Can we understand population healthcare needs using electronic medical records?

dc.contributor.author

Chong, Jia Loon

dc.contributor.author

Low, Lian Leng

dc.contributor.author

Chan, Darren Yak Leong

dc.contributor.author

Shen, Yuzeng

dc.contributor.author

Thin, Thiri Naing

dc.contributor.author

Ong, Marcus Eng Hock

dc.contributor.author

Matchar, David Bruce

dc.date.accessioned

2021-05-05T06:24:11Z

dc.date.available

2021-05-05T06:24:11Z

dc.date.issued

2019-09

dc.date.updated

2021-05-05T06:23:59Z

dc.description.abstract

Introduction

The identification of population-level healthcare needs using hospital electronic medical records (EMRs) is a promising approach for the evaluation and development of tailored healthcare services. Population segmentation based on healthcare needs may be possible using information on health and social service needs from EMRs. However, it is currently unknown if EMRs from restructured hospitals in Singapore provide information of sufficient quality for this purpose. We compared the inter-rater reliability between a population segment that was assigned prospectively and one that was assigned retrospectively based on EMR review.

Methods

200 non-critical patients aged ≥ 55 years were prospectively evaluated by clinicians for their healthcare needs in the emergency department at Singapore General Hospital, Singapore. Trained clinician raters with no prior knowledge of these patients subsequently accessed the EMR up to the prospective rating date. A similar healthcare needs evaluation was conducted using the EMR. The inter-rater reliability between the two rating sets was evaluated using Cohen's Kappa and the incidence of missing information was tabulated.

Results

The inter-rater reliability for the medical 'global impression' rating was 0.37 for doctors and 0.35 for nurses. The inter-rater reliability for the same variable, retrospectively rated by two doctors, was 0.75. Variables with a higher incidence of missing EMR information such as 'social support in case of need' and 'patient activation' had poorer inter-rater reliability.

Conclusion

Pre-existing EMR systems may not capture sufficient information for reliable determination of healthcare needs. Thus, we should consider integrating policy-relevant healthcare need variables into EMRs.
dc.identifier

j60/9/446

dc.identifier.issn

0037-5675

dc.identifier.uri

https://hdl.handle.net/10161/22780

dc.language

eng

dc.publisher

Singapore Medical Journal

dc.relation.ispartof

Singapore medical journal

dc.relation.isversionof

10.11622/smedj.2019012

dc.subject

Humans

dc.subject

Incidence

dc.subject

Retrospective Studies

dc.subject

Prospective Studies

dc.subject

Reproducibility of Results

dc.subject

Emergency Medicine

dc.subject

Algorithms

dc.subject

Needs Assessment

dc.subject

Nurses

dc.subject

Physicians

dc.subject

Emergency Service, Hospital

dc.subject

Hospitals

dc.subject

Health Services Needs and Demand

dc.subject

Patient-Centered Care

dc.subject

Singapore

dc.subject

Electronic Health Records

dc.title

Can we understand population healthcare needs using electronic medical records?

dc.type

Journal article

duke.contributor.orcid

Matchar, David Bruce|0000-0003-3020-2108

pubs.begin-page

446

pubs.end-page

453

pubs.issue

9

pubs.organisational-group

School of Medicine

pubs.organisational-group

Duke Clinical Research Institute

pubs.organisational-group

Duke Global Health Institute

pubs.organisational-group

Pathology

pubs.organisational-group

Medicine, General Internal Medicine

pubs.organisational-group

Duke

pubs.organisational-group

Institutes and Centers

pubs.organisational-group

University Institutes and Centers

pubs.organisational-group

Institutes and Provost's Academic Units

pubs.organisational-group

Clinical Science Departments

pubs.organisational-group

Medicine

pubs.publication-status

Published

pubs.volume

60

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Can we understand population healthcare needs using electronic medical records.pdf
Size:
3.62 MB
Format:
Adobe Portable Document Format