Clinical eHealth最新文献

Background

Telemedicine involves the innovative application of technology to provide remote patient healthcare services especially those relating to emergency care and contagious disease spread. Telemedicine is less developed in low-and-middle-income countries like Pakistan and there is little published literature on its function and efficiency. Our institution was established to triage patients with COVID-19 symptoms to ease the load on emergency departments.

Objective

To conduct an analysis of the first month of function of a telemedicine/tele-triage center in Pakistan. To determine in which ways it was beneficial to hospital management during the COVID-19 pandemic.

Methods

This study was carried out at the newly established telemedicine/tele-triage center at the Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU) in Islamabad Pakistan between the 26th March and the 25th April 2020. Data were collected over telephone interview using a proforma to provide each caller with a C-Score (a C-score of >3 indicated observe at home 3–5-indicated home isolation with confirmatory testing and >5 indicated testing and transfer to hospital) representing their COVID-19 risk and informing the nature of the advice given to them. Data were presented using descriptive statistics.

Results

The center received total of 857 calls. Fever cough dyspnea and flu were present in 327(38.2%) 268(31.3%) 107(12.5%) and 124(14.5%) callers respectively. Based on the completion of the proforma 774(90.3%) callers had a C-Score of > 75(8.8%) callers had a C-Score of 3–5 and 8 (0.9%) callers had a C-Score of >5. We recommended COVID-19 testing in 83 patients (9.68%) based on C-score. Out of these 83 patients 64 underwent testing and only 1 tested positive for COVID-19.

Conclusion

In a one-month period the center was able to support patients by providing a triage service thereby preventing numerous unnecessary hospital visits and helping to protect healthcare professionals during a global pandemic. Telemedicine has great potential to help patient populations in low-and-middle-income countries.

Background

Chronic care patients can play an important role in the self-management of their disease; however, large-scale implementation of self-management has been challenging. To aid and stimulate self-management in primary care, a Dutch cooperation supported healthcare providers in primary care, through collective healthcare groups, who wanted to implement and execute a self-management program. The program aimed to increase the quality of life (QoL) in people with a chronic condition and simultaneously reduce healthcare costs.

Objective

The aim of this study was to assess the public value of the self-management program that was available for approximately 375.000 chronically ill patients.

Methods

A Social Return on Investment analysis was carried out. The analysis is based on the principles of a cost-benefit analysis, and attributes monetary value to the social return of the self-management program in primary care.

Results

The analysis of the self-management program showed that each euro invested translated to a social return of 4.90 euros per patient per year (measured over 5 years). This result was mainly caused by an increase in QoL and a decrease in healthcare costs. Importantly, the results show that costs and benefits were inequitably distributed across the relevant stakeholders.

Discussion

The results show that self-management support programs in primary care can provide a social return on investment. There is a clear need for lasting support for healthcare providers, both material and non-material, to enable successful implementation of self-management programs in practice.

Background

University Hospitals (UHs) are key players in contributing to a sustainable health care system. In the Netherlands the eight UHs joined forces from 2016 till 2018 within the Citrien fund (CF) – program eHealth to develop sustainable eHealth solutions by carrying out 32 research projects.

Objective

The objective of this study was to develop an evaluation study protocol that would be capable of evaluating the first Dutch University Hospitals eHealth program in depth.

Methods

To develop the protocol three consecutive steps were carried out: 1) a rapid review to find suitable eHealth evaluation frameworks and eHealth project progress indicators, 2) assessment of the selected eHealth evaluation frameworks to determine the most suitable framework to evaluate CF - program eHealth, and 3) development of a mixed-methods study to evaluate eHealth project progress indicators in relation to the 32 eHealth research projects.

Results

The ‘Commonwealth Scientific and Industrial Research Organization (CSIRO) framework for evaluating telehealth trials or programs’ was deemed most suitable for evaluating CF - program eHealth. The aspects planning, needs assessment, policy/organization, technology, ethics, legal, and finance, were considered useful indicators for monitoring the progress of an eHealth project, and therefore incorporated into the survey.

Conclusion

The developed evaluation study protocol will be used to evaluate the first Dutch University Hospitals’ eHealth program, the CF – program eHealth, and therewith contribute to maximizing successful uptake of eHealth solutions. Also, the selected set of eHealth project performance indicators could be used by researchers or policymakers to securely monitor the progress of eHealth projects.

Coronaviruses are enveloped non-segmented positive-sense RNA viruses belonging to the family Coronaviridae. The human coronavirus infections are mild; the epidemics of the two β-coronaviruses, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) have caused more than ten thousand cumulative cases in the past twodecades. There is a new public health crisis threatening the world with the emergence and spread of 2019 novel coronavirus (2019-nCoV). The virus originated in bats and was transmitted to humans through yet unknown intermediary animals in Wuhan, Hubei province in China during the month of December 2019. Till date around 7,823,289 reported cases of coronavirus disease 2019 (COVID-2019) and 431,541 reported deaths till date. The disease is transmitted by inhalation or contact with infected droplets with incubation period of 2–14 days. The symptoms are usually fever, sore throat, dry cough, breathlessness, fatigue while many people are asymptomatic. Coronavirus (2019-nCoV) may progress to pneumonia, acute respiratory distress syndrome (ARDS) and can cause multi-organ dysfunction. Currently diagnosis is done by demonstration of the virus in respiratory secretions by special molecular tests like real-time reverse-transcription–polymerase-chain-reaction (RT-PCR), Radiological examinations (chest CT). Common laboratory tests like white cell counts and C-reactive protein (CRP) and measure symptoms can be used as preliminary screening at large scale after lock down the area or country. Treatment is essentially supportive; role of antiviral agents is yet to be established. It is paramount to implement infection control practices by infection source controlling, transmission route blocking, and susceptible population protection. Early preventive measures can be home isolation of suspected cases and those with mild illnesses and strict infection control measures at hospitals that include contact and droplet precautions. The worldwide impact of this Coronavirus new epidemic is yet uncertain.

Introduction

In hospital care, urinary catheters are frequently used, causing a substantial risk for catheter-associated urinary tract infections (CAUTI). Patient awareness and evaluation of appropriateness of their catheter through mHealth could decrease these healthcare-associated infections. However, patient engagement via mHealth in infection prevention is still limited. Therefore, we describe the systematic development and usability evaluation of the mHealth intervention Participatient, to prevent CAUTI, aiming for optimal adoption of the app in the clinical setting.

Method

The CeHRes roadmap was used as development guideline, operationalizing phases for (1) contextual inquiry (observations and interviews), (2) value specification (interviews with probing) and (3) design in multiple steps and in co-creation with end-users. During phases 1 and 2, semi-structured interviews were conducted with fifteen patients and three nurses. The design phase was combined with the minimum viable product development strategy, with a focus on early cyclic steps of prototyping.

Results

In phase 1, patients acknowledged the risks of catheter use. Patients in phase 2 valued endorsement of a mHealth application by healthcare workers and reported to own a smartphone. Both patients and nurses recognized the need for useful modules in the app besides catheter care. Based on the needs and values as found in phase 2, the Participation app was developed. Based on usability tests in phase 3, content, text size, plain language, and navigation structures were further amended, and images were added.

Conclusion

This study provides real-world insight in the developmental strategy for mHealth interventions by involving both patients and care providers. Development of an app using thorough needs-assessment provided understanding for its content and design. By developing an app providing patients with reliable information and daily checklists, we aim to provide a tailored tool for communication and awareness on catheter use for the whole ward, and a potential blueprint for mHealth development.

Introduction

The burden of chronic diseases has continued to grow globally. Technology has been used successfully in the management of chronic conditions through diagnosis, monitoring and education. This review aims to collate available evidence to evaluate the role of short message services (SMS), as an intervention, in patient participation in disease management of three common chronic diseases, i.e., Human Immunodeficiency Virus (HIV), Diabetes Mellitus (DM) and Chronic Obstructive Pulmonary Disease (COPD).

Materials and method

The review identified eight eligible studies in PubMed, Google Scholar and Cochrane library that assessed the effect of SMS interventions on self-management in HIV, DM and COPD. Outcomes of interest included adherence to the self-management plan, lifestyle modification (e.g., exercise and diet), clinical outcomes (e.g body mass index, blood glucose level, blood pressure), knowledge of disease, feasibility, acceptability, usability, and cost-effectiveness.

Results

Adherence, knowledge about their disease conditions, and feasibility/acceptability were assessed in eight, six and three studies, respectively. Improvements from pre- to post-intervention in adherence and clinical outcomes (including body mass index and blood glucose level) were recorded in eight and three studies. Patients reported having knowledge about their conditions and were willing to continue with the intervention (patient acceptability and usability). However, some patients complained they could not send response messages either due to technical issues or forgetfulness and no analysis of cost-effectiveness.

Conclusion

Chronic conditions require long-term care and patients can be active participants in their care plan to improve their health. SMS interventions have been used successfully to encourage patient self-management, patient awareness, and treatment adherence in HIV, COPD, and DM. The SMS intervention has been applied in various research designs and disease conditions. Patients’ adherence and clinical conditions changed from pre- to post-intervention; however, more research is needed to elucidate the effect of SMS interventions in different countries, socioeconomic status and age groups, and chronic diseases.

This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network (MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for early diagnosis of breast cancer based on their accuracy in order to identify which method is better for the diagnosis of breast cell malignancies. Deep comparison of functioning of each network and its designing is performed and then analysis is done based on the accuracy of diagnosis and classification of breast malignancy by the network to decide which network outperforms the other. CNN is found to give slightly higher accuracy than MLP for diagnosis and detection of breast cancer. There still is the need to carefully analyse and perform a thorough research that uses both these methods on the same data set under same conditions in order identify the architecture that gives better accuracy.

Artificial intelligence (AI) has been applied widely in almost every area of our daily lives, due to the growth of computing power, advances in methods and techniques, and the explosion of data, it also plays a critical role in academic disciplines, medicine is not an exception. AI can augment the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Kidney disease causes great economic burden worldwide, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality. Outstanding challenges in nephrology may be addressed by leveraging big data and AI. In this review, we summarized advances in machine learning (ML), artificial neural network (ANN), convolution neural network (CNN) and deep learning (DL), with a special focus on acute kidney injury (AKI), chronic kidney disease (CKD), end-stage renal disease (ESRD), dialysis, kidney transplantation and nephropathology. AI may not be anticipated to replace the nephrologists’ medical decision-making for now, but instead assisting them in providing optimal personalized therapy for patients.