Evidence map›Paper›PMID 30041651›Full record

ArticleBMC health services research2018

Development of an enterprise risk inventory for healthcare.

Ana Paula Beck da Silva Etges, Veronique Grenon, Ming Lu, Ricardo Bertoglio Cardoso, Joana Siqueira de Souza, Francisco José Kliemann Neto, Elaine Aparecida Felix

Abstract read
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Impact of industry 4.0 to create advancements in orthopaedics.Journal of clinical orthopaedics and trauma · 2020
    Article
  6. Medication Risk Management.Innovations in pharmacy · 2019
    Article
  7. 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

7 authors.

Ana Paula Beck da Silva EtgesSchool of Technology, PUCRS, Avenida Ipiranga, 6681, Porto Alegre, 90619-900, Brazil. anabsetges@gmail.com.
Veronique GrenonThe Risk Authority Stanford, Palo Alto, California, USA.
Ming LuThe Risk Authority Stanford, Palo Alto, California, USA.
Ricardo Bertoglio CardosoDepartment of Industrial Engineering, UFRGS, Porto Alegre, RS, Brazil.
Joana Siqueira de SouzaDepartment of Industrial Engineering, UFRGS, Porto Alegre, RS, Brazil.
Francisco José Kliemann NetoDepartment of Industrial Engineering, UFRGS, Porto Alegre, RS, Brazil.
Elaine Aparecida FelixDepartment of Anesthesiology, School of Medicine, UFRGS, Porto Alegre, RS, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe first phase of an enterprise risk management (ERM) program is the identification of risks. Accurate identification is essential to a proactive and effective ERM function. The authors identified a lack of such risk identification in the literature and in practical cases when interviewing the chief risk officers from healthcare organizations. A risk inventory specific to healthcare organizations that includes detailed risk scenarios and risk impacts currently does not exist. Thus, the objective of this research is to develop an enterprise risk inventory for healthcare organizations to create a common understanding of how each type of risk impacts a healthcare organization.

methodERM guidelines and data from 15 interviews with chief risk officers were analyzed to create the risk inventory. The identified risks were confirmed through a survey of risk managers from a range of global healthcare organizations during the ASHRM conference in 2017. Descriptive statistics were developed and cluster analysis was performed using the survey results.

resultsThe risk inventory includes 28 risks and their specific risk scenarios. Cyberattack was ranked as the principal risk by the participants, followed by sentinel events and risks associated with human capital management (organizational culture, use of electronic medical records and physician wellness). The data analysis showed that the specific characteristics of the survey participants, such as the length of time working in risk management, the size of the organization, and the presence of a school of medicine, do not impact an individual's opinion of the importance of the risks identified. A personal background in risk management (clinical or enterprise) was a characteristic that showed a small difference in the perceived importance of the risks from the proposed risk inventory.

conclusionsIn addition to defining specific risk scenarios, the enterprise risk inventory presented in this research can contribute to guiding the risk identification phase of an ERM program and thereby support the development of a risk culture. Patient data security in hospitals that operate with high levels of technology is fundamental to delivering high quality and safe care to patients. At the top of the risk ranking, the identification of cyberattacks reflects the importance that healthcare risk managers place on this risk by allocating time and other resources. Exploring opportunities to improve cyber risk management and evaluating the benefits of using the risk inventory at the beginning of the risk identification phase in an ERM program are suggestions for future studies.

Indexed as

Attitude of Health PersonnelBiomedical TechnologyComputer SecurityDelivery of Health CareElectronic Health RecordsGroup PracticeHealth ResourcesHospitalsHumansOrganizational CultureOrganizationsPersonnel ManagementPhysiciansRisk ManagementEnterprise risk managementHealthcareHealthcare managementRisk analysisRisk identificationRisk inventory

Identifiers

PMID30041651
PMCPMC6057062

What OpenQuestion holds

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LicenceCC BY
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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.