Identification of inpatient falls using automated review of text-based medical records
Journal of Patient Safety, ISSN: 1549-8425, Vol: 16, Issue: 3, Page: E174-E178
2020
- 8Citations
- 41Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations8
- Citation Indexes8
- CrossRef6
- Captures41
- Readers41
- 41
Review Description
Objectives Although falls are among the most common adverse event in hospitals, they are difficult to measure and often unreported. Mechanisms to track falls include incident reporting and medical records review. Because of limitations of each method, researchers suggest multimodal approaches. Although incident reporting is commonly used, medical records review is limited by the need to read a high volume of clinical notes. Natural language processing (NLP) is 1 potential mechanism to automate this process. Method We compared automated NLP to manual chart review and incident reporting as a method to detect falls among inpatients. First, we developed an NLP algorithm to identify inpatient progress notes describing falls. Second, we compared the NLP algorithm to manual records review in identifying inpatient progress notes that describe falls. Third, we compared the NLP algorithm to the incident reporting system in identifying falls. Results When examining individual inpatient notes, our NLP algorithm was highly specific (0.97) but had low sensitivity (0.44) when compared with our manual records review. However, when considering groups of inpatient notes, all describing the same fall, our NLP algorithm had a large improvement in sensitivity (0.80) with some loss of specificity (0.65) compared with incident reporting. Conclusions National language processing represents a promising method to automate review of inpatient medical records to identify falls.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84975497084&origin=inward; http://dx.doi.org/10.1097/pts.0000000000000275; http://www.ncbi.nlm.nih.gov/pubmed/27331601; https://journals.lww.com/10.1097/PTS.0000000000000275; http://Insights.ovid.com/crossref?an=01209203-900000000-99594; https://dx.doi.org/10.1097/pts.0000000000000275; https://journals.lww.com/journalpatientsafety/Abstract/2020/09000/Identification_of_Inpatient_Falls_Using_Automated.23.aspx
Ovid Technologies (Wolters Kluwer Health)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know