As low- and middle-income countries transition from paper to digital systems, family planning programs can benefit from unprecedented opportunities to improve services. Investments in digital health tools have expanded exponentially, but information on what works—and what does not— remains limited and scattered. As investments have increased, digital applications and data fragmentation have proliferated, but stakeholders are moving towards more coordinated efforts to scale digital health solutions, support countries’ digital health infrastructure, and share evidence-based learnings.
This Digital Health Compendium enables users to explore case studies across a range of digital health technologies used to enhance family planning programs mainly in sub-Saharan Africa, but also in other regions of the world. Digital health applications in family planning programs can be broadly classified as those affecting demand generation, service delivery, supply chain management, and the policy and enabling environment. In many low- and middle-income countries, digital health innovations were adopted earlier in other health sectors, including HIV/AIDS, maternal and child health, and noncommunicable disease prevention and response. As a result, much of the impact evidence is likewise restricted to those sectors. To advance greater adoption of digital technology in family planning programs, more data and information on the challenges, opportunities, scalability, and results are needed. This compendium aims to consolidate emerging information and data on applications of digital technology in family planning programs to inform adoption and scale-up of successful approaches.
All of the case studies were submitted by the implementing organizations and include a description of the digital health intervention, program context, and, if available, important findings and lessons learned through rigorous evaluations or program data. The compendium facilitates a quick search for case studies based on the target user for digital health intervention, building block for the digital health enabling environment, family planning program classification, and country location. The case studies give policy and program decisionmakers insights on real-world applications of digital health, promising practices, challenges, and other lessons that can be applied to current and future programs.
Standardizing family planning and sexually transmitted infections clinical guidelines data in digital health tools to bridge the gap between human-readable clinical guidelines and machine-readable fast healthcare interoperability resources implementation guides.
Digital Health Implementation Research Consultant
World Health Organization
Global Health Informatics
Dynamic Content Group
November 2019 -May, 2020
Health Care Provider, Data Services Provider
Services and Applications, Standards and Interoperability
Policy and Enabling Environment
Clinical practice guidelines (CPGs) are used worldwide to inform clinical decision-making through the implementation of evidence-based clinical and public health practices. The use of digital technology, such as electronic health records (EHRs), continues to increase as countries work to facilitate public health interventions, to improve care delivery with decision support, and to ensure accountability at all levels of the health system. However, the translation of CPGs into digital systems often results in a subjective interpretation by implementers and software developers due to ambiguities during translation into an electronic format. These difficulties can lead to divergences in electronic CPG implementation, reducing usefulness of collected data outside of that implementation setting (Biondich et al., 2006; Gillois et al., 2001; Shiffman et al., 2004; and Tierney et al., 1995).
To resolve these challenges, the World Health Organization (WHO) created HL7 Fast Healthcare Interoperability Resources (FHIR), which reflect WHO recommendations in standards-based digital format. This is to ensure WHO’s evidence-based guideline content is implemented in digital systems with fidelity, using interoperability standards.
WHO created FHIR Implementation Guides (IGs) for the areas of family planning (FP) and sexually transmitted infections (STI). These IGs can be used by implementers and software developers to standardize FP and STI clinical guidelines in digital health solutions supporting these service areas. The FHIR IG for Family Planning was created based on the following WHO guidelines and guidance documents:
The FHIR IG contains the minimum dataset to be collected for service delivery and indicator reporting, according to these normative guidance documents, in a standards-based format. To support this, HL7 FHIR and standard semantic terminologies (see text box), including LOINC, SNOMED CT, ICD10, and RxNorm, provided structure and codes to take an additional step toward the goal of computable guidelines, critical for high-quality patient care (IHE Wiki, 2019). The structure and codes provided using FHIR and standard semantic terminologies offer clarification on the way a new healthcare application or system should be structured and what questions should be asked, which answer options should be available, and more. The Unified Medical Language System (UMLS) is one example of a tool that was used for terminology mapping across multiple standards.
To demonstrate that the creation of a FHIR IG for FP and STI is feasible and to support the processes and resourcing required—that is, a set of rules about how the FHIR resources can be applied—the project established the following objectives:
Existing FHIR IGs, including the FHIR Clinical Guidelines IG Template, were reviewed to understand their goals and structures, and mapping recommendations were reviewed for the selected standard terminologies. Data dictionary terms were consolidated into a master data dictionary spreadsheet to serve as a collaborative environment where FHIR mapping, terminology coding, and progress evaluation occurred. A mapping process standard operation procedure (SOP) served as guidance while the FHIR and terminology mapping took place. This SOP also assisted in the development of tooling to generate the IGs. In the case that FHIR resources did not adequately address the mapping need, elements were added on to existing FHIR resources to address the need. Additionally, data modeling based on a data dictionary and minimum dataset provided by WHO could be altered in the case that a better modeling scheme was identified by clinical informatics personnel. Other data dictionary alterations to suit mapping were performed in collaboration with relevant subject matter experts.
Agile methods were used to iteratively review the process and fine-tune the data dictionary, mapping processes, terminology coding, FHIR modeling, and IG tooling. The outputted FHIR IGs from the mapped terms allowed for iterative review and subsequent issue resolution. Of the 947 terms to be mapped, 907 have been assigned a FHIR mapping, and 805 have been given a related terminology code. These terms represent both FP and STI content, along with basic underlying information for a healthcare application or system.
Because of the need for clarification in a term name or definition or of use of the data, not all terms have been mapped to terminology. Furthermore, due to the limitations of the standard terminology code sets themselves, not all data elements could be mapped. For example, the list of contraceptive methods needed to be mapped across multiple terminology code sets in order to be reflective of all the recommended contraceptive methods available.
Some 801 terms have been mapped to both terminology and FHIR Resources, with 31 terms requiring custom attributes to be added to FHIR Resources, that is, additional attributes outside of base FHIR. IGs have been successfully generated from the data dictionary with the terminology mappings and FHIR resources assigned. This project continues to progress as mapping continues, the IG tooling is refined, and IGs are regenerated.
While the project is still underway, FHIR profiles and IGs have been successfully generated from the data dictionary with the semantic terminology mappings and FHIR resources assigned. Future directions for this work will involve the refinement of this process and adaptation to other health domains. The establishment of a standardized mapping process, such as a terminology management system, is recommended to ensure consistency and usefulness. Finally, the products of this work require testing in various health care settings worldwide, allowing for further refinement.