Showing 1 to 10 of 3706 records(fetched in 1.249 seconds)
TitleThe effect of a clinical decision-making mHealth support system on maternal and neonatal mortality and morbidity in Ghana: study protocol for a cluster randomized controlled trial.
AuthorsAmoakoh, HB; Klipstein-Grobusch, K; Amoakoh-Coleman, M; Agyepong, IA; Kayode, GA; Sarpong, C; Grobbee, DE; Ansah, EK
Publication Date4 Apr 2017
Date Added to PubMed5 Apr 2017
AbstractMobile health (mHealth) presents one of the potential solutions to maximize health worker impact and efficiency in an effort to reach the Sustainable Development Goals 3.1 and 3.2, particularly in sub-Saharan African countries. Poor-quality clinical decision-making is known to be associated with poor pregnancy and birth outcomes. This study aims to assess the effect of a clinical decision-making support system (CDMSS) directed at frontline health care providers on neonatal and maternal health outcomes. A cluster randomized controlled trial will be conducted in 16 eligible districts (clusters) in the Eastern Region of Ghana to assess the effect of an mHealth CDMSS for maternal and neonatal health care services on maternal and neonatal outcomes. The CDMSS intervention consists of an Unstructured Supplementary Service Data (USSD)-based text messaging of standard emergency obstetric and neonatal protocols to providers on their request. The primary outcome of the intervention is the incidence of institutional neonatal mortality. Outcomes will be assessed through an analysis of data on maternal and neonatal morbidity and mortality extracted from the District Health Information Management System-2 (DHIMS-2) and health facility-based records. The quality of maternal and neonatal health care will be assessed in two purposively selected clusters from each study arm. In this trial the effect of a mobile CDMSS on institutional maternal and neonatal health outcomes will be evaluated to generate evidence-based recommendations for the use of mobile CDMSS in Ghana and other West African countries., identifier: NCT02468310 . Registered on 7 September 2015; Pan African Clinical Trials Registry, identifier: PACTR20151200109073 . Registered on 9 December 2015 retrospectively from trial start date.
TitleCan an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?
AuthorsAmoakoh, HB; Klipstein-Grobusch, K; Agyepong, IA; Amoakoh-Coleman, M; Kayode, GA; Reitsma, JB; Grobbee, DE; Ansah, EK
JournalBMC pediatrics
Publication Date27 Nov 2020
Date Added to PubMed28 Nov 2020
AbstractThis study assessed health workers' adherence to neonatal health protocols before and during the implementation of a mobile health (mHealth) clinical decision-making support system (mCDMSS) that sought to bridge access to neonatal health protocol gap in a low-resource setting. We performed a cross-sectional document review within two purposively selected clusters (one poorly-resourced and one well-resourced), from each arm of a cluster-randomized trial at two different time points: before and during the trial. The total trial consisted of 16 clusters randomized into 8 intervention and 8 control clusters to assess the impact of an mCDMSS on neonatal mortality in Ghana. We evaluated health workers' adherence (expressed as percentages) to birth asphyxia, neonatal jaundice and cord sepsis protocols by reviewing medical records of neonatal in-patients using a checklist. Differences in adherence to neonatal health protocols within and between the study arms were assessed using Wilcoxon rank-sum and permutation tests for each morbidity type. In addition, we tracked concurrent neonatal health improvement activities in the clusters during the 18-month intervention period. In the intervention arm, mean adherence was 35.2% (SD = 5.8%) and 43.6% (SD = 27.5%) for asphyxia; 25.0% (SD = 14.8%) and 39.3% (SD = 27.7%) for jaundice; 52.0% (SD = 11.0%) and 75.0% (SD = 21.2%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. In the control arm, mean adherence was 52.9% (SD = 16.4%) and 74.5% (SD = 14.7%) for asphyxia; 45.1% (SD = 12.8%) and 64.6% (SD = 8.2%) for jaundice; 53.8% (SD = 16.0%) and 60.8% (SD = 11.7%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. We observed nonsignificant improvement in protocol adherence in the intervention clusters but significant improvement in protocol adherence in the control clusters. There were 2 concurrent neonatal health improvement activities in the intervention clusters and over 12 in the control clusters during the intervention period. Whether mHealth interventions can improve adherence to neonatal health protocols in low-resource settings cannot be ascertained by this study. Neonatal health improvement activities are however likely to improve protocol adherence. Future mHealth evaluations of protocol adherence must account for other concurrent interventions in study contexts.
TitleAdvancing beyond the system: telemedicine nurses' clinical reasoning using a computerised decision support system for patients with COPD - an ethnographic study.
AuthorsBarken, TL; Thygesen, E; Söderhamn, U
JournalBMC medical informatics and decision making
Publication Date28 Dec 2017
Date Added to PubMed29 Dec 2017
AbstractTelemedicine is changing traditional nursing care, and entails nurses performing advanced and complex care within a new clinical environment, and monitoring patients at a distance. Telemedicine practice requires complex disease management, advocating that the nurses' reasoning and decision-making processes are supported. Computerised decision support systems are being used increasingly to assist reasoning and decision-making in different situations. However, little research has focused on the clinical reasoning of nurses using a computerised decision support system in a telemedicine setting. Therefore, the objective of the study is to explore the process of telemedicine nurses' clinical reasoning when using a computerised decision support system for the management of patients with chronic obstructive pulmonary disease. The factors influencing the reasoning and decision-making processes were investigated. In this ethnographic study, a combination of data collection methods, including participatory observations, the think-aloud technique, and a focus group interview was employed. Collected data were analysed using qualitative content analysis. When telemedicine nurses used a computerised decision support system for the management of patients with complex, unstable chronic obstructive pulmonary disease, two categories emerged: "the process of telemedicine nurses' reasoning to assess health change" and "the influence of the telemedicine setting on nurses' reasoning and decision-making processes". An overall theme, termed "advancing beyond the system", represented the connection between the reasoning processes and the telemedicine work and setting, where being familiar with the patient functioned as a foundation for the nurses' clinical reasoning process. In the telemedicine setting, when supported by a computerised decision support system, nurses' reasoning was enabled by the continuous flow of digital clinical data, regular video-mediated contact and shared decision-making with the patient. These factors fostered an in-depth knowledge of the patients and acted as a foundation for the nurses' reasoning process. Nurses' reasoning frequently advanced beyond the computerised decision support system recommendations. Future studies are warranted to develop more accurate algorithms, increase system maturity, and improve the integration of the digital clinical information with clinical experiences, to support telemedicine nurses' reasoning process.
TitleUsing Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs.
AuthorsAmoakoh, HB; Klipstein-Grobusch, K; Grobbee, DE; Amoakoh-Coleman, M; Oduro-Mensah, E; Sarpong, C; Frimpong, E; Kayode, GA; Agyepong, IA; Ansah, EK
JournalJMIR mHealth and uHealth
Publication Date24 May 2019
Date Added to PubMed28 May 2019
AbstractDeveloping and maintaining resilient health systems in low-resource settings like Ghana requires innovative approaches that adapt technology to context to improve health outcomes. One such innovation was a mobile health (mHealth) clinical decision-making support system (mCDMSS) that utilized text messaging (short message service, SMS) of standard emergency maternal and neonatal protocols via an unstructured supplementary service data (USSD) on request of the health care providers. This mCDMSS was implemented in a cluster randomized controlled trial (CRCT) in the Eastern Region of Ghana. This study aimed to analyze the pattern of requests made to the USSD by health workers (HWs). We assessed the relationship between requests made to the USSD and types of maternal and neonatal morbidities reported in health facilities (HFs). For clusters in the intervention arm of the CRCT, all requests to the USSD during the 18-month intervention period were extracted from a remote server, and maternal and neonatal health outcomes of interest were obtained from the District Health Information System of Ghana. Chi-square and Fisher exact tests were used to compare the proportion and type of requests made to the USSD by cluster, facility type, and location; whether phones accessing the intervention were shared facility phones or individual-use phones (type-of-phone); or whether protocols were accessed during the day or at night (time-of-day). Trends in requests made were analyzed over 3 6-month periods. The relationship between requests made and the number of cases reported in HFs was assessed using Spearman correlation. In total, 5329 requests from 72 (97%) participating HFs were made to the intervention. The average number of requests made per cluster was 667. Requests declined from the first to the third 6-month period (44.96% [2396/5329], 39.82% [2122/5329], and 15.22% [811/5329], respectively). Maternal conditions accounted for the majority of requests made (66.35% [3536/5329]). The most frequently accessed maternal conditions were postpartum hemorrhage (25.23% [892/3536]), other conditions (17.82% [630/3536]), and hypertension (16.49% [583/3536]), whereas the most frequently accessed neonatal conditions were prematurity (20.08% [360/1793]), sepsis (15.45% [277/1793]), and resuscitation (13.78% [247/1793]). Requests made to the mCDMSS varied significantly by cluster, type of request (maternal or neonatal), facility type and its location, type-of-phone, and time-of-day at 6-month interval (P<.001 for each variable). Trends in maternal and neonatal requests showed varying significance over each 6-month interval. Only asphyxia and sepsis cases showed significant correlations with the number of requests made (r=0.44 and r=0.79; P<.001 and P=.03, respectively). There were variations in the pattern of requests made to the mCDMSS over time. Detailed information regarding the use of the mCDMSS provides insight into the information needs of HWs for decision-making and an opportunity to focus support for HW training and ultimately improved maternal and neonatal health.
TitleDesigning a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach.
AuthorsTimotijevic, L; Hodgkins, CE; Banks, A; Rusconi, P; Egan, B; Peacock, M; Seiss, E; Touray, MML; Gage, H; Pellicano, C; Spalletta, G; Assogna, F; Giglio, M; Marcante, A; Gentile, G; Cikajlo, I; Gatsios, D; Konitsiotis, S; Fotiadis, D
JournalBMC medical informatics and decision making
Publication Date19 Feb 2020
Date Added to PubMed23 Feb 2020
AbstractDespite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson's disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson's disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians' (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians' (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. Study 1 indicated that system development should move away from the traditional silos of 'motor' and 'non-motor' symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients' overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient's condition from the patient's self-report and the wearable devices. Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.
TitleUse of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study.
AuthorsLangford, A; Orellana, K; Kalinowski, J; Aird, C; Buderer, N
JournalJMIR mHealth and uHealth
Publication Date12 Aug 2020
Date Added to PubMed14 Aug 2020
AbstractTablet and smartphone ownership have increased among US adults over the past decade. However, the degree to which people use mobile devices to help them make medical decisions remains unclear. The objective of this study is to explore factors associated with self-reported use of tablets or smartphones to support medical decision making in a nationally representative sample of US adults. Cross-sectional data from participants in the 2018 Health Information National Trends Survey (HINTS 5, Cycle 2) were evaluated. There were 3504 responses in the full HINTS 5 Cycle 2 data set; 2321 remained after eliminating respondents who did not have complete data for all the variables of interest. The primary outcome was use of a tablet or smartphone to help make a decision about how to treat an illness or condition. Sociodemographic factors including gender, race/ethnicity, and education were evaluated. Additionally, mobile health (mHealth)- and electronic health (eHealth)-related factors were evaluated including (1) the presence of health and wellness apps on a tablet or smartphone, (2) use of electronic devices other than tablets and smartphones to monitor health (eg, Fitbit, blood glucose monitor, and blood pressure monitor), and (3) whether people shared health information from an electronic monitoring device or smartphone with a health professional within the last 12 months. Descriptive and inferential statistics were conducted using SAS version 9.4. Weighted population estimates and standard errors, univariate odds ratios, and 95% CIs were calculated, comparing respondents who used tablets or smartphones to help make medical decisions (n=944) with those who did not (n=1377), separately for each factor. Factors of interest with a P value of <.10 were included in a subsequent multivariable logistic regression model. Compared with women, men had lower odds of reporting that a tablet or smartphone helped them make a medical decision. Respondents aged 75 and older also had lower odds of using a tablet or smartphone compared with younger respondents aged 18-34. By contrast, those who had health and wellness apps on tablets or smartphones, used other electronic devices to monitor health, and shared information from devices or smartphones with health care professionals had higher odds of reporting that tablets or smartphones helped them make a medical decision, compared with those who did not. A limitation of this research is that information was not available regarding the specific health condition for which a tablet or smartphone helped people make a decision or the type of decision made (eg, surgery, medication changes). In US adults, mHealth and eHealth use, and also certain sociodemographic factors are associated with using tablets or smartphones to support medical decision making. Findings from this study may inform future mHealth and other digital health interventions designed to support medical decision making.
TitleDecision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review.
AuthorsKim, Y; Groombridge, C; Romero, L; Clare, S; Fitzgerald, MC
JournalJournal of medical Internet research
Publication Date8 Dec 2020
Date Added to PubMed9 Dec 2020
AbstractTelemedicine offers a unique opportunity to improve coordination and administration for urgent patient care remotely. In an emergency setting, it has been used to support first responders by providing telephone or video consultation with specialists at hospitals and through the exchange of prehospital patient information. This technological solution is evolving rapidly, yet there is a concern that it is being implemented without a demonstrated clinical need and effectiveness as well as without a thorough economic evaluation. Our objective is to systematically review whether the clinical outcomes achieved, as reported in the literature, favor telemedicine decision support for medical interventions during prehospital care. This systematic review included peer-reviewed journal articles. Searches of 7 databases and relevant reviews were conducted. Eligibility criteria consisted of studies that covered telemedicine as data- and information-sharing and two-way teleconsultation platforms, with the objective of supporting medical decisions (eg, diagnosis, treatment, and receiving hospital decision) in a prehospital emergency setting. Simulation studies and studies that included pediatric populations were excluded. The procedures in this review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool was used for the assessment of risk of bias. The results were synthesized based on predefined aspects of medical decisions that are made in a prehospital setting, which include diagnostic decision support, receiving facility decisions, and medical directions for treatment. All data extractions were done by at least two reviewers independently. Out of 42 full-text reviews, 7 were found eligible. Diagnostic support and medical direction and decision for treatments were often reported. A key finding of this review was the high agreement between prehospital diagnoses via telemedicine and final in-hospital diagnoses, as supported by quantitative evidence. However, a majority of the articles described the clinical value of having access to remote experts without robust quantitative data. Most telemedicine solutions were evaluated within a feasibility or short-term preliminary study. In general, the results were positive for telemedicine use; however, biases, due to preintervention confounding factors and a lack of documentation on quality assurance and protocol for telemedicine activation, make it difficult to determine the direct effect on patient outcomes. The information-sharing capacity of telemedicine enables access to remote experts to support medical decision making on scene or in prolonged field care. The influence of human and technology factors on patient care is poorly understood and documented.
TitlemHealth for Clinical Decision-Making in Sub-Saharan Africa: A Scoping Review.
AuthorsAdepoju, IO; Albersen, BJ; De Brouwere, V; van Roosmalen, J; Zweekhorst, M
JournalJMIR mHealth and uHealth
Publication Date23 Mar 2017
Date Added to PubMed25 Mar 2017
AbstractIn a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for example, is established in resource-rich settings. However, the extent to which mobile clinical decision support systems (mCDSS) have been adopted specifically in resource-poor settings such as Africa and the lessons learned about their use in such settings are yet to be established. The aim of this study was to synthesize evidence on the use of mHealth for point-of-care decision support and improved quality of care by health care workers in Africa. A scoping review of 4 peer-reviewed and 1 grey literature databases was conducted. No date limits were applied, but only articles in English language were selected. Using pre-established criteria, 2 reviewers screened articles and extracted data. Articles were analyzed using Microsoft Excel and MAXQDA. We retained 22 articles representing 11 different studies in 7 sub-Saharan African countries. Interventions were mainly in the domain of maternal health and ranged from simple text messaging (short message service, SMS) to complex multicomponent interventions. Although health workers are generally supportive of mCDSS and perceive them as useful, concerns about increased workload and altered workflow hinder sustainability. Facilitators and barriers to use of mCDSS include technical and infrastructural support, ownership, health system challenges, and training. The use of mCDSS in sub-Saharan Africa is an indication of progress in mHealth, although their effect on quality of service delivery is yet to be fully explored. Lessons learned are useful for informing future research, policy, and practice for technologically supported health care delivery, especially in resource-poor settings.
TitleTrial of telemedicine for patients on home ventilator support: feasibility, confidence in clinical management and use in medical decision-making.
AuthorsCasavant, DW; McManus, ML; Parsons, SK; Zurakowski, D; Graham, RJ
JournalJournal of telemedicine and telecare
Publication Date1 Dec 2014
Date Added to PubMed16 Oct 2014
AbstractWe investigated whether telemedicine (videoconferencing) was feasible in patients with special care needs on home ventilation, whether it affected the confidence of families about the clinical management of their child, and whether it supported clinical decision-making. Videoconferencing software was provided free for 14 families who had a computer and webcam. Families completed questionnaires about clinical management before the addition of telemedicine and 2-3 months after they had used telemedicine. They also completed a questionnaire about their experience with videoconferencing. There were 27 telemedicine encounters during the 9-month study. Families reported higher confidence in clinical care with telemedicine compared to telephone. They also reported that the videoconferencing was high-quality, easy to use, and did not increase their telecommunication costs. The telemedicine encounters supported clinical decision-making, especially in patients with active clinical problems or when the patient was acutely ill. The telemedicine encounters prevented the need for 23 clinic visits, three emergency room visits, and probably one hospital admission. Although the study was small, videoconferencing appears useful in the management of medically fragile patients on home ventilator support, producing high levels of family confidence in clinical management and value to clinicians in their decision-making.
TitleClinical decision making in basic emergency obstetric and newborn care among nurses and midwives: the role of the safe delivery mhealth application_pre-post-intervention study (research protocol).
AuthorsNishimwe, A; Nyssen, M; Ibisomi, L; Nozizwe Conco, D
JournalInformatics for health & social care
Publication Date2 Jun 2021
Date Added to PubMed12 Jan 2021
AbstractMost maternal and newborn deaths in low-income countries, including Rwanda, are attributable to preventable causes. Timely access to Basic Emergency Obstetric and Newborn Care (BEmONC) guidelines to support clinical decisions could lead to better obstetric care thus reduction of maternal and newborn deaths. Besides, innovative methods such as the usage and reference to healthcare guidelines using mobile devices (mhealth) may support clinical decision making. However, there is little evidence about mhealth that focuses on the clinical decision support process. This proposal aims to investigate the effect of the Safe Delivery mhealth Application(SDA) on nurses' and midwives' clinical decision making, so as to inform mhealth interventions for work in specific contexts. The study adopts a quasi-experimental design. Convergent parallel mixed - methods will be used to collect, analyze and interpret data. A pre-intervention assessment of the BEmONC outcomes: Apgar score and PPH progressions, and related knowledge, skills, and perceptions of nurses and midwives will be conducted. The intervention will take place in two district hospitals in Rwanda and entails the implementation of the SDA for six months. Six months' post-intervention, the effect of the SDA on BEmONC outcomes and the nurses' and midwives' knowledge and skills will be evaluated.
MNCHFPRHHIV/AIDSMalariaNoncommunicable diseaseCOVID-19Decision-makingEducation & trainingBehavior changeGovernancePrivacy & securityEquityCHWsYouth & adolescentsSystematic reviewsProtocols & research designMedical RecordsLaboratoryPharmacyHuman ResourcesmHealthSMSChatbotsAI