Detection of overdose and underdose prescriptions—An unsupervised machine learning approach

Nagata, K.; Tsuji, Toshikazu; Suetsugu, Kimitaka; Muraoka, Kayoko; Watanabe, Hiroyuki; Kanaya, Akiko; Egashira, Nobuaki; Ieiri, Ichiro

doi:10.1371/journal.pone.0260315

Cited by 18 publications

(20 citation statements)

References 32 publications

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“…"Overdosage" was then the most frequently class of DRPs associated to recommendations observed in this study. Similar results have been shown in previous research [9,[13][14][15][16][17][18][19][20]. Bedouch et al [9] for example reported 19.2% of supratherapeutic doses.…”

Section: Discussionsupporting

confidence: 90%

Use of a deep learning application to classify recommendations made by hospital pharmacists during medication prescription review

Alkanj,

Godet,

Johns

et al. 2023

Preprint

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Background Recommendations are proposals made by hospital pharmacists to address the sub-optimal use of medications during prescription review. Objective To perform a large-scale descriptive analysis of recommendations formulated during prescription review using a deep neural network classifier in a hospital Setting This retrospective study was conducted at the University Hospital of Strasbourg. Main outcome measures Recommendations were automatically classified according to the coding of the French Society of Clinical Pharmacy. Method Data from 2018 to 2020 were collected from prescription support software. Results 2,930,656 prescription lines were analysed for a total of 119,689 patients. Among these prescription lines, 153,335 resulted in recommendations (n = 48,202 patients). Recommendations were predominantly observed in patients aged 65 years or older (n=26,141 patients) and in patients taking 5 or more medications (44,702). The most frequently identified types of Drug-related problems associated with recommendations were “Non conformity to guidelines or contra-indication” (n =88,523; 57.7%), “Overdosage” (16,975; 11.1%) and “Improper administration” (13, 898; 9.1%). The most frequently encountered drugs were: Paracetamol (n= 10,585; 6.9%), Esomeprazole (6,031; 3.9%), Hydrochlorothiazide (2,951; 1.9%), Enoxaparin (2,191; 1.4%), Tramadol (1,879; 1.2%), Calcium (2, 073; 1.3%), Perindopril (1,950; 1.2%), Amlodipine (1,716; 1.1%), Simvastatin (1,560; 1.0%) and Insulin (1,019; 0.7%). Conclusion The deep neural network classifier used met the challenge of automatically classifying recommendations from a large database without mobilizing significant human resources. The use of such a classifier can lead to alerting caregivers about certain risky attitudes in prescription and administration, and triggering actions to improve practices.

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Section: Discussionsupporting

confidence: 90%

Use of a deep learning application to classify recommendations made by hospital pharmacists during medication prescription review

Alkanj,

Godet,

Johns

et al. 2023

Preprint

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show abstract

“…Eleven articles (84.6%) were published between 2020 and 2023. The training datasets used were very heterogeneous: the length of study varied from 2 weeks14 to 7 years15; the number of prescription orders analysed went from 3116 to 5 804 192 17…”

Section: Resultsmentioning

confidence: 99%

“…Eight publications14 15 17–19 23 24 26 used supervised ML models (online supplemental table 1); unsupervised ML algorithms (online supplemental tale 2) were used in three publications 16 20 25…”

Section: Resultsmentioning

confidence: 99%

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Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

Johns,

Alkanj,

Beck

et al. 2023

Eur J Hosp Pharm

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ObjectivesThe emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders.MethodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase databases were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Results13 articles were selected after review: 12 studies were judged to have high risk of bias; 11 studies were published between 2020 and 2023; 8 were conducted in North America and Asia; 6 analysed orders and detected inappropriate prescriptions according to patient profiles and medication orders; and 7 detected specific inappropriate prescriptions, such as detecting antibiotic resistance, dosage abnormality in prescriptions, high alert drugs errors from prescriptions or predicting the risk of adverse drug events. Various AI models were used, mainly supervised learning techniques. The training datasets used were very heterogeneous; the length of study varied from 2 weeks to 7 years and the number of prescription orders analysed went from 31 to 5 804 192.ConclusionsThis systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.

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“…There is an increasing trend to study how machine learning (ML) tools can be used to augment medical professionals’ decisions concerning diagnosis, treatment safety, and quality of patient care 4 – 10 . Several pharmaceutical studies 11 – 15 have applied ML to find anomalous prescriptions but not tailored to RT. In RT, several studies 16 – 19 have used ML to look at the treatment parameters to detect errors in treatment plans, but did not focus on prescription error detection.…”

Section: Introductionmentioning

confidence: 99%

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Wright

Hales

et al. 2022

npj Digit. Med.

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Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a physician peer-review process, which includes a review of the treatment plan’s dose and fractionation. Potentially, physicians may not identify errors during this manual peer review due to time constraints and caseload. A novel prescription anomaly detection algorithm is designed that utilizes historical data from the past to predict anomalous cases. Such a tool can serve as an electronic peer who will assist the peer-review process providing extra safety to the patients. In our primary model, we create two dissimilarity metrics, R and F. R defining how far a new patient’s prescription is from historical prescriptions. F represents how far away a patient’s feature set is from that of the group with an identical or similar prescription. We flag prescription if either metric is greater than specific optimized cut-off values. We use thoracic cancer patients (n = 2504) as an example and extracted seven features. Our testing set f1 score is between 73%-94% for different treatment technique groups. We also independently validate our results by conducting a mock peer review with three thoracic specialists. Our model has a lower type II error rate compared to the manual peer-review by physicians.

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Detection of overdose and underdose prescriptions—An unsupervised machine learning approach

Cited by 18 publications

References 32 publications

Use of a deep learning application to classify recommendations made by hospital pharmacists during medication prescription review

Use of a deep learning application to classify recommendations made by hospital pharmacists during medication prescription review

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

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