{"doi":"10.1093/oodh/oqae025","title":"Crossing the digital divide: the workload of manual data entry and integration between mobile health applications and eHealth infrastructure","abstract":"Abstract Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions and sustained funding. Manual data linkage processes are commonly used to ‘integrate’ across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood. As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data were analysed in Excel. We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone. Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement. RESUMEN Muchas intervenciones de salud digital (ISD), aplicaciones de salud móvil (mSalud) incluídas, aspiran a mejorar tanto los resultados de los clientes como la eficiencia, con sistemas de historias clínicas electrónicas (SHCE), por ejemplo. Aunque la interoperabilidad es un ideal al cual apuntar, es, sin embargo, compleja y costosa, y requiere pericia técnica, permisos de partes interesadas, y financiamiento sostenido. Procesos de enlace o vinculación manual de datos se usan comúnmente para ‘integrar’ a través de sistemas y así permitir evaluar el impacto de las ISD, una ‘mejor práctica’, antes de continuar o incrementar una inversión. En la mSalud, la carga de trabajo que supone vincular manualmente los datos, incluyendo actividades de monitoreo y evaluación (M&E), sigue sin entenderse del todo. Como un estudio de base para una aplicación de código abierto que refleje SHCE y reduzca la carga de trabajo de los prestadores de salud (PS) mientras mejora el cuidado brindado por el Programa Comunitario dirigido por Enfermeras de terapia Anti-retroviral (PCEA) en Lilongwe, Malaui, condujimos un estudio de tiempos y movimientos observando a PS completar actividades de manejo de datos, incluyendo M&E de rutina y la vinculación manual de datos, de las aplicaciones de nivel individual a los SHCE. Las tareas de manejo de datos deberán reducirse o cesar del todo con la exitosa implementación de la aplicación y la integración de los SHCE. Los datos fueron analizados en Excel. Observamos 69:53:00 de PS realizando tareas rutinarias de servicio del PCEA: 39:52:00 (57%) se usaron para completar labores relacionadas a datos de M&E, de los que 15:57:00 (23%) se gastaron en tan sólo enlazar datos a mano. Entender la carga de trabajo que supone asegurar la calidad de los datos de M&E, incluyendo la vinculación manual de datos entre las aplicaciones de mSalud y los SHCE, provee a las partes interesadas de información que puede empujar a la innovación en ISD y guiar la toma de decisiones sobre integración. El cuantificar beneficios potenciales de mSalud con datos de M&E de alta calidad y más eficientes, puede disparar la aparición de nuevas innovaciones que reduzcan carga","journal":"Oxford Open Digital Health","year":2024,"id":458982,"datarank":0.3854914760551949,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.11672755567098664,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.11672755567098664,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":3,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9536,"is_data_producer":true,"deposit_databanks":{"Dryad":["10.5061/dryad.66t1g1k8q"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1286214,"name":"Joel Usiri","orcid":null,"position":1,"is_corresponding":false},{"id":1058570,"name":"Christine Kiruthu-Kamamia","orcid":"0009-0008-7682-2496","position":2,"is_corresponding":false},{"id":1285796,"name":"Geetha M. Waehrer","orcid":"0000-0001-8169-2958","position":3,"is_corresponding":false},{"id":1286215,"name":"Hiwot Weldemariam","orcid":null,"position":4,"is_corresponding":false},{"id":1058568,"name":"Jacqueline Huwa","orcid":"0000-0001-8786-2487","position":5,"is_corresponding":false},{"id":1286216,"name":"Jessie Hau","orcid":null,"position":6,"is_corresponding":false},{"id":1240499,"name":"Agness Thawani","orcid":"0009-0007-1221-0901","position":7,"is_corresponding":false},{"id":1286217,"name":"Mirriam Chapanda","orcid":null,"position":8,"is_corresponding":false},{"id":597212,"name":"Hannock Tweya","orcid":"0000-0002-8247-1273","position":9,"is_corresponding":false},{"id":467848,"name":"Caryl Feldacker","orcid":"0000-0002-8152-6754","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T02:03:55.280882Z","pmid":"40191682","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}