Implementation of Telemedicine Services in Lower-Middle Income Countries: Lessons for the Philippines

Regardless of the promising potential of telemedicine to address healthcare problems, especially in lower-middle income countries, its success rate has been unsatisfactory and many telemedicine services fail to sustain their implementation shortly after initial funding or after a pilot phase. Therefore, it is important to document existing models of telemedicine implementation in these countries, to identify commonalities and extract experiences that would be useful for implementers, policy makers and future researchers. This review seeks to review and describe the experience of Low and Middle Income Countries (LMICs) in implementing telemedicine services. Evidence extracted from the included studies were analysed through a narrative synthesis which suggests a multi-sectoral approach for implementing telemedicine. It highlights the importance of education, financing options, policy, technology, governance, and partnership, in the wider picture of a sustainable telemedicine implementation among developing countries such as the Philippines. Moreover, the literature reveals both top-down and bottom-up approach for successful telemedicine implementation. These approaches include strengthening the local health workers and integrating telemedicine into the health system. Studies included in this review have been helpful, but there is an obvious lack of studies with high level of evidence that can yield generalisable, thus findings must be inferred with prudence. Even so, this review described and summarised the data which allowed description of factors and lessons in the implementation of telemedicine in LMICs

Computer-vision based method for quantifying rising from chair in Parkinson's disease patients

BACKGROUND: The ability to arise from a sitting to a standing position is often impaired in Parkinson's disease (PD). This impairment is associated with an increased risk of falling, and higher risk of dementia. We propose a novel approach to estimate Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) ratings for “item 3.9” (arising from chair) using a computer vision-based method, whereby we use clinically informed reasoning to engineer a small number of informative features from high dimensional markerless pose estimation data. METHODS: We analysed 447 videos collected via the KELVIN-PD™ platform, recorded in clinical settings at multiple sites, using commercially available mobile smart devices. Each video showed an examination for item 3.9 of the MDS-UPDRS and had an associated severity rating from a trained clinician on the 5-point scale (0, 1, 2, 3 or 4). The deep learning library OpenPose was used to extract pose estimation key points from each frame of the videos, resulting in time-series signals for each key point. From these signals, features were extracted which capture relevant characteristics of the movement; velocity variation, smoothness, whether the patient used their hands to push themselves up, how stooped the patient was while sitting and how upright the patient was when fully standing. These features were used to train an ordinal classification system (with one class for each of the possible ratings on the UPDRS), based on a series of random forest classifiers. RESULTS: The UPDRS ratings estimated by this system, using leave-one-out cross validation, corresponded exactly to the ratings made by clinicians in 79% of videos, and were within one of those made by clinicians in 100% of cases. The system was able to distinguish normal from Parkinsonian movement with a sensitivity of 62.8% and a specificity of 90.3%. Analysis of misclassified examples highlighted the potential of the system to detect potentially mislabelled data. CONCLUSION: We show that our computer-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action. As far as we are aware this is the first study estimating scores for item 3.9 of the MDS-UPDRS from singular monocular video. This approach can help prevent human error by identifying unusual clinician ratings, and provides promise for such a system being used routinely for clinical assessments, either locally or remotely, with potential for use as stratification and outcome measures in clinical trials

JoinSTNassistant Framework: An Agile Holistic Framework for Assisting Decision in Healthcare Facilities to Join Saudi Telemedicine Network

In 2011, the Saudi Arabian Ministry of Health (MOH) launched the Saudi Telemedicine Network (STN) as the first national project for telemedicine in the KSA, which is planned to be completed by 2020. The benefits associated with the STN will only be realised through its successful implementation within the Healthcare Facilities (HCFs) across the Kingdom of Saudi Arabia (KSA). There is a high failure rate of implementation projects of telemedicine within other countries (approximately 75% globally, and 90% in developing countries). Furthermore, there is high failure rate of implementation projects of complex Health Information Technology (HIT) systems within HCFs of the KSA (roughly 80%). These dramatic statistics demonstrate the great need for a suitable framework to assist the STN implementation and increase the likelihood of its successful implementation. Prior studies have asserted that there could not be a one-size-fits-all framework that could be applicable and used by all countries for assisting the implementation of telemedicine. To the best of our knowledge, there is not any existing framework that has been specifically developed for assisting the STN implementation. Thus, this research is aimed at developing a novel, agile, holistic framework, referred to as “JoinSTNassistant Framework”, aimed to assist HCFs across the KSA regarding their organisational decision to join the STN. It must be ensured that this JoinSTNassistant Framework is theoretically rigorous, as well as relevant specifically to the context and the needs of the KSA, its HCFs, and the STN roadmap. Therefore, the JoinSTNassistant Framework has been developed through three-sequential phases. The First Phase of development defines and applies the theoretical and philosophical foundations of the JoinSTNassistant Framework. In this First Phase, 56-selected studies from an extensive literature review were analysed. The Second and Third phases of development reflect the practical and pragmatic requirements of the JoinSTNassistant Framework. These two phases must be considered as two stages of validation of the findings of the First Phase, involving as many potential users as possible in the development of the Framework, so as to ensure that it reflects their expectations and meets their needs. The Second Phase of development involved interviews with 81 strategic-level decision makers of HCFs within the KSA. The Third Phase implemented an even higher level of validation, involving as many as 905 potential users, forming a representative sample size of the decision makers of all HCFs across the KSA. In addition, a web-based application (i.e., Portal) for the JoinSTNassistant Framework, referred to as “JoinSTNassistant Portal” was developed for modifying and adjusting the JoinSTNassistant Framework in order to be applicable for each one of HCFs across the KSA, for assisting and guiding them in reaching a decision to join the STN. This research is part of the STN project and is collaborating with the National eHealth Strategy and Change Management Office in the MOH of KSA, and with the STN agency, who is the sponsor and the owner of the STN project