Evidence map›Paper›PMID 21732130›Full record

ArticleJournal of medical systems2012

Two RFID-based solutions for secure inpatient medication administration.

Yi-Chung Yen, Nai-Wei Lo, Tzong-Chen Wu

Abstract read
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In one paragraph

Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. RFID analytics for hospital ward management.Flexible services and manufacturing journal · 2016
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Radio Frequency Identification (RFID) technology and patient safety.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2013
    Review
  12. Review
  13. 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

3 authors.

Yi-Chung YenDepartment of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China.
Nai-Wei Lo
Tzong-Chen Wu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication error can easily cause serious health damage to inpatients in hospital. Consequently, the whole society has to spend huge amount of extra resources for additional therapies and medication on those affected inpatients. In order to prevent medication errors, secure inpatient medication administration system is required in a hospital. Using RFID technology, such administration system provides automated medication verification for inpatient's medicine doses and generates corresponding medication evidence, which may be audited later for medical dispute. Recently, Peris-Lopez et al. (Int. J. Med. Inform., 2011) proposed an IS-RFID system to enhance inpatient medication safety. Nevertheless, IS-RFID system does not detect the denial of proof attack efficiently and the generated medication evidence cannot defend against counterfeit evidence generated from the hospital. That is, the hospital possesses enough privilege from the design of IS-RFID system to modify generated medication evidence whenever it is necessary. Hence, we design two lightweight RFID-based solutions for secure inpatient medication administration, one for online verification environment and the other for offline validation situation, to achieve system security on evidence generation and provide early detection on denial of proof attack.

Indexed as

Computer SecurityInpatientsAlgorithmsConfidentialityEquipment DesignHumansMedication ErrorsMedication Systems, HospitalPatient SafetyRadio Frequency Identification Device

Identifiers

What OpenQuestion holds

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Registered trials

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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.