The health economic effects of an imaging technology–based telemedicine system for rural neuro-emergency patient care

Hirotaka Sato MD1,2, Manabu Kinoshita MD, PhD1, Yuji Tani MBA, PhD3, Teruo Kimura MD, PhD2, Toshiya Osanai MD, PhD4, Hiroaki Osanai MD, PhD5, and Katsuhiko Ogasawara MBA, PhD6
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  • 1 Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan;
  • | 2 Department of Neurosurgery, Japanese Red Cross Kitami Hospital, Kitami, Japan;
  • | 3 Department of Medical Informatics and Hospital Management, Asahikawa Medical University Hospital, Asahikawa, Japan;
  • | 4 Department of Neurosurgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan;
  • | 5 Furano Kyokai Hospital, Furano, Japan; and
  • | 6 Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
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OBJECTIVE

“Join,” an imaging technology–based telemedicine system, allows simultaneous radiological information sharing between physically remote institutions, virtually connecting advanced medical institutions and rural hospitals. This study aimed to elucidate the health economics effect of Join for neurological telemedicine in rural areas in Hokkaido, Japan.

METHODS

Information concerning 189 requests for patient transfer from Furano Kyokai Hospital, a regional rural hospital, to Asahikawa Medical University Hospital (AMUH), an advanced academic medical institution, was retrospectively collected. The Join system was established between Furano Kyokai Hospital and AMUH in February 2019. Data collected from patients between April 2017 and December 2018 were included in the non-Join group, and those collected between February 2019 and October 2020 were included in the Join group. Clinical variables, reasons for patient transfer requests, duration of hospital stay, and medical costs per patient were analyzed between these two groups. Furthermore, clinical characteristics were compared between patients who were transferred and not transferred based on Join.

RESULTS

More patients were discharged < 7 days after transfer to AMUH in the non-Join group compared with the Join group (p = 0.02). When focusing on the Join group, more patients who were not transferred were discharged < 1 week (p < 0.01). On the other hand, more patients required surgery (p = 0.01) when transferred. The ratio of patients whose medical cost was < USD5000 substantially decreased, from 33% for the non-Join group to 13% for the Join group.

CONCLUSIONS

An imaging technology–based telemedicine system, Join, contributed to reducing unnecessary neuro-emergency patient transfer in a remote rural area, and telemedicine with an integrated smartphone system allowed medical personnel to effectively triage at a distance neuro-emergency patients requiring advanced tertiary care.

ABBREVIATIONS

AMUH = Asahikawa Medical University Hospital; DICOM = Digital Imaging and Communications in Medicine; FKH = Furano Kyokai Hospital; IQR = interquartile range; mRS = modified Rankin Scale; PACS = Picture Archiving and Communication System; SAH = subarachnoid hemorrhage; TIA = transient ischemic attack.

OBJECTIVE

“Join,” an imaging technology–based telemedicine system, allows simultaneous radiological information sharing between physically remote institutions, virtually connecting advanced medical institutions and rural hospitals. This study aimed to elucidate the health economics effect of Join for neurological telemedicine in rural areas in Hokkaido, Japan.

METHODS

Information concerning 189 requests for patient transfer from Furano Kyokai Hospital, a regional rural hospital, to Asahikawa Medical University Hospital (AMUH), an advanced academic medical institution, was retrospectively collected. The Join system was established between Furano Kyokai Hospital and AMUH in February 2019. Data collected from patients between April 2017 and December 2018 were included in the non-Join group, and those collected between February 2019 and October 2020 were included in the Join group. Clinical variables, reasons for patient transfer requests, duration of hospital stay, and medical costs per patient were analyzed between these two groups. Furthermore, clinical characteristics were compared between patients who were transferred and not transferred based on Join.

RESULTS

More patients were discharged < 7 days after transfer to AMUH in the non-Join group compared with the Join group (p = 0.02). When focusing on the Join group, more patients who were not transferred were discharged < 1 week (p < 0.01). On the other hand, more patients required surgery (p = 0.01) when transferred. The ratio of patients whose medical cost was < USD5000 substantially decreased, from 33% for the non-Join group to 13% for the Join group.

CONCLUSIONS

An imaging technology–based telemedicine system, Join, contributed to reducing unnecessary neuro-emergency patient transfer in a remote rural area, and telemedicine with an integrated smartphone system allowed medical personnel to effectively triage at a distance neuro-emergency patients requiring advanced tertiary care.

Hokkaido is a Japanese prefecture that occupies 22% of the nation’s territory, a size equivalent to the territorial size of Ireland. On the other hand, only 4.2% of the Japanese population live in Hokkaido, making it difficult to supply enough specialized medical personnel for its rural areas. This difficulty is particularly pronounced in neurology and neurosurgery because these disciplines require highly specialized personnel and equipment for accurate diagnosis and treatment. As a result, rural hospitals tend to request the transfer of patients with neurological conditions to distant advanced medical institutions. However, patients with only minor medical problems are discharged the day after transfer, thus posing unnecessary burdens on medical personnel and the infrastructure of advanced medical institutions.

Our institution, one of the advanced academic hospitals in Hokkaido covering 800,000 inhabitants, started to adopt an imaging technology–based telemedicine system to address the issue mentioned above. The system consists of a smartphone application called “Join” (Allm, Inc.; FDA Listing Number: D245938, Medical device approval number in Japan: 227AOBZX00007Z00, https://www.allm.net/wp-content/uploads/Join_Brochure_EN.pdf) that is capable of directly transferring Digital Imaging and Communications in Medicine (DICOM) images from a local Picture Archiving and Communication System (PACS) server to the target smartphone via high-speed cellular networks such as LTE or 4G (Fig. 1).1 This technology allows simultaneous radiological information sharing between physically remote institutions, virtually connecting advanced medical institutions and rural hospitals. Unlike freely available instant messaging systems on smartphones, Join allows the user to select and view all the shared DICOM images as if viewing them on a PACS client under a secure environment without leaving any trace of the patient’s personal information in the target smartphone. Specialized medical personnel can instantly determine whether it is necessary to transfer the patient in question based on the shared radiological information, leading to proper triaging of patients to transfer. Previous studies focused mainly on using Join in neurological emergencies such as stroke care.2,3 However, the use of Join has not been studied from a medical economy point of view as a neurological telemedicine tool connecting advanced medical institutions and rural hospitals. This study aimed to elucidate the health economics effect of Join for neurological telemedicine in rural areas in Japan.

FIG. 1.
FIG. 1.

An imaging technology–based telemedicine system, Join, is shown. This application is approved by government authorities worldwide, including Japan, the United States, the European Union, Brazil, and Saudi Arabia. Image provided with permission by Allm, Inc., Tokyo, Japan.

Methods

Cohort Design

The local institutional review board approved the use of clinical data for this research. Information concerning 189 requests for patient transfer from Furano Kyokai Hospital (FKH) to Asahikawa Medical University Hospital (AMUH) between April 2017 and December 2018 and between February 2019 and October 2020 was retrospectively collected. Data collected during January 2019 were excluded, because Join was in the beta phase during this period. FKH is in Furano, a rural region in Hokkaido of 601 km2 encompassing a population of 22,000, with no full-time, board-certified neurosurgeon working in the region. On the other hand, AMUH is an advanced academic medical institution situated in the center of Hokkaido, providing 24/7 neurological and neurosurgical service. The two hospitals are located 50 km apart (Fig. 2A). Patient consultations between the two hospitals were done by telephone calls before introducing Join. The Join system was established between FKH and AMUH in February 2019. Patient consultation workflow after introducing Join involved the following: 1) patient consultation request by telephone calls exchanging information on basic clinical background of the patient; 2) key DICOM image exchange via Join; and 3) determination whether to keep the patient at FKH or to initiate patient transfer procedure to AMUH. Data collected from patients between April 2017 and December 2018 were included in the non-Join group, and data collected between February 2019 and October 2020 were included in the Join group (Fig. 2B). Thus, information on each group of patients was collected over 21 months. No system failure involving Join occurred during this period. Days of hospital stay were determined as the time from transfer to discharge at AMUH.

FIG. 2.
FIG. 2.

A: The geographical relationship and medical system of FKH and AMUH are shown. B: Patient transfer requests from FKH to AMUH were made by telephone between April 2017 and December 2018, resulting in the acceptance of all transfer requests. On the other hand, between February 2019 and October 2020, a doctor-to-doctor discussion was performed using the imaging technology–based telemedicine system Join and telephone, aiding decision-making for patient transfer.

Clinical Variables

The clinical characteristics of the entire cohort are shown in Table 1. Patients were classified as suffering from a cerebral ischemic disease such as cerebral infarction and transient ischemic attack (TIA) or a cerebral hemorrhagic disease such as cerebral hemorrhage and subarachnoid hemorrhage (SAH) or trauma, and/or other conditions. Other conditions included brain tumors, convulsions, spinal stenosis, dizziness, and no neurological findings. Treatment outcome was measured by the modified Rankin Scale (mRS) score at discharge. Patient deaths and surgical cases were counted for those who died or underwent surgery during hospitalization at AMUH. Medical costs were categorized into 5 groups for every USD5000 (USD1 = 100 yen). One patient was not included when calculating medical costs because this individual was repeatedly hospitalized in AMHU within 24 hours, which hampered an accurate medical cost calculation.

TABLE 1.

Characteristics of 189 patients whose transfer was requested

CharacteristicNon-JoinJoinp Value
Mean age in yrs (IQR)73 (63–95)76 (70–95)0.16
Sex (M/F)59:4041:490.06
Cause of hospitalization
 Surgery cases237<0.01*
 Infarction or TIA31350.29
 Cerebral hemorrhage26190.49
 SAH730.34
 Trauma22120.13
 Other11200.05*

Variables showing significant differences.

Statistical Analysis

Variables in Table 1 are expressed as the mean ± interquartile range (IQR) 25th–75th percentile or number and percentage of patients. Statistical analysis was performed using Pearson’s chi-square test or Fisher’s exact test to evaluate correlations between categorical variables. The Kolmogorov-Smirnov test was used to assess the data distribution of continuous variables. The Student t-test was used to compare normally distributed continuous variables, and the Mann-Whitney U-test was used for nonnormally distributed variables. All statistical analyses were performed using EZR software.4

Results

Overall Cohort Characteristics

Table 1 and Fig. 2B summarize the analyzed cohort’s patients’ characteristics. There were 59 men and 40 women, with a mean age of 73 (range 63–95) years for the non-Join group and 41 men and 49 women, mean age 76 (range 70–95) years, for the Join group. There were no statistically significant differences between the two groups regarding age and sex, but there were more surgical cases for the non-Join group (p < 0.01). FKH transferred 23 patients during the Join period and 11 patients during the non-Join period to other hospitals for neuro-emergency care. A total of 44 cases were not transported to AMUH during the Join period (Table 2 and Fig. 2B).

TABLE 2.

Cases not transported from FKH to AMUH

Transport DataNo. of Pts
Cases not transported based on Join telemedicine app44
Cases transported to other hospitals34
Join period23
Before Join period (non-Join)11

Pts = patients.

Comparison of Transferred Patients During the Non-Join and Join Periods

Table 3 compares the clinical characteristics of the patients transferred to AMUH between the non-Join and Join periods. There were no differences in age, sex, mRS score 0–2, death after the transfer, surgery cases, and transfer causes. However, numbers of patients with hospital stays < 1 week were significantly different between the two groups. More patients were discharged < 7 days after transfer to AMUH in the non-Join group (p = 0.02) (Table 3 and Fig. 3A).

TABLE 3.

Comparison of transferred patients by non-Join and Join group

Non-Join, n = 99Join, n = 46p Value
Mean age in yrs (IQR)77 (63–85)78 (70–84)0.51
Female sex40 (40%)23 (50%)0.37
mRS score 0–253 (54%)28 (61%)0.52
Death after transfer6 (6%)2 (4%)0.98
Hospital stay <1 wk25 (25%)3 (7%)0.02*
Kind of disease
 Surgery cases23 (23%)7 (15%)0.37
 Infarction or TIA31 (31%)20 (43%)0.22
 Cerebral hemorrhage26 (26%)12 (26%)1.00
 SAH7 (7%)3 (7%)1.00
 Trauma22 (22%)8 (17%)0.65
 Other11 (11%)3 (7%)0.57

Unless otherwise indicated, values are expressed as the number of patients (%).

Variables showing significant differences.

FIG. 3.
FIG. 3.

The ratio of patients with a hospital stay of < 1 week is shown. A: More patients were discharged < 7 days after transfer to AMUH in the non-Join group (non-Join vs Join: 25% vs 7%, p = 0.02). B: More patients were also discharged < 1 week in the nontransferred group (transferred vs nontransferred: 7% vs 50%, p < 0.01).

Comparison of Transferred and Nontransferred Cases in the Join Group

Table 4 compares the clinical characteristics between transferred and nontransferred cases in the Join group. There were no differences in age, sex, and causes of transfer request such as infarction and TIA, cerebral hemorrhage, SAH, and trauma. There were significantly more deaths in the nontransferred group (p = 0.01). Significantly more patients were discharged < 1 week in the nontransferred group (p < 0.01). On the other hand, more patients required surgery (p = 0.01) in the transferred group. There were more patients requiring hospitalization due to other causes in the nontransferred group (p < 0.01).

TABLE 4.

Comparison of transferred and nontransferred patients during the Join period

Transferred, n = 46Nontransferred, n = 44p Value
Mean age in yrs (IQR)78 (70–84)81 (72–86)0.49
Female sex23 (50%)26 (59%)0.51
Death after transfer2 (4%)11 (25%)0.01*
Hospital stay <1 wk3 (7%)22 (50%)<0.01*
Surgery cases7 (15%)0 (0%)0.01*
Cause of hospitalization
 Infarction or TIA20 (43%)15 (34%)0.49
 Cerebral hemorrhage12 (26%)7 (16%)0.36
 SAH3 (7%)0 (0%)0.24
 Trauma8 (17%)4 (9%)0.40
 Other3 (7%)17 (39%)<0.01*

Unless otherwise indicated, values are expressed as the number of patients (%).

Variables showing significant differences.

The Medical Cost Difference of Transferred Cases Between the Non-Join and Join Periods

Figure 4 compares medical costs of patients transferred from FKH to AMUH between the non-Join and Join periods, each categorized by increments of USD5000. The ratio of patients whose medical cost was < USD5000 substantially decreased from 33% for the non-Join group to 13% for the Join group. On the other hand, cases that cost between USD5000 and < USD10,000 compensated for the loss, and the portion of cases that cost ≥ USD10,000 stayed approximately the same after introducing Join.

FIG. 4.
FIG. 4.

The ratio of patients with various medical costs per patient is shown. The number of patients who paid < USD5000 substantially decreased during the Join period compared with the non-Join period. K = USD1000.

Discussion

Recent developments in digital communication technology have been remarkable, and telemedicine enables cost savings in neurological emergencies.5 These technologies allow doctors to evaluate detailed medical images on smartphones and remotely make an instant and accurate diagnosis. However, there are not many reports on the use of technologies such as smartphone applications in neuro-emergency care.6 The smartphone application Join is one of these technologies, mainly in acute stroke care.3 The primary purpose of its use is to shorten the time from the first contact with medical personnel to treatment.

On the other hand, this technology could be used differently as telemedicine technology in rural areas. Rural hospitals cannot necessarily afford to employ specialists; thus, even mild cases are transferred to a large hospital. Such an inefficient medical system will exhaust medical personnel in both rural and large hospitals. Furthermore, one can argue that the current system is also inefficient from an economic point of view. Our institution started to use Join to solve this problem, and the present study attempted to prove the usefulness of Join for emergency triage and telemedicine in rural areas of Hokkaido. The decrease in the number of patients with short hospital stays and with medical costs of < USD5000 indicates that Join reduced transfer of mild cases from a rural hospital to a large medical center (Figs. 3A and 4). The efficiency of patient triage using Join was further supported by the fact that more patients were discharged < 1 week when a decision of nontransfer was made based on Join (Fig. 3B). The fact that there were significantly more surgical cases during the non-Join period also implies that the treatment strategy was more aggressive than during the Join period (Table 1). This phenomenon is probably due to the fact that all patients requested for transfer were transferred and admitted to the advanced academic medical institution, resulting in more surgeries.

The analysis in Table 4 also shows that there were more deaths in the nontransfer group. This phenomenon implies that untreatable cases were left within the local rural hospital, avoiding unnecessary transfer of patients along with their families. We consider this trend crucial from a social perspective. Whereas patients and their families expect to receive meaningful advanced treatments at distant academic institutions, they also hope to stay within their local region when suffering from mild or untreatable conditions. In the present study, the number of cases requiring transfer as far as 50 km one way was reduced by Join.

From a hospital administrative perspective, transferring patients with mild or untreatable conditions is inefficient and unprofitable; thus, proper triage is necessary. Human resources such as doctors and nurses will be forced to engage in patient care that does not necessarily require skills or infrastructure that only academic medical centers can provide. We were able to show that telemedicine systems such as Join can resolve this issue, particularly in fields that require advanced skills such as neurology and neurosurgery.

We believe that cost reduction by telemedicine has been the focus of attention in recent years. Telemedicine support in neuro-emergency cases has already been shown to reduce per-patient costs.5 Furthermore, the usefulness of remote diagnostic imaging has also been demonstrated,7 and many cost-reduction effects have been shown even in chronic medical care.8–10 In addition to previous studies, the current research suggests that telemedicine can appropriately triage patient transfer between hospitals, enhance efficiency, and reduce the unnecessary workload of advanced medical institutions.

Several careful considerations should also be mentioned. Although we did not encounter any system failure during the period analyzed, a backup operation plan should be prepared to deal with such a situation, and a workflow via traditional patient consultation methods should be established between rural hospitals and academic medical centers. Furthermore, the proposed telemedicine system has a potential drawback, as can be seen by the higher number of patients transferred from FKH to hospitals other than AMUH during the Join period than during the non-Join period (11 during non-Join vs 23 during Join period). This implies that the local doctors at FKH may have been reluctant to treat neuro-emergency cases in their hospitals. A larger-scale analysis will be required to determine the negative impact of using telemedicine systems between rural hospitals and academic medical centers.

Conclusions

An imaging technology–based telemedicine system, Join, substantially reduced the ratio of patients whose medical cost was < USD5000 and contributed to reducing unnecessary neuro-emergency patient transfer in a remote rural area. We were able to demonstrate that patients with neuro-emergency status not requiring transfer were kept close to home for mild care—or in cases of death, close to family for closure—by using telemedicine with an integrated smartphone system. On the other hand, patients at a distance who required advanced tertiary care were effectively triaged and received a higher level of care.

Acknowledgments

This research was funded by the Hokkaido Development Association (Dr. Sato); Japan Society for the Promotion of Science (grant no. 19K09526, Dr. Kinoshita); the Japan Agency for Medical Research and Development (Japan Cancer Research Project grant nos. 19188187 and 21459042, Dr. Kinoshita); the Takeda Science Foundation (Dr. Kinoshita); the MSD Life Science Foundation (Dr. Kinoshita); and the Okawa Foundation for Information and Telecommunications (Dr. Kinoshita).

Disclosures

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author Contributions

Conception and design: Sato, T Osanai, Ogasawara. Acquisition of data: Sato, Tani, H Osanai, Ogasawara. Analysis and interpretation of data: Kinoshita, Sato, Tani. Drafting the article: Kinoshita, Sato, Ogasawara. Critically revising the article: Kinoshita, Tani, Kimura, T Osanai, Ogasawara. Reviewed submitted version of manuscript: Kinoshita, Sato, Kimura, T Osanai, H Osanai, Ogasawara. Approved the final version of the manuscript on behalf of all authors: Kinoshita. Statistical analysis: Sato. Administrative/technical/material support: Kinoshita, Ogasawara. Study supervision: Kinoshita.

References

  • 1

    Takao H, Sakai K, Mitsumura H, et al. A smartphone application as a telemedicine tool for stroke care management. Neurol Med Chir (Tokyo). 2021;61(4):260267.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2

    Sakai K, Komatsu T, Iguchi Y, Takao H, Ishibashi T, Murayama Y. Reliability of smartphone for Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores in acute ischemic stroke patients: diagnostic test accuracy study. J Med Internet Res. 2020;22(6):e15893.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Martins SCO, Weiss G, Almeida AG, et al. Validation of a smartphone application in the evaluation and treatment of acute stroke in a comprehensive stroke center. Stroke. 2020;51(1):240246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48(3):452458.

  • 5

    Whetten J, van der Goes DN, Tran H, Moffett M, Semper C, Yonas H. Cost-effectiveness of Access to Critical Cerebral Emergency Support Services (ACCESS): a neuro-emergent telemedicine consultation program. J Med Econ. 2018;21(4):398405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Munich SA, Tan LA, Nogueira DM, et al. Mobile real-time tracking of acute stroke patients and instant, secure inter-team communication—the Join app. Neurointervention. 2017;12(2):6976.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7

    Tanikawa T, Suzuki R, Suzuki T, et al. Where does telemedicine achieve a cost reduction effect? Cost minimization analysis of teleradiology services in Japan. Telemed J E Health. 2019;25(12):11741182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8

    Aoki N, Ohta S, Yamamoto H, Kikuchi N, Dunn K. Triangulation analysis of tele-palliative care implementation in a rural community area in Japan. Telemed J E Health. 2006;12(6):655662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Michaud TL, Zhou J, McCarthy MA, Siahpush M, Su D. Costs of home-based telemedicine programs: a systematic review. Int J Technol Assess Health Care. 2018;34(4):410418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Hwang R, Morris NR, Mandrusiak A, et al. Cost-utility analysis of home-based telerehabilitation compared with centre-based rehabilitation in patients with heart failure. Heart Lung Circ. 2019;28(12):17951803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    An imaging technology–based telemedicine system, Join, is shown. This application is approved by government authorities worldwide, including Japan, the United States, the European Union, Brazil, and Saudi Arabia. Image provided with permission by Allm, Inc., Tokyo, Japan.

  • View in gallery

    A: The geographical relationship and medical system of FKH and AMUH are shown. B: Patient transfer requests from FKH to AMUH were made by telephone between April 2017 and December 2018, resulting in the acceptance of all transfer requests. On the other hand, between February 2019 and October 2020, a doctor-to-doctor discussion was performed using the imaging technology–based telemedicine system Join and telephone, aiding decision-making for patient transfer.

  • View in gallery

    The ratio of patients with a hospital stay of < 1 week is shown. A: More patients were discharged < 7 days after transfer to AMUH in the non-Join group (non-Join vs Join: 25% vs 7%, p = 0.02). B: More patients were also discharged < 1 week in the nontransferred group (transferred vs nontransferred: 7% vs 50%, p < 0.01).

  • View in gallery

    The ratio of patients with various medical costs per patient is shown. The number of patients who paid < USD5000 substantially decreased during the Join period compared with the non-Join period. K = USD1000.

  • 1

    Takao H, Sakai K, Mitsumura H, et al. A smartphone application as a telemedicine tool for stroke care management. Neurol Med Chir (Tokyo). 2021;61(4):260267.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2

    Sakai K, Komatsu T, Iguchi Y, Takao H, Ishibashi T, Murayama Y. Reliability of smartphone for Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores in acute ischemic stroke patients: diagnostic test accuracy study. J Med Internet Res. 2020;22(6):e15893.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Martins SCO, Weiss G, Almeida AG, et al. Validation of a smartphone application in the evaluation and treatment of acute stroke in a comprehensive stroke center. Stroke. 2020;51(1):240246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48(3):452458.

  • 5

    Whetten J, van der Goes DN, Tran H, Moffett M, Semper C, Yonas H. Cost-effectiveness of Access to Critical Cerebral Emergency Support Services (ACCESS): a neuro-emergent telemedicine consultation program. J Med Econ. 2018;21(4):398405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Munich SA, Tan LA, Nogueira DM, et al. Mobile real-time tracking of acute stroke patients and instant, secure inter-team communication—the Join app. Neurointervention. 2017;12(2):6976.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7

    Tanikawa T, Suzuki R, Suzuki T, et al. Where does telemedicine achieve a cost reduction effect? Cost minimization analysis of teleradiology services in Japan. Telemed J E Health. 2019;25(12):11741182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8

    Aoki N, Ohta S, Yamamoto H, Kikuchi N, Dunn K. Triangulation analysis of tele-palliative care implementation in a rural community area in Japan. Telemed J E Health. 2006;12(6):655662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Michaud TL, Zhou J, McCarthy MA, Siahpush M, Su D. Costs of home-based telemedicine programs: a systematic review. Int J Technol Assess Health Care. 2018;34(4):410418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Hwang R, Morris NR, Mandrusiak A, et al. Cost-utility analysis of home-based telerehabilitation compared with centre-based rehabilitation in patients with heart failure. Heart Lung Circ. 2019;28(12):17951803.

    • Crossref
    • Search Google Scholar
    • Export Citation

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