. To tackle these gaps, this review proposes a conceptual framework using present technologies and designs a unified HMIS system combined with voice-based data capture and generates a structured EMR by using AI-assisted clinical decision support within one single ecosystem. The framework supports multilingual speech recognition and encourages conversations to be in an unstructured way; the framework automatically collects the data, which reduces the burden on frontline workers from the dual data entry problem. The voice samples which are collected can also be used to extract mental health aspects. The baseline dataset which is proposed uses the most essential parameters to predict the risk for NCDs. The dataset uniquely includes the AQUA index, as it is the second major reason for the cause of NCDs and also includes important genetic variables. Future research should focus on the integration of these using current AI capabilities and making it a reality. Routine data, when properly collected and connected, can become actionable insight for clinicians, for programme managers, and ultimately for patients.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: Not required

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