Background The predominant implementation paradigm of electronic health record (EHR) systems in low- and middle-income countries (LMICs) relies on standalone system installations at facilities. This implementation approach exacerbates the digital divide, with facilities in areas with inadequate electrical and network infrastructure often left behind. Mobile health (mHealth) technologies have been implemented to extend the reach of digital health, but these systems largely add to the problem of siloed patient data, with few seamlessly interoperating with the EHR systems that are now scaled nationally in many LMICs. Robust mHealth applications that effectively extend EHR systems are needed to improve access, improve quality of care, and ameliorate the digital divide. Objective We report on the development and scaled implementation of mUzima, an mHealth extension of the most broadly deployed EHR system in LMICs (OpenMRS). Methods The “Guidelines for reporting of health interventions using mobile phones: mobile (mHealth) evidence reporting assessment (mERA)” checklist was employed to report on the mUzima application. The World Health Organization (WHO) Principles for Digital Development framework was used as a secondary reference framework. Details of mUzima’s architecture, core features, functionalities, and its implementation status are provided to highlight elements that can be adapted in other systems. Results mUzima is an open-source, highly configurable Android application with robust features including offline management, deduplication, relationship management, security, cohort management, and error resolution, among many others. mUzima allows providers with lower-end Android smartphones (version 4.4 and above) who work remotely to access historical patient data, collect new data, view media, leverage decision support, conduct store-and-forward teleconsultation, and geolocate clients. The application is supported by an active community of developers and users, with feature priorities vetted by the community. mUzima has been implemented nationally in Kenya, is widely used in Rwanda, and is gaining scale in Uganda and Mozambique. It is disease-agnostic, with current use cases in HIV, cancer, chronic disease, and COVID-19 management, among other conditions. mUzima meets all WHO’s Principles of Digital Development, and its scaled implementation success has led to its recognition as a digital global public good and its listing in the WHO Digital Health Atlas. Conclusions Greater emphasis should be placed on mHealth applications that robustly extend reach of EHR systems within resource-limited settings, as opposed to siloed mHealth applications. This is particularly important given that health information exchange infrastructure is yet to mature in many LMICs. The mUzima application demonstrates how this can be done at scale, as evidenced by its adoption across multiple countries and for numerous care domains.
Background Unique patient identification remains a challenge in many health care settings in low- and middle-income countries (LMICs). Without national-level unique identifiers for whole populations, countries rely on demographic-based approaches that have proven suboptimal. Affordable biometrics-based approaches, implemented with consideration of contextual ethical, legal, and social implications, have the potential to address this challenge and improve patient safety and reporting accuracy. However, limited studies exist to evaluate the actual performance of biometric approaches and perceptions of these systems in LMICs. Objective The aim of this study is to evaluate the performance and acceptability of fingerprint technology for unique patient matching and identification in the LMIC setting of Kenya. Methods In this cross-sectional study conducted at an HIV care and treatment facility in Western Kenya, an open source fingerprint application was integrated within an implementation of the Open Medical Record System, an open source electronic medical record system (EMRS) that is nationally endorsed and deployed for HIV care in Kenya and in more than 40 other countries; hence, it has potential to translate the findings across multiple countries. Participants aged >18 years were conveniently sampled and enrolled into the study. Participants’ left thumbprints were captured and later used to retrieve and match records. The technology’s performance was evaluated using standard measures: sensitivity, false acceptance rate, false rejection rate, and failure to enroll rate. The Wald test was used to compare the accuracy of the technology with the probabilistic patient-matching technique of the EMRS. Time to retrieval and matching of records were compared using the independent samples 2-tailed t test. A survey was administered to evaluate patient acceptance and satisfaction with use of the technology. Results In all, 300 participants were enrolled; their mean age was 36.3 (SD 12.2) years, and 58% (174/300) were women. The relevant values for the technology’s performance were sensitivity 89.3%, false acceptance rate 0%, false rejection rate 11%, and failure to enroll rate 2.3%. The technology’s mean record retrieval speed was 3.2 (SD 1.1) seconds versus 9.5 (SD 1.9) seconds with demographic-based record retrieval in the EMRS (P<.001). The survey results revealed that 96.3% (289/300) of the participants were comfortable with the technology and 90.3% (271/300) were willing to use it. Participants who had previously used fingerprint biometric systems for identification were estimated to have more than thrice increased odds of accepting the technology (odds ratio 3.57, 95% CI 1.0-11.92). Conclusions Fingerprint technology performed very well in identifying adult patients in an LMIC setting. Patients reported a high level of satisfaction and acceptance. Serious considerations need to be given to the use of fingerprint technology for patient identification in LMICs, but this has to be done with strong consideration of ethical, legal, and social implications as well as security issues.
BACKGROUND The predominant implementation paradigm of electronic health record (EHR) systems in low- and middle-income countries (LMICs) relies on standalone system installations at facilities. This implementation approach exacerbates the digital divide, with facilities in areas with inadequate electrical and network infrastructure often left behind. Mobile health (mHealth) technologies have been implemented to extend the reach of digital health, but these systems largely add to the problem of siloed patient data, with few seamlessly interoperating with the EHR systems that are now scaled nationally in many LMICs. Robust mHealth applications that effectively extend EHR systems are needed to improve access, improve quality of care, and ameliorate the digital divide. OBJECTIVE We report on the development and scaled implementation of <i>mUzima</i>, an mHealth extension of the most broadly deployed EHR system in LMICs (OpenMRS). METHODS The “Guidelines for reporting of health interventions using mobile phones: mobile (mHealth) evidence reporting assessment (mERA)” checklist was employed to report on the <i>mUzima</i> application. The World Health Organization (WHO) Principles for Digital Development framework was used as a secondary reference framework. Details of <i>mUzima</i>’s architecture, core features, functionalities, and its implementation status are provided to highlight elements that can be adapted in other systems. RESULTS <i>mUzima</i> is an open-source, highly configurable Android application with robust features including offline management, deduplication, relationship management, security, cohort management, and error resolution, among many others. <i>mUzima</i> allows providers with lower-end Android smartphones (version 4.4 and above) who work remotely to access historical patient data, collect new data, view media, leverage decision support, conduct store-and-forward teleconsultation, and geolocate clients. The application is supported by an active community of developers and users, with feature priorities vetted by the community. <i>mUzima</i> has been implemented nationally in Kenya, is widely used in Rwanda, and is gaining scale in Uganda and Mozambique. It is disease-agnostic, with current use cases in HIV, cancer, chronic disease, and COVID-19 management, among other conditions. <i>mUzima</i> meets all WHO’s Principles of Digital Development, and its scaled implementation success has led to its recognition as a digital global public good and its listing in the WHO Digital Health Atlas. CONCLUSIONS Greater emphasis should be placed on mHealth applications that robustly extend reach of EHR systems within resource-limited settings, as opposed to siloed mHealth applications. This is particularly important given that health information exchange infrastructure is yet to mature in many LMICs. The <i>mUzima</i> application demonstrates how this can be done at scale, as evidenced by its adoption across multiple countries and for numerous care domains.
BACKGROUND Unique patient identification remains a challenge in many healthcare settings within low- and middle-income countries (LMICs). Without national-level unique identifiers for whole populations, countries rely on deterministic and probabilistic patient matching approaches that have proven suboptimal in LMICs. Affordable bio-metric-based approaches, implemented with consideration of contextual ethical, legal and social implications (ELSI), have a potential to address patient identification challenges and to improve care quality, patient safety and reporting accuracy. However, limited studies exist to evaluate actual performance of biometric approaches and perceptions towards these systems within LMIC contexts. OBJECTIVE To evaluate performance and acceptability of fingerprint technology (FPT) for unique patient matching and identification in the LMIC setting of Kenya METHODS This cross-sectional study was conducted at a HIV care and treatment facility in Western Kenya. An open-source fingerprint application was integrated within an implementation of the Open Medical Records System (OpenMRS) which is an open source electronic medical records system (EMR) and currently in use at the study setting. OpenMRS is nationally-endorsed and deployed for HIV care in Kenya and in over 40 countries, hence potential for ease of translating findings across multiple countries. Adult participants over 18 years of age were conveniently sampled and enrolled into the study. Participants’ left thumbprints were captured, stored and used to retrieve and match patient records. FPT performance was evaluated using standard measures namely: Sensitivity, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Failure to Enroll Rate (FER). Wald test was used to compare the accuracy of the FPT to the EMRs’ probabilistic matching technique. Time to retrieval and matching of records was compared using the independent samples t-test. A survey was administered to evaluate patient acceptance and satisfaction with use of the FPT. RESULTS 300 participants were enrolled, mean age was 36.3 years (SD 12.2) and 174/300 (58%) were female. FPT per-formed as follows: sensitivity 89.3%, FAR 0%, FRR 11%, and FER 2.3%. FPT mean record retrieval speed was 3.2s (SD 1.1) vs. 9.5s (SD 1.9) with demographic-based record retrieval in the EMR (p<.001). Survey results revealed participants’ comfort (96.3%) and willingness (90.3%) to use the FPT. CONCLUSIONS Fingerprint Technology (FPT) performed very well in identifying adult patients within a LMIC setting. Patients reported a high level of satisfaction and acceptance of the technology. Serious considerations need to be given to use of FPT for patient identification in LMICs, but this has to be done with strong consideration on ELSI and security issues.
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