Journal of Telemedicine and Telecare最新文献

Background: For an optimal interplay based on the data-secure exchange of diagnostic data between patients, ambulatory care, and hospital care, we implemented the first tele-medical network for pediatric cardiology in Germany, the Congenital Cardiology Cloud. This study focuses on its feasibility, technical characteristics, and implementation in routine clinical work.

Methods: Tele-medical traffic comprised numbers of incoming/outgoing data, related file types, treatment options for tele-medically processed patients, and patient classification with respect to the severity of disease. Proof of feasibility was related to the number of successful tele-medical transmissions of discharge documents at the end of the observation period (02/2020-10/2020).

Results: Analysis of Congenital Cardiology Cloud's data communication showed a number of 1178 files for a total of 349 patients, favoring transmissions towards the clinic. Incoming traffic was predominantly characterized by diagnostic data regarding tele-consultations (76.6%), consisting of a multitude of file types, whereas 93.4% of the dispatched data corresponded to discharge letters. The number of tele-consultations counted up to 61, with a necessary subsequent treatment or diagnostic procedure in 90.2% of the presented cases. Tele-medically processed patients generally showed to be more complex (severe chronic heart disease 42.4% vs. 23.7%). At the end of the observation period, 97.6% of the discharge documents were transmitted via telemedicine.

Discussion: The implementation of the first tele-medical network for pediatric cardiology in Germany proves recent technological developments to successfully enable innovative patient care, connecting the ambulatory and hospital sector for a joint patient advice, predominantly in more complex cases. Possible governmentally guided refinancing concepts will show its long-term feasibility.

Introduction: The pandemic increased pressure on healthcare services and forced limited care in all health facilities to ensure the care of all patients. Telepharmacy appears as an alternative to the remote pharmacy practice approach through information and communication technologies, but there are no comprehensive tools to measure pharmacists' knowledge, perception, and readiness to implement telepharmacy. This study developed and validated a questionnaire version of the Knowledge, Perceptions, and Readiness of Telepharmacy (KPR-TP) for pharmacists.

Methods: The KPR-TP assessed three domains: knowledge, perception, and readiness. Its factor structure, reliability, and validity were assessed using 7730 pharmacists from 34 Indonesian provinces. The validity of the model's three-factor structure was assessed using confirmatory factor analysis. Cronbach's alpha and composite reliability were used to validate the consistency of our factors, whereas convergent and discriminant validity established significant relationships between them.

Results: The goodness-of-fit index indicated that the model was economical and reasonable. Furthermore, the correlation between the three domains revealed a significant positive relationship. KRP-TP is a viable instrument for assessing pharmacists' perceptions of telepharmacy in Indonesia.

Conclusion: Overall, we discovered that our questionnaire contains critical constructs for assessing a pharmacist's knowledge, perception, and level of readiness to implement telepharmacy. This study will help pharmacists identify appropriate strategies for skill improvement.

Aim: To gain a better understanding of consumer experiences with and preferences for telephone and videoconference consultations (telehealth), and how these compare to traditional in-person consultations.

Methods: A national cross-sectional survey was administered to a representative sample of Australian adults who have received a telehealth service within the last year. Consumers were recruited by Qualtrics® through their online sampling service. The sample was representative of the broader Australian population according to gender, age, location (state/territory), and place of residence (urban or remote). Information on demographics (e.g., age, gender, employment status), recent telehealth experience, and preferences for consultation modality was collected. To measure preferences consumers were asked to indicate which modality they would prefer (in-person, telephone, or videoconference) for different scenarios. These included consultations of various time lengths, and for the top ten conditions for which individuals sought a general practitioner.

Results: A total of 1069 consumers completed the survey. When consumers were asked to describe their most recent telehealth appointment, most were for follow-up appointments (67%) and completed by telephone (77%) rather than by videoconference, and with a general practitioner (75%). In-person consultations at a clinic were the top preference in all clinical scenarios presented, except when needing a prescription or to receive test results. In these cases, a telephone consultation was the preferred modality. Inexperience with videoconference and duration of consultation influenced preference for consultation mode. Consumers preferred to have short consultations of around five minutes done by telehealth (telephone or videoconference), while they preferred in-person for longer consultations (up to 60 minutes).

Conclusions: Many Australians have used telehealth in the past year to access healthcare, with telephone being the most common form of communication. Given the option and the experience to date, consumers prefer telephone when consultations related to either prescriptions or test results. Experience with videoconference for consultations increased consumer preferences for using it for future consultations.

Background: There is limited information about how on-line screening tools developed by integrated systems facilitated management of COVID-like illness patients.

Methods: Using the Kaiser Permanente Northwest (KPNW) Electronic Health Record, we identified adult plan members who accessed online COVID-19 screening e-visits and enumerated their subsequent medical encounters, tests for SARS-CoV-2, and test outcomes.

Results: Between May 2020 and December 2021, members completed 55,139 e-visits, with disproportionate representation among females (65% vs. 53% in the overall membership) and members aged <45 years (61% vs. 39%). Thirty percent of patients (16,953) were managed entirely through e-visits and 70% received subsequent in-person care. The percent of SARS-CoV-2 positive individuals was highest among the 1055 individuals triaged to inpatient care (17.9%), compared to 9.5% among those escalated to additional ambulatory care.

Conclusions: The e-visit on-line screening tool helped KPNW assist thousands of patients with COVID-19 symptoms, avoid unnecessary in-person patient encounters, and preserved KPNW infection control and pandemic surge capacity.

Introduction: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19.

Methods: Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence.

Results: A total of 385 eyes from 195 screening participants were included (mean age 52.43  ±  14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6  ±  13.3 s) faster than the tele-ophthalmology grader (129  ±  41.0) and clinical ophthalmologist (68  ±  21.9, p < 0.001).

Discussion: Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.

Introduction: Videoconferencing psychotherapy (VCP) delivers treatment to individuals with limited access to face-to-face mental healthcare. VCP's effectiveness has been demonstrated for various disorders and therapeutic interventions. However, there is contradictory evidence regarding the therapeutic alliance in VCP as compared to psychotherapy in person (PIP). This meta-analysis examines whether therapeutic alliance differs by psychotherapy's delivery format, namely VCP versus PIP.

Methods: We searched five databases for trials comparing the therapeutic alliance in VCP and PIP, wherein the therapeutic alliance was rated by either patients or therapists or both. Eighteen publications were included, and the difference between VCP and PIP was assessed. Furthermore, we tested possible moderators of the difference in therapeutic alliance between VCP and PIP by meta-regression, and we assessed the risk of bias of this meta-analysis.

Results: The meta-analysis revealed no statistically significant difference in the therapeutic alliance between VCP and PIP for alliance ratings by patients (SMD = -0.09; 95% CI = -0.26; 0.07) as well as by therapists (SMD = 0.04; 95% CI = -0.17; 0.25). No significant moderators were found.

Discussion: In this meta-analysis, VCP and PIP did not differ with respect to the therapeutic alliance as rated by either patients or therapists. Further research is required into mechanisms driving the therapeutic alliance in VCP and PIP.

Introduction: Using a human factors engineering approach, the Johns Hopkins Personalized Pain Program adopted telemedicine for perioperative pain management in response to the COVID-19 pandemic. This study examines the impact of telemedicine adoption on the quality and outcomes of perioperative pain management.

Methods: A mixed-methods study with a convergent parallel design was conducted. From June 2017 to December 2021, 902 patients participated in the Personalized Pain Program. Quantitative data on daily opioid consumption, pain severity and interference, physical and mental health status, and patient satisfaction and engagement were continuously collected with all patients using chart review and patient surveys. Beginning 23 March 2020, the Personalized Pain Program transitioned to telemedicine. A pre-post quasi-experimental design was used to examine the impact of telemedicine. In addition, qualitative interviews were conducted with 3 clinicians and 17 patients to explore their experience with telemedicine visits.

Results: The monthly number of new patients seen in the Personalized Pain Program did not significantly change before and after telemedicine adoption. Compared to patients having in-person visits before the pandemic, patients having telemedicine visits during the pandemic achieved comparable improvements in daily opioid consumption, pain severity and interference, and physical health status. While telemedicine helped overcome many challenges faced by the patients, the limitations of telemedicine were also discussed.

Conclusion: The COVID-19 pandemic stimulated the use of telemedicine. To facilitate telemedicine adoption beyond the pandemic, future research is needed to examine best practices for telemedicine adoption and provide additional evidence on the effectiveness of telemedicine.

Introduction: Teleneurocritical care (TNCC) provides virtual care for hospitals who do not have continuous neurointensivist coverage. It is not known if TNCC is cost effective nor which variables impact the total billed charges per patient encounter. We characterize cost, defined by charge characteristics of TNCC compared to in-person neurocritical care (NCC), for patients with acute ischemic or hemorrhagic stroke requiring ICU care.

Methods: We performed a retrospective review from 2018 to 2021 of prospectively collected multinstitutional databases from a large, integrated, not-for-profit health system with an in-person NCC and spoke TNCC sites. The primary outcome was the total billable charge per TNCC patient with acute ischemic or hemorrhagic stroke compared to in-person NCC. Secondary outcomes were functional outcome, transfer rate, and length of stay (LOS).

Results: A total of 1779 patients met inclusion criteria, 1062 at the hub in-person NCC hospital and 717 at spoke TNCC hospitals. Total billed patient charges of TNCC were similar to in-person NCC (median 104% of the cost per in-person NCC patient, 95% CI: 99%-108%). From 2018 to 2021, the charge difference between TNCC and NCC was not different (r²  =  0.71, p  =  0.16). Both age and length stay were independently predictive of charges: for every year older the charge increased by US $6.3, and every day greater LOS the charge increased by $2084.3 (p < 0.001, both). TNCC transfer rates were low, and TNCC had shorter LOS and greater favorable functional outcome.

Discussion: TNCC was associated with similar patient financial charges as compared to in-person NCC. Standardization of care and the integrated hub-spoke value-focused operational procedures of TNCC may be applicable to other healthcare systems, however, further prospective study is needed.

Introduction: Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions.

Methods: We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties.

Results: Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use.

Discussion: Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations.

Introduction: The rapid evolution and implementation of virtual care technologies for clinical use often exceeds the development of standardized implementation protocols, leading to gaps in the equitable and sustainable adoption of virtual care services, particularly in rural and remote areas. This paper introduces a comprehensive evaluation tool designed to assess community capacity and readiness for virtual care.

Methods: The development of the tool was informed by experiences from the Virtual Care and Robotics Program at the University of Saskatchewan. It involved a collaborative, multi-stakeholder approach that engaged healthcare leaders, IT experts and community healthcare workers. This iterative process included defining evaluation categories, mapping evaluative domains and refining the tool into a user-friendly checklist manual.

Results: The tool identifies three key domains for assessing readiness: clinical needs, technology infrastructure and human resources/workflows. It was piloted in the remote community of Fond du Lac, Saskatchewan, where it successfully qualified the community for implementing telerobotic ultrasound services. The tool facilitated local engagement and highlighted the community's specific needs and readiness, enhancing the implementation process.

Conclusion: This evaluation tool contributes to bridging the gap between the rapid deployment of virtual care technologies and the need for comprehensive, standardized implementation protocols. It offers a structured, practical approach to assessing and enhancing community readiness for virtual care while promoting successful clinical implementation and equitable access to healthcare.