Evidence map›Paper›PMID 36644659›Full record

ArticleDigital health

Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform.

Hee Young Lee, Kang Hyun Lee, Kyu Hee Lee, Urtnasan Erdenbayar, Sangwon Hwang, Eun Young Lee, Jung Hun Lee, Hee Jin Kim, Sung Bin Park, Joon Wook Park and 3 more

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
7.6field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 1 institution in 1 country.

Hee Young LeeDepartment of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.ORCID https://orcid.org/0000-0003-3254-2261
Kang Hyun LeeDepartment of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Kyu Hee LeeArtificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Urtnasan ErdenbayarArtificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Sangwon HwangArtificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Eun Young LeeArtificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Jung Hun LeeDepartment of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Hee Jin KimDepartment of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Sung Bin ParkDigital Healthcare Team, Corporate Support Division, Wonju Medical Industry Technovalley, Wonju, Republic of Korea.
Joon Wook ParkDigital Healthcare Team, Corporate Support Division, Wonju Medical Industry Technovalley, Wonju, Republic of Korea.
Tae Yun ChungOpen Platform Team, Platform Research Department, Gangwon Research Institute of ICT Convergence, Wonju, Republic of Korea.
Tae Hyoung KimOpen Platform Team, Platform Research Department, Gangwon Research Institute of ICT Convergence, Wonju, Republic of Korea.
Hyun YoukDepartment of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Yonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, core, analytics, and services. Among the implemented MEDBIZ platform, we performed four clinical trials that designed monitoring services related to chronic obstructive pulmonary disease, metabolic syndrome, arrhythmia, and diabetes mellitus. Of the four empirical studies on monitoring services, two had been completed and the rest were still in progress. In the metabolic syndrome monitoring service, two studies were reported. One was reported that intervention components, especially wearable devices and mobile apps, made systolic blood pressure, diastolic blood pressure, waist circumference, and glycosylated hemoglobin decrease after 6 months. Another one was presented that increasing high-density lipoprotein cholesterol and triglyceride levels were prevented in participants with the pre-metabolic syndrome. Also, self-care using healthcare devices might help prevent and manage metabolic syndrome. In the arrhythmia monitoring service, during the real-time monitoring of vital signs remotely at the monitoring center, 318 (15.9%) general hikers found abnormal signals, and 296 (93.1%) people were recommended for treatment. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting real-world evidence. And then through this platform, we were developing software as a medical device, digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

Indexed as

big data platformDigital healthcareempirical studieshealthcare servicesinternet of medical things

Identifiers

PMID36644659
PMCPMC9834931
OpenAlexW4313891884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.