Capacity and Readiness Assessment of Healthcare Facilities for Digital Health Interventions Against Tuberculosis and HIV in Addis Ababa, Ethiopia

Health professionals' technology readiness on the acceptance of teleradiology in the Amhara regional state public hospitals, northwest Ethiopia: Using technology readiness acceptance model (TRAM)

BACKGROUND

Considering individual differences caused by personality differences is crucial for end users' technology acceptance. However, previous studies overlooked the influence of users' technology readiness on technology acceptance. This study, therefore, aimed to evaluate the influence of technology readiness on teleradiology acceptance in the Amhara Regional State Public Hospitals using a technology readiness acceptance model.

METHODS

An institutional-based cross-sectional mixed study design was conducted in September 2021 among 547 health professionals working at sixteen public hospitals in the Amhara region of northwest Ethiopia. Eight key informants were interviewed to explore organizational-related factors. Face-to-face and Google Meet approaches were used to collect the data. We applied structural equation modeling to investigate the influence of technology readiness on health professionals' teleradiology acceptance using Analysis of Moment Structures Version 23 software.

RESULTS

Of the total participants, 70.2% and 85.7% were ready and intended to use teleradiology, respectively. According to technology readiness measuring constructs, optimism and innovativeness positively influenced health professionals' technology acceptance. Perceived ease of use and perceived usefulness showed a statistically positive significant effect on health professionals' intention to use teleradiology. In addition, a statistically significant mediation effect was observed between technology readiness measuring constructs and behavioral intention to use. Furthermore, a shortage of budget, inadequate infrastructure, and users' lack of adequate skills were reported as critical organizational challenges.

CONCLUSIONS

We found a higher proportion of readiness and intention to use teleradiology among health professionals. Personality difference measuring constructs and organizational factors played considerable influence on teleradiology acceptance. Therefore, before the actual implementation of teleradiology, ensuring the system's user-friendliness, improving infrastructure, allocating an adequate budget, and availing of capacity-building opportunities are recommended.

Patterns of childhood tuberculosis diagnosis in Ethiopia: A multicenter cross-sectional study

Background

Children share 12% of the global 10 million people infected with tuberculosis (TB) each year. Closing case detection gap in children remains difficult, with 56% of all children and 65% under-five with TB missed each year. We aimed to assess the patterns of childhood TB diagnosis and underlying determinants in Ethiopia when different TB diagnostic platforms are applied.

Methods

A multi-site, cross-sectional study was carried out in Ethiopia as part of the larger EXIT-TB study - evidence-based multiple focused integrated intensified TB screening package. Outpatient children aged ≤ 15 with cough of any duration seeking care at four healthcare facilities in Ethiopia were enrolled consecutively. Participants underwent sputum Xpert MTB/RIF and/or smear microscopy and posteroanterior chest X-ray (CXR), and their clinical and sociodemographic data were captured using a structured questionnaire. Data were analyzed using Stata version 23. Multiple regression model was computed to determine the factors that influence TB case detection, with a 95% confidence interval (CI) and p < 0.05 taken as statistically significant.

Results

A total of 438 children were enrolled. Of these, 399 had CXR examination of which 55 (13.8%) were suggestive of TB, 270 had Xpert MTB/RIF testing of which 32 (11.9%) were positive, and AFB smear microscopy was done for 51 children of which 2 (3.9%) were positive. Febrile children were more likely to be diagnosed with pulmonary TB than those without fever [aPR = 1.3, 95% CI (1.1-1.4)], and those with a TB contact history were more likely to be diagnosed with pulmonary TB than those with no such contacts [aPR = 1.2, 95% CI (1.1-1.3)]. Children from rural residences were more likely to be diagnosed with TB than those from urban residences [aPR = 1.3, 95% CI (1.1-1.5)].

Conclusion

The findings showed that clinical diagnosis remains an important method of TB diagnosis in children and the preferred choice to avert underdiagnosis. A more sensitive TB diagnostic method for children was symptom screening, followed by CXR and Xpert MTB/RIF assay or smear microscopy. Hence, an algorithm that combines clinical, CXR, and microbiological confirmatory tests can improve the rate of pulmonary TB diagnosis in children till more accurate and cost-effective diagnostic tools are accessible. Fever, weight loss, and TB contact history are highly associated with TB positivity rates in children.

Factors Influencing the Acceptance and Adoption of Mobile Health Apps by Physicians During the COVID-19 Pandemic: Systematic Review

BACKGROUND

During the COVID-19 pandemic, the provision of and access to health care have been uniquely challenging, particularly during lockdowns or when dealing with COVID-19 cases. Health care professionals have had to provide patients with the necessary health care. However, delivering health care services while reducing face-to-face interaction puts an immense strain on health systems that are already overburdened. Against this backdrop, it is now more critical than ever to ensure the accessibility of health care services. Such access has been made increasingly available through mobile health (mHealth) apps. These apps have the potential to significantly improve health care outcomes and expectations and address some of the challenges confronting health care systems worldwide. Despite the advantages of mHealth, its acceptance and adoption remain low. Hence, health care organizations must consider the perceptions and opinions of physicians if the technology is to be successfully implemented.

OBJECTIVE

The objective of this systematic review was to explore and synthesize the scientific literature on the factors influencing the acceptance and adoption of mHealth among physicians during the COVID-19 pandemic.

METHODS

A systematic review of the studies published between March 2020 and December 2022 was conducted using the MEDLINE, Scopus, Embase, and ProQuest databases. The database search yielded an initial sample of 455 potential publications for analysis, of which 9 (2%) met the inclusion criteria. The methodology of this review was based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses).

RESULTS

The factors influencing mHealth acceptance and adoption by physicians were divided into perceived barriers and perceived facilitators, which were further grouped into the following 3 major thematic categories: technological, individual, and organizational barriers and facilitators, respectively. The technological barriers were accessibility, technical issues, usefulness, and data management; individual barriers were perceived patient barriers, time and workload pressure, technical literacy, knowledge of mHealth, and peer support; and organizational barriers were financial factors, management support and engagement, data security, telemonitoring policy, and collaboration. The technological facilitators of uptake were technical factors, clinical usefulness, and data management; individual facilitators were patient-related care, intrinsic motivation, collaboration, and data sharing (individual); and organizational facilitators were workflow-related determinants, organizational financial support, recommendation of mHealth services, and evidence-based guidelines.

CONCLUSIONS

This review summarized the evidence on the factors influencing mHealth acceptance and adoption by physicians during the COVID-19 pandemic. The main findings highlighted the importance of addressing organizational readiness to support physicians with adequate resources, shifting the focus from technological to patient-centered factors, and the seamless integration of mHealth into routine practice during and beyond the pandemic.

TRIAL REGISTRATION

PROSPERO CRD42022356125; https://tinyurl.com/2mmhn5yu.

Mapping digital health ecosystems in Africa in the context of endemic infectious and non-communicable diseases

Investments in digital health technologies such as artificial intelligence, wearable devices, and telemedicine may support Africa achieve United Nations (UN) Sustainable Development Goal for Health by 2030. We aimed to characterize and map digital health ecosystems of all 54 countries in Africa in the context of endemic infectious and non-communicable diseases (ID and NCD). We performed a cross-national ecological analysis of digital health ecosystems using 20-year data from the World Bank, UN Economic Commission for Africa, World Health Organization, and Joint UN Programme on HIV/AIDS. Spearman's rank correlation coefficients were used to characterize ecological correlations between exposure (technology characteristics) and outcome (IDs and NCDs incidence/mortality) variables. Weighted linear combination model was used as the decision rule, combining disease burden, technology access, and economy, to explain, rank, and map digital health ecosystems of a given country. The perspective of our analysis was to support government decision-making. The 20-year trend showed that technology characteristics have been steadily growing in Africa, including internet access, mobile cellular and fixed broadband subscriptions, high-technology manufacturing, GDP per capita, and adult literacy, while many countries have been overwhelmed by a double burden of IDs and NCDs. Inverse correlations exist between technology characteristics and ID burdens, such as fixed broadband subscription and incidence of tuberculosis and malaria, or GDP per capita and incidence of tuberculosis and malaria. Based on our models, countries that should prioritize digital health investments were South Africa, Nigeria, and Tanzania for HIV; Nigeria, South Africa, and Democratic Republic of the Congo (DROC) for tuberculosis; DROC, Nigeria, and Uganda for malaria; and Egypt, Nigeria, and Ethiopia for endemic NCDs including diabetes, cardiovascular disease, respiratory diseases, and malignancies. Countries such as Kenya, Ethiopia, Zambia, Zimbabwe, Angola, and Mozambique were also highly affected by endemic IDs. By mapping digital health ecosystems in Africa, this study provides strategic guidance about where governments should prioritize digital health technology investments that require preliminary analysis of country-specific contexts to bring about sustainable health and economic returns. Building digital infrastructure should be a key part of economic development programs in countries with high disease burdens to ensure more equitable health outcomes. Though infrastructure developments alongside digital health technologies are the responsibility of governments, global health initiatives can cultivate digital health interventions substantially by bridging knowledge and investment gaps, both through technology transfer for local production and negotiation of prices for large-scale deployment of the most impactful digital health technologies.

Digital health in the era of COVID-19: Reshaping the next generation of healthcare

COVID-19 is one of the most deadly diseases to have stricken us in recent decades. In the fight against this disease, governments and stakeholders require all the assistance they can get from various systems, including digital health interventions. Digital health technologies are supporting the tracking of the COVID-19 outbreak, diagnosing patients, expediting the process of finding potential medicines and vaccines, and disinfecting the environment, The establishment of electronic medical and health records, computerized clinical decision support systems, telemedicine, and mobile health have shown the potential to strengthen the healthcare system. Recently, these technologies have aided the health sector in a variety of ways, including prevention, early diagnosis, treatment adherence, medication safety, care coordination, documentation, data management, outbreak tracking, and pandemic surveillance. On the other hand, implementation of such technologies has questions of cost, compatibility with existing systems, disruption in patient-provider interactions, and sustainability, calling for more evidence on clinical utility and economic evaluations to help shape the next generation of healthcare. This paper argues how digital health interventions assist in the fight against COVID-19 and their opportunities, implications, and limitations.

Patient-reported usability and satisfaction with electronic medication event reminder and monitor device for tuberculosis: a multicentre, randomised controlled trial

Background

The success of a tuberculosis digital adherence technology relies on patients' satisfaction with and the usability of the technology. This study aimed to evaluate treatment satisfaction and usability of a digital medication event reminder and monitor (MERM) device for patients with tuberculosis to address the prespecified secondary endpoint of the SELFTB trial.

Methods

In this multicenter, randomised controlled trial, adults (≥18 years) with new or previously treated, bacteriologically-confirmed, drug-sensitive pulmonary tuberculosis who were eligible to start anti-tuberculosis therapy were recruited from 10 healthcare facilities in Ethiopia. With a computer-generated random number sequence, participants were randomly assigned 1:1 to receive a 15-day tuberculosis medication supply dispensed with an evriMED500® MERM device to self-administer and return every 15 days or the standard in-person DOT. Both arms were followed throughout the standard two-month intensive treatment phase. Treatment was based on the WHO-recommended two-month fixed-dose-combination of first-line anti-tuberculosis drug delivered as a single daily dose (2RHZE). Treatment Satisfaction Questionnaire for Medication version 4 (TSQM 1.4©) was used to measure and compare treatment satisfaction between arms. Adapted System Usability Scale (SUS) was used to assess the usability of the device, with emphasis on ease of use, challenges, benefits, motivation, popularity, and recommendation. The findings were correlated with adherence and clinical endpoints including sputum smear conversion and IsoScreen urine isoniazid test results. This trial is registered with ClinicalTrials.gov, NCT04216420.

Findings

Between June 2, 2020, and June 15, 2021, 337 patients were screened for eligibility, of whom 109 participants enrolled and completed the satisfaction [control (n = 57) and intervention (n = 52) arms] and usability [intervention arm (n = 52)] questionnaires. TSQM 1.4© geometric mean scores were: Effectiveness 73.25 [geometric standard deviation (GSD) 1.28], Side Effects 100, Convenience 63.31 (GSD 1.45), and Global Satisfaction 77.29 (GSD 1.25). TSQM score was significantly higher in the intervention vs the control: Effectiveness [85.78 vs 63.43, 95% CI 1.35 (1.26-1.45), p < 0.001], Convenience [85.41 vs 48.18, 95% CI 1.77 (1.63-1.93), p < 0.001], and Global Satisfaction [90.19 vs 67.11, 95% CI 1.34 (1.26-1.43), p < 0.001]. There were significant associations between Global Satisfaction and medication adherence (p = 0.017). Average SUS score was 97.45%, which was close to the best imaginable SUS value of 100%. Likelihood to Recommend (LTR) value was ≥9, on a scale of 0-10, for 90.4% of MERM users, yielding higher net promoters. There was no significant association between usability and medication adherence (p = 0.691).

Interpretation

Our findings suggested that treatment satisfaction scores were superior in the intervention vs control arms and across the domains of Effectiveness, Convenience, and Global Satisfaction. There was excellent usability of the MERM device and a significantly higher number of users likely to promote the device. High tuberculosis burden countries may transform patient-centered care through ongoing evaluation and scale-up of digital health innovations.

Funding

U.S. National Institute of Health (NIH) Fogarty International Center and National Institute of Allergy and Infectious Diseases (D43 TW009127) and the Emory Center for AIDS Research (P30 AI050409).

A qualitative process evaluation within a clinical trial that used healthcare technologies for children with asthma-insights and implications

BACKGROUND

Healthcare technologies are becoming more commonplace, however clinical and patient perspectives regarding the use of technology in the management of childhood asthma have yet to be investigated. Within a clinical trial of asthma management in children, we conducted a qualitative process evaluation that provided insights into the experiences and perspectives of healthcare staff and families on (i) the use of smart inhalers to monitor medication adherence and (ii) the use of algorithm generated treatment recommendations.

METHODS

We interviewed trial staff (n = 15) and families (n = 6) who were involved in the trial to gauge perspectives around the use of smart inhalers to monitor adherence and the algorithm to guide clinical decision making.

FINDINGS

Staff and families indicated that there were technical issues associated with the smart inhalers. While staff suggested that the smart inhalers were good for monitoring adherence and enabling communication regarding medication use, parents and children indicated that smart inhaler use increased motivation to adhere to medication and provided the patient (child) with a sense of responsibility for the management of their asthma. Staff were open-minded about the use of the algorithm to guide treatment recommendations, but some were not familiar with its' use in clinical care. There were some concerns expressed regarding treatment step-down decisions generated by the algorithm, and some staff highlighted the importance of using clinical judgement. Families perceived the algorithm to be a useful technology, but indicated that they felt comforted by the clinicians' own judgements.

CONCLUSION

The use of technology and individual data within appointments was considered useful to both staff and families: closer monitoring and the educational impacts were especially highlighted. Utilising an algorithm was broadly acceptable, with caveats around clinicians using the recommendations as a guide only and wariness around extreme step-ups/downs considering contextual factors not taken into account.

Effectiveness of a digital medication event reminder and monitor device for patients with tuberculosis (SELFTB): a multicenter randomized controlled trial

BACKGROUND

Tuberculosis remains the leading cause of death from a single infectious disease worldwide. Trials evaluating digital adherence technologies for tuberculosis in low- and middle-income countries are urgently needed. We aimed to assess whether a digital medication event reminder and monitor (MERM) device-observed self-administered therapy improves adherence and treatment outcomes in patients with tuberculosis compared with the standard in-person directly observed therapy (DOT).

METHODS

We did a two-arm, attention-controlled, effectiveness-implementation type 2 hybrid, randomized controlled trial in ten healthcare facilities in Addis Ababa, Ethiopia. We included adults with new or previously treated, bacteriologically confirmed, drug-sensitive pulmonary tuberculosis who were eligible to start anti-tuberculosis therapy. Participants were randomly assigned (1:1) to receive a 15-day tuberculosis medication supply in the evriMED500® MERM device to self-administer and return every 15 days (intervention arm) or visit the healthcare facilities each day to swallow their daily dose with DOT by healthcare providers (control arm). Both arms were followed throughout the standard two-month intensive treatment phase (2RHZE). For control participants, some provider-approved take-home doses might be allowed for extenuating circumstances in real-world practice. Data were collected on patient information (demographic, socioeconomic, behavioral, social, and clinical information), medication adherence measures (MERM vs. DOT records, IsoScreenTM urine colorimetric isoniazid test, and adherence self-report), and clinical measures (pre-post treatment sputum Xpert MTB/RIF assay or microscopy, and adverse treatment outcomes). The intention-to-treat (ITT) primary endpoints were (1) individual-level percentage adherence over the two-month intensive phase measured by adherence records compiled from MERM device vs. DOT records that also considered all take-home doses as having been ingested and (2) sputum smear conversion following the standard two-month intensive phase treatment. Secondary endpoints were (1) individual-level percentage adherence over the two-month intensive phase measured by adherence records compiled from the MERM device vs. DOT records that considered all take-home doses as not ingested, (2) negative IsoScreen urine isoniazid test, (3) adverse treatment outcome (having at least one of the three events: treatment not completed; death; or loss to follow-up), and (4) self-reported adherence. The MERM device has an electronic module and a medication container that records adherence, stores medication, emits audible and visual on-board alarms to remind patients to take their medications on time and refill, and enables providers to download the data and monitor adherence.

RESULTS

Participants were enrolled into the study between 02 June 2020 and 15 June 2021, with the last participant completing follow-up on 15 August 2021. A total of 337 patients were screened for eligibility, of whom 114 were randomly assigned and included in the final analysis [57 control and 57 intervention participants]. Participants were 64.9% male, 15% with HIV, 10.5% retreatment, and 5.3% homeless. Adherence to TB medication was comparable between the intervention arm [geometric mean percentage (GM%) 99.01%, geometric standard deviation (GSD) 1.02] and the control arm [GM% 98.97%, GSD 1.04] and was within the prespecified margin for non-inferiority [mean ratio (MR) 1.00 (95% CI 0.99-1.01); p = 0.954]. The intervention arm was significantly superior to the control arm in the secondary analysis that considered all take-home doses in the control were not ingested [control GM% 77.71 (GSD 1.57), MR 1.27 (95% CI 1.33-1.43)]. Urine isoniazid testing was done on 443 (97%) samples from 114 participants; 13 participants had at least one negative result; a negative test was significantly more common among the control group compared with the intervention group [11/57 (19.3%) vs 2/57 (3.5%); p = 0.008]. There was no significant difference between the control and intervention arms for smear conversion [55 (98.2%) vs 52 (100%); p>0.999], adverse treatment outcomes [0 vs 1 (1.9%); p = 0.48], and self-report non-adherence [5 (8.9%) vs 1 (1.9%); p = 0.21].

CONCLUSIONS

In this randomized trial of patients with drug-sensitive pulmonary tuberculosis, medication adherence among participants assigned to MERM-observed self-administered therapy was non-inferior and superior by some measures when compared with the standard in-person DOT. Further research is needed to understand whether adherence in the intervention is primarily driven by allowing self-administered therapy which reduced challenges of repeated clinic visits or by the adherence support provided by the MERM system. To avoid contributing to patient barriers with DOT, tuberculosis medical programs should consider alternatives such as medication event monitors.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04216420.