JMIR MEDICAL INFORMATICS

Choudhury & Asan

Review

Role of Artificial Intelligence in Patient Safety Outcomes:
Systematic Literature Review
Avishek Choudhury, MSc; Onur Asan, PhD
School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States

Corresponding Author:
Onur Asan, PhD
School of Systems and Enterprises
Stevens Institute of Technology
1 Castle Point Terrace
Hoboken, NJ, 07030
United States
Phone: 1 2012168901 ext 2012168901
Email: oasan@stevens.edu

Abstract
Background: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient
safety outcomes.
Objective: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating
AI to address and report clinical-level patient safety outcomes.
Methods: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles
published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative,
or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms andnatural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes
were excluded from further review.
Results: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the mostfrequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly basedon decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly basedon decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark
and (2) heterogeneity in AI reporting.
Conclusions: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid
in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed
for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict
safety outcomes in health care settings.
(JMIR Med Inform 2020;8(7):e18599) doi: 10.2196/18599
KEYWORDS
artificial intelligence; patient safety; drug safety; clinical error; report analysis; natural language processing; drug; review

Introduction
Patient safety is defined as the absence of preventable harm toa patient and minimization of the risk of harm associated withthe health care process [1,2]. Every part of the care-giving
process involves a certain degree of inherent risk. Since
resolution WHA55.18 on “Quality of Care: Patient Safety” at
the 55th World Health Assembly was proposed in 2002, there
has been increasing attention paid to patient safety concerns
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and adverse events in health care settings [3]. Despite the safety
initiatives and investments made by federal and local
governments, private agencies, and concerned institutions,studies continue to report unfavorable patient safety outcomes[4,5].
The integration of artificial intelligence (AI) into the health caresystem is not only changing dynamics such as the role of healthcare providers but is also creating new potential to improve
patient safety outcomes [6] and the quality of care [7]. The term
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AI can be broadly defined as a computer program that is capableof making intelligent decisions [8]. The operational definitionof AI we adopt in this review is the ability of a computer orhealth care device to analyze extensive health care data, reveal
hidden knowledge, identify risks, and enhance communication[9]. In this regard, AI encompasses machine learning and natural
language processing. Machine learning enables computers toutilize labeled (supervised learning) or unlabeled (unsupervised
learning) data to identify latent information or make predictions
about the data without explicit programming [9]. Among
different types of AI, machine learning and natural language
processing specifically have societal impacts in the health caredomain [10] and are also frequently used in the health care field[9-12].

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The third category within machine learning is known as
reinforcement learning, in which an algorithm attempts toaccomplish a task while learning from its successes and failures[9]. Machine learning also encompasses artificial neuralnetworks or deep learning [13]. Natural language processing
focuses on building a computer’s ability to understand humanlanguage and consecutively transform text to machine-readable
structured data, which can then be analyzed by machine-learningtechniques [14]. In the literature, the boundary defining natural
language processing and machine learning is not clearly defined.
However, as illustrated in Figure 1, studies in the field of healthcare have been using natural language processing in conjunction
with machine-learning algorithms [15].

Figure 1. Schematic illustration of how natural language processing converts unstructured text to machine-readable structured data, which can then be
analyzed by machine-learning algorithms.

AI has potential to assist clinicians in making better diagnoses[16-18], and has contributed to the fields of drug development[19-21], personalized medicine, and patient care monitoring[14,22-24]. AI has also been embedded in electronic healthrecord (EHR) systems to identify, assess, and mitigate threats
to patient safety [25]. However, with the deployment of AI in
health care, several risks and challenges can emerge at anindividual level (eg, awareness, education, trust), macrolevel(eg, regulation and policies, risk of injuries due to AI errors),
and technical level (eg, usability, performance, data privacy andsecurity).
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The measure of AI accuracy does not necessarily indicate
clinical efficiency [26]. Another common measure, the area
under the receiver operating characteristic curve (AUROC), is
also not necessarily the best metric for clinical applicability[27]. Such AI metrics might not be easily understood byclinicians or might not be clinically meaningful [28]. Moreover,AI models have been evaluated using a variety of parameters
and report different measure(s) such as the F1 score, accuracy,
and false-positive rate, which are indicative of different aspectsof AI’s analytical performance. Understanding the functioningof complex AI requires technical knowledge that is not common
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among clinicians. Moreover, clinicians do not necessarily havethe training to identify underlying glitches of the AI, such asdata bias, overfitting, or other software errors that might resultin misleading outcomes. Such flaws in AI can result in incorrect
medication dosage and poor treatment [29-33].
Furthermore, a system error in a widely used AI might lead tomass patient injuries compared to a limited number of patient
injuries due to a provider’s error [34]. Additionally, there havebeen instances where traditional analytical methods
outperformed machine-learning techniques [9]. Owing to thewide range of effectiveness of AI, it is crucial to understandboth the promising and deterring impacts of AI on patient safety
outcomes [35].
AI in the health care system can assist at both the “clinical” and“diagnostic” levels [36]. AI provides a powerful tool that canbe implemented within the health care domain to reveal subtle
patterns in data, and these patterns can then be interpreted byclinicians to identify new clinical and health-related issues [9].
Recent studies and reviews have primarily focused on theperformance of AI at the diagnostic level, such as for disease
identification [37-42], and the application of AI robotics insurgery and disease management [43-46]. Other studies havealso implemented AI technologies to assist at the clinical level,
including assessing fall risks [47] and medication errors [48,49].
However, many of these studies are centered around AI
development and performance and there is a notable lack ofstudies reviewing the role and impact of AI used at the clinicallevel on patient safety outcomes.
Many studies have reported high accuracy of AI in health care.
However, its actual influence (negative or positive) can only berealized when it is integrated into clinical settings or interpreted
and used by care providers [50]. Therefore, in our view, patient
safety and AI performance might not necessarily complement
each other. AI in health care depends on data sources such as
EHR systems, sensor data, and patient-reported data. EHR
systems may contain more severe cases for specific patient
populations. Certain patient populations may have more ailments

Choudhury & Asanor may be seen at multiple institutions. Certain subgroups ofpatients with rare diseases may not exist in sufficient numbers
for a predictive analytic algorithm. Thus, clinical data retrieved
from EHRs might be prone to biases [9,50]. Owing to thesepotential biases, AI accuracy might be misleading [51] whentrained on a small subgroup or small sample size of patientswith rare ailments.
Furthermore, patients with limited access to health care may
receive fewer diagnostic tests and medications and may haveinsufficient health information in the EHR to trigger an earlyintervention [52]. In addition, institutions record patient
information differently; as a result, if AI models trained at one
institution are implemented to analyze data at another institution,
this may result in errors [52]. For instance, machine-learningalgorithms developed at a university hospital to predictpatient-reported outcome measures, which tend to be
documented by patients who have high education as well as
high income, may not be applicable when implemented at a
community hospital that primarily serves underrepresented
patient groups with low income.
A review [53] conducted in 2017 reported that only about 54%
of studies that developed prediction models based on EHRs
accounted for missing data. Recent studies and reviews havebeen primarily focusing on the performance and influence ofAI systems at a diagnostic level, such as for disease
identification [37-42], and the influence of AI robotics in surgery
and disease management [43-46]; however, there is a lack ofstudies reviewing and reporting the impact of AI used at theclinical level on patient safety outcomes, as well as
characteristics of the AI algorithms used. Thus, it is essential
to study how AI has been shown to influence patient safety
outcomes at the clinical level, along with reported AI
performance in the literature. In this systematic review, we
address this gap by exploring the studies that utilized AI
algorithms as defined in this review to address and reportchanges in patient safety outcomes at the clinical level (Figure
2).

Figure 2. Artificial intelligence (AI) route to patient safety via “Clinical” and “Diagnostic” level interventions. DSS: decision support system.

Methods
Protocol Registration
This systematic review is reported according to the Preferred
Reporting Items for Systematic Reviews and Meta-Analysis(PRISMA) guidelines [54]. We followed the PRISMA Checklist(see Multimedia Appendix 1). Our protocol [55] was registeredwith the Open Science Framework on September 15, 2019.

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Information Sources
We searched for peer-reviewed publications in the PubMed,
PubMed Central, and Web of Science databases from January
2009 to August 2019 to identify articles within the scope andeligibility criteria of this systematic literature review.

Search Strategy
We followed a systematic approach of creating all search terms
to capture all related and eligible papers in the searched
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databases. Keywords used in the search were initially determinedby a preliminary review of the literature and then modified basedon feedback from content experts as well as our institution’s
librarian.
We then refined the search strategy in collaboration with thelibrarian to ensure that all clinical-level patient safety-related
papers (as shown in Figure 2) were covered in our review anddetermined the Medical Subject Headings (MeSH) terms. We
grouped the query keywords, which were derived from MeSH
terms and combined through Boolean (AND/OR) operators to

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identify all relevant studies that matched with our scope andinclusion criteria.
The keywords consisted of MeSH terms such as “safety[MeSH]” and “artificial intelligence [MeSH],” in combination
with narrower MeSH terms (subheadings/related words/phrases)
and free text for “artificial intelligence” and “safety.” We also
included broader key terms to encompass all latent risk factors
affecting patient safety. The final search keywords (Figure 3)
described below were used to explore all databases.

Figure 3. Medical Subject Heading (MeSH) terms and free text used in the systematic literature review.

MeSH terms are organized in a tree-like hierarchy, with more
specific (narrower) terms arranged beneath broader terms. By
default, PubMed includes all of the narrow items in the searchin a strategy known as “exploding” the MeSH term [56].
Moreover, the inclusion of MeSH terms optimizes the searchstrategy [56]. Therefore, the final search query for PubMed was
as follows: (“patient safety” OR “safety” [MeSH] OR “drugsafety” OR “safety-based Drug withdraws” [MeSH] OR“medication error” OR “Medication Error” [MeSH] OR“medication reconciliation” OR “near miss” OR “inappropriateprescribing” OR “clinical error” OR “Clinical alarms” [MeSH])
AND (“Machine learning” [MeSH] OR “Machine learning”
OR “Deep learning” [MeSH] OR “Deep learning” OR “natural
language processing” [MeSH] OR “natural language
processing”).

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Inclusion and Exclusion Criteria
This study focused on peer-reviewed publications satisfying
the following two primary conditions: (1) implementation ofmachine-learning or natural language processing techniques toaddress patient safety concerns, and (2) discussing or reporting
the impact or changes in clinical-level patient safety outcomes.
Any papers that failed to satisfy both conditions were excluded
from this review. For instance, studies only focusing on
developing or evaluating machine-learning models that did notreport or discuss changes or impact on clinical-level patient
safety outcomes were excluded, as well as studies that used AI
beyond our scopes, such as robotics or computer vision.
Secondary research such as reviews, commentaries, andconceptual articles was excluded from this study. The search
was restricted to papers published in English between January
2009 and August 2019.

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Study Selection and Quality Assurance
The two authors together reviewed all of the retrieved
publications for eligibility. We first screened the publications
by studying the titles and abstracts and removed duplications.
We then read the full text for the remaining papers and finalized
the selection. To minimize any selection bias, all discrepancies
were resolved by discussion requiring consensus from both
reviewers and the librarian. Before finalizing the list of papers,
we consulted our results and searched keywords with thelibrarian to ensure that no relevant articles were missed.
A data abstraction form was used to record standardized
information from each paper as follows: authors, aims,
objectives of the study, methods, and findings. Using this form,
we categorized each article based on the type of AI algorithm
as well as clinical-level patient safety outcomes reported.

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Results
Study Selection
Figure 4 illustrates the flowchart of the selection process of thearticles included in this systematic literature review. The initial
search using a set of queries returned 272 publications in
PubMed, 1976 publications in PubMed Central, and 248
publications in Web of Science for a total of 2496 articles. We
used EndNote X9.3.2 to manage the filtering and duplication
removal process. As a first step, we removed duplicates (n=101),
all review/opinion/perspective papers (n=120), and posters or
short abstracts (n=127). The two authors then applied a second
filtering step by reading abstracts and titles (n=2148). The
screening process followed the inclusion and exclusion criteria
explained above, resulting in 80 papers eligible for a full-text
review. The authors then removed 27 more articles based on
the full-text review. Hence, the final number of studies includedin the systematic review was 53, with consensus from both
authors.

Figure 4. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow chart illustrating the process of selecting eligible
publications for inclusion in the systematic review. WoS: Web of Science.

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Table 1 outlines all characteristics of the final selected studies(n=53), including the objective of the study and AI methods
used, as well as classification of all articles by latent risk factorsof patient safety according to (a) Clinical Alarms/Alerts, (b)Clinical Reports, and (c) Adverse Drug Event/Drug Safety.
Table 1 also reports the findings obtained regarding changes inpatient safety outcomes.
The studies mostly reported positive changes in patient safety
outcomes, and in most cases improved or outperformed
traditional methods. For instance, AI was successful inminimizing false alarms in several studies and also improved
real-time safety reporting systems (Table 1). AI was also able
to extract useful information from clinical reports. For example,

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Choudhury & Asan
AI helped in classifying patients based on their ailments andseverity, identified common incidents such as fall risks, delivery
delays, hospital information technology errors, bleeding
complications, and others that pose risks to patient safety. AI
also helped in minimizing adverse drug effects. Further, somestudies reported poor outcomes of AI, in which AI’s
classification accuracy was lower than that of clinicians orexisting standards.
Table 2 outlines the performance and accuracy measures of AI
models used by the final selected studies, demonstrating theheterogeneity in AI performance measures adopted by different
studies.

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Choudhury & Asan

Table 1. Evidentiary table of 53 selected publications.
Reference

Objective

Study theme

AIa method

Chen et al [57]

To classify alerts as real or Clinicalartifacts in online noninva- alarms/alerts
sive vital sign data streams
and minimize alarm fatigue and missed true instability

KNNb, NBc,de

LR , SVM , RF

Findings (patient safety outcomes)fMachine-learning (ML) models could distinguish clinically
relevant pulse arterial O2 saturation, blood pressure, andrespiratory rate from artifacts in an online monitoring dataset(AUCg>0.87)

Clinical
alarms/alerts

MMDi, DTj

ML algorithm along with MMD was effective in suppressing
false alarms

Zhang et al [59] To minimize the rate offalse critical arrhythmia
alarmsClinical
alarms/alerts

SVM

SVM reduced false alarm rates. The model gave an overall
true positive rate of 95% and true negative rate of 85%

Antink et al[60]

Clinical
alarms/alerts

BCTk, SVM, RF, A false alarm reduction score of 65.52 was achieved;
employing an alarm-specific strategy, the model performed
RDACl
at a true positive rate of 95% and true negative rate of 78%.

Ansari et al [58] To minimize false alarmshin the ICU

To reduce false alarms byusing multimodal cardiac
signals recorded from apatient monitor

False alarms for extreme tachycardia were suppressed with
100% sensitivity and specificity

Eerikäinen et al To classify true and false Clinical alarms or[61]
cardiac arrhythmia alarms alerts

RF

Out of 5 false alarms, 4 were suppressed; 77.39% real-timemodel accuracy

Menard et al[62]

Develop a predictive mod- Clinicalel that enables
alarms/alerts
Roche/Genentech Quality
Program Leads oversightof adverse event reportingat the program, study, site,and patient level.

ML (not disclosed)

The ML method identified the sites by risk of underreportingand enabled real-time safety reporting. The proposed model
had an AUC of 0.62, 0.79, and 0.92 for simulation scenariosof 25%, 50%, and 75%, respectively.

Segal et al [63]To determine the clinicalusefulness of medication
error alerts in a real-life
inpatient setting

Probabilistic ML 85% of the alerts were clinically valid, and 80% were considered clinically useful; 43% of the alerts caused changes
in subsequent medical orders. Thus, the model detected
medication errors

Hu et al [64]To detect clinical deteriora- Clinical alarms ortion
alertsKwon et al [65] To develop alarm systems
that predict cardiac arrest
early

Clinical alarms oralertsClinical alarms oralertsThis project was part of a broader effort at Roche/Genentech
Product Development Quality to apply advanced analytics
to augment and complement traditional clinical quality assurance approaches

NNm

NN-based model could detect health deterioration such asheart rate variability with more accuracy than one of thebest-performing early warning scores (ViEWS). The positive
prediction value of NN was 77.58% and the negative prediction value was 99.19%

RF, LR, DEWSn, The DEWS identified more than 50% of patients with inhospital cardiac arrest 14 hours before the event. It allowed
and MEWSo
medical staff to have enough time to intervene. The AUC
and AUPRCp of DEWS was 0.85 and 0.04, respectively, andoutperformed MEWS with AUC and AUPC of 0.60 and
0.003, respectively; RF with AUC and AUPC of 0.78 and
0.01, respectively; and LR with AUC and AUPRC of 0.61
and 0.007, respectively.
DEWS reduced the number of alarms by 82.2%, 13.5%, and42.1% compared with the other models at the same sensitivity

Gupta andPatrick [66]To classify clinical incidents

Clinical Report

J48q, NB multino- The selected models performed poorly in classifying incident
categories (48.77% best, using J48), but performed comparmial, and SVM
atively better in classifying free text (76.49% using NB).

Wang et al [67]

To identify multiple incident types from a single
reportClinical ReportCompares binary Binary classifier improved identification of common incident
types: falls, medications, pressure injury, aggression, docurelevance, CCr
mentation problem, and others. Automated identification
enabled safety problems to be detected and addressed in amore timely manner

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Reference

Objective

Study theme

AIa method

Findings (patient safety outcomes)

Zhou et al [49]To extract informationfrom clinical reports

Clinical Report

SVM, NB, RF,

ML algorithms identified the medication event originatingstages, event types, and causes, respectively. The models
improved the efficiency of analyzing the medication event
reports and learning from the reports in a timely manner with(SVM) F1 of 0.792 and (RF) F1 of 0.925

To analyze patient safety
reports

Clinical Report

NLPt

Pyxis Discrepancy and Pharmacy Delivery Delay were foundto be the main two factors affecting patient safety. The NLP
models significantly reduced the time required to analyze
safety reports

El Messiry et al To analyze patient feed[69]backClinical Report

NLP

Care-related complaints were influenced by money andemotionChondrogiannis To identify the meaning of Clinical Reportet al [70]abbreviations used in clinical studies

NLP

Each clinical study document contained about 6.8 abbreviations. Each abbreviation can have 1.25 meanings on average.
This helped in identification of acronyms

Liang and Gong To extract information
Clinical Report[71]
from patient safety reports

Multilabel classi- Binary relevance was the best problem transformation algofication methods rithm in the multilabeled classifiers. It provided suggestions
on how to implement automated classification of patient
safety reports in clinical settings

Ong et al [72]

To identify risk events inclinical incident reports

Text classifiers
based on SVM

SVM performed well on datasets with diverse incident types(85.8%) and data with patient misidentification (96.4%).
About 90% of false positives were found in “near-misses”
and 70% of false negative occurred due to spelling errors

Taggart et al[73]

To identify bleeding events Clinical Reportusing in clinical notes

NLP, SVM,

Rule-based NLP was better than the ML approach. NLP detected bleeding complications with 84.6% specificity, 62.7%
positive predictive value, and 97.1% negative predictive
value. It can thus be used for quality improvement and prevention programs

Denecke et al[74]

To minimize any loss of
information during a doctor-patient conversation

Clinical Report

NLP

Electronic health platform provides an intuitive conversational user interface that patients use to connect to their therapist
and self-anamnesis app. The app also allows data sharing
among treating therapists

Evans et al [75] To determine the incident
type and the severity ofharm outcome

Clinical Report

J48, SVM, andNB

The SVM classifier improved the identification of patient
safety incidents. Incident reports containing deaths weremost easily classified with an accuracy of 72.82%. The
severity classifier was not accurate to replace manual
scrutiny

Wang et al [76]Clinical Report

CNN and SVM
ensemble

CNN achieved high F scores (>85%) across all test datasets
when identifying common incident types, including falls,
medications, pressure injury, and aggression. It improved
the process by 11.9% to 45.10% across different datasets

Klock et al [47] To understand the root
Clinical Reportcauses of falls and increase
learning from fall reports
for better prevention ofpatient falls.

SVM, RF, and

The model identified high and low scoring fall reports. Mostof the patient fall reports scores were between 0.3 and 0.4,
indicating poor quality of reports

Li et al [77]Ensemble-ML

The adverse event risk score at the 0.1 level could identify
57.2% of adverse events with 26.3% accuracy from 9.2% of
the validation sample. The adverse event risk score of 0.04
could identify 85.5% of adverse events

NLP

NLP identified 82% of acute renal failure cases comparedwith 38% for patient safety indicators. Similar results wereobtained for venous thromboembolism (59% vs 46%),
pneumonia (64% vs 5%), sepsis (89% vs 34%), and postoperative myocardial infarction (91% vs 89%)

Fong et al [68]

To identify the type andseverity of patient safetyincident reports

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Clinical report

To stratify patient safety
Clinical Reportadverse event risk and predict safety problems of individual patients

Murff et al [78] To identify postoperative
surgical complications
within a comprehensive
electronic medical record

XSL• FOand MLPs

Clinical Reportuv

CNN , and ET

RNNw

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Reference

Objective

Study theme

Wang et al [79]To automate the identifica- Clinical Reporttion of patient safety incidents in hospitals

AIa method

Findings (patient safety outcomes)

Text-based classi- For severity level, the F score for severity assessment code
fier: LR, SVM(SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (lowrisk) on balanced data. With stratified data, a high recall wasachieved for SAC1 (82.8%-84%), but precision was poor
(6.8%-11.2%).
High-risk incidents (SAC2) and medium-risk incidents(SAC3) were often misclassified.
Reports about falls, medications, pressure injury, aggression,and blood tests were identified with high recall and precision

Rosenbaum and To detect Wrong Blood in Clinical Report
Baron [80]Tube errors and mitigate
patient harm

LR, SVM

In contrast to the univariate analysis, the best performing
multivariate delta check model (SVM) identified errors witha high degree of accuracy (0.97)

McKnight [81]To improve the ability to
Clinical Report
extract clinical informationfrom patient safety reports
efficiently

NLP

The semisupervised model categorized patient safety reports
into their appropriate patient safety topic and avoided overlaps; 85% of unlabeled reports were assigned correct labels.

To analyze patient safety
reports describing healthhazards from electronic
health records

Text mining
based on: NB,
KNN, rule induction

Marella et al[82]

Clinical Report

It helped NCPSx analysts to develop policy and mitigation
decisions
The NB kernel performed best, with an AUC of 0.927, accuracy of 0.855, and F score of 0.877.
The overall proportion of cases found relevant was comparable between manually and automatically screened cases; 334
reports identified by the model as relevant were identified
as not relevant, implying a false-positive rate of 13%.
Manual screening identified 4 incorrect predictions, implying
a false-negative rate of 29%

Ye et al [83]Fong et al [84]To validate a real-time
Clinical Report
early warning system topredict patients at high riskof inpatient mortality during their hospital episodes

RF, XGBy,
boosting SVM,

To identify health informa- Clinical Reporttion technology-related
events from patient safety
reports

Unigram and Bigram LR, SVM

Unigram models performed better than Bigram and combined

PANDIT

27 out of 74 (36.5%) PANDIT advice differed from those
provided by diabetes nurses. However, only one of these(1.4%) was considered unsafe by the panel

The 10‐fold crossvalidation improved the identification of
drug-drug interaction with AUC>0.97, which is significantly
greater than the analogously developed ML model (0.67)

Simon et al [85] To establish whether paDrug safety
tients with type 2 diabetes

LASSOz, andKNN

can safely use PANDITcc
and whether its insulin
dosing advice is clinicallysafeSong et al [86]To predict drug-drug inter- Drug safety
actions

SVM

Hammann et al[87]

To identify drugs that
Drug safety
could be suspected of
causing adverse reactionsin the central nervous system, liver, and kidneys

CHAIDdd andBean et al [88]To predict adverse drug
reactions

LR, SVM, DT,
NLP, own model

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Drug safety

CART

ee

The modified early warning system accurately predicted thepossibility of death for the top 13.3% (34/255) of patients atleast 40.8 hours before deathmodels. It identified HITaa-related events trained on PSEbb
free-text descriptions from multiple states and health caresystems. The unigram LR model gave an AUC of 0.931 andan F1 score of 0.765. LR also showed potential to maintaina faster runtime when more reports are analyzed. The final
HIT model had less complexity and was more easily sharable

CART exhibited high predictive accuracy of 78.94% for allergic reactions, 88.69% for renal, and 90.22% for the liver.
CHAID model showed a high accuracy of 89.74% for thecentral nervous system
The proposed model (own model) outperformed traditional
LR, SVM, DT, and predicted adverse drug reactions withan AUC of 0.92

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Reference

Objective

Study theme

Hu et al [89]To predict the appropriate- Drug safety
ness of initial digoxin
dosage and minimize drugdrug adverse interactions

AIa method

Findings (patient safety outcomes)

C4.5, KNN,
In the non drug-drug interaction group, the AUC of RF,
CART, RF, MLP, MLP, CART, and C4.5 was 0.91, 0.81, 0.79, and 0.784, reand LR
spectively; for the drug-drug interaction group, the AUC ofRF, CART, MLP, and C4.5 was 0.89, 0.79, 0.77, and 0.77,
respectively.
DT-based approaches and MLP can determine the initial
dosage of a high-alert digoxin medication, which can increase
drug safety in clinical practiceTang et al [90]

To identify adverse drug
effects from unstructured
hospital discharge summaries

Drug safety

NLP

A total of 33 trial sets were evaluated by the algorithm andreviewed by pharmacovigilance experts. After every 6 trial
sets, drug and adverse event dictionaries were updated, andrules were modified to improve the system. The model
identified adverse events with 92% precision and recall

Hu et al [91]

To predict the dosage of
warfarin

Drug safety

KNN, SVRff,

The proposed model improved warfarin dosage when compared to the baseline (mean absolute error 0.394); reduced
mean absolute error by 40.04%

Hasan et al [92] To improve medication
reconciliation task

Drug safety

NN-BPgg, MThh
LR, KNN

Collaborative filtering identified the top 10 missing drugs
about 40% to 50% of the time and the therapeutic missing
drugs about 50% to 65% of the time

Labovitz et al[93]

To evaluate the use of a
Drug safety
mobile AI platform on
medication adherence instroke patients on anticoagulation therapy

Cell phone–based Mean (SD) cumulative adherence based on the AI platform
AI platform
was 90.5% (7.5%). Plasma drug concentration levels indicated that adherence was 100% (15/15) and 50% (6/12) in theintervention and control groups, respectively

Long et al [94]To improve the reconcilia- Drug safetytion method

iPad-based soft- All patients completed the task. The software improved
ware tool with an reconciliation; all patients identified at least one error in their
AI algorithm
electronic medical record medication list; 8 of 10 patients
reported that they would use the device in the future. The
entire team (clinical and patients) liked the device and preferred to use it in the future

Reddy et al [95] To assess proof of concept, Drug safety
safety, and feasibility of

ABC4D

ABC4Dii in a free-living
environment over 6 weeks

ABC4D was safe for use as an insulin bolus dosing system.
A trend suggesting a reduction in postprandial hypoglycemia
was observed.
The median (IQR) number of postprandial hypoglycemia
episodes within 6 h after the meal was 4.5 (2.0-8.2) in week
1 versus 2.0 (0.5-6.5) in week 6 (P=.10). No episodes of severe hypoglycemia occurred during the study

Schiff et al [96] To evaluate the perforDrug safety
mance and clinical usefulness of medication error
alerts generated by analerting system

MedAware, prob- 75% of the chart-reviewed alerts generated by MedAware
abilistic ML
were valid from which medication errors were identified. Of
these valid alerts, 75.0% were clinically useful in flagging
potential medication errors.

Li et al [97]

To develop a computerized Drug safety
algorithm for medication
discrepancy detection andassess its performance onreal-world medication reconciliation data

Hybrid system
consisting of ML
algorithms and
NLP

Carrell et al[98]

To identify evidence ofproblem opioid use inelectronic health records

Drug safety

NLP

The NLP-assisted manual review identified an additional
728 (3.1%) patients with evidence of clinically diagnosed
problem opioid use in clinical notes.

Tinoco et al[99]

To evaluate the source of
information affecting different adverse events

Drug safety

CSSjj (ML)CSS detected more hospital-associated infections than manual chart review (92% vs 34%); CSS missed events that were
not stored in a coded format

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The hybrid algorithm yielded precision (P) of 95.0%, recall(R) of 91.6%, and F value of 93.3% on medication entity
identification, and P=98.7%, R=99.4%, and F=99.1% on
attribute linkage.
The combination of the hybrid system and medication
matching system gave P=92.4%, R=90.7%, and F=91.5%,
and P=71.5%, R= 65.2%, and F=68.2% on classifying thematched and the discrepant medications, respectively

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JMIR MEDICAL INFORMATICS
Reference

Objective

Choudhury & Asan
Study theme

AIa method

Findings (patient safety outcomes)

Onay et al [100] To classify approved drugs Drug safety
from withdrawn drugs andthus reduce adverse drug
effects

SVM, Boosted
The Gaussian SVM model yielded 78% prediction accuracy
and Bagged trees for the drug dataset, including all diseases.
(Ensemble)
The ensemble of bagged tree and linear SVM models involved 89% of the accuracies for psycholeptics and psychoanalytic drugs

Cai et al [101]To discover drug-drug in- Drug safety
teractions from the Foodand Drug Administration’s
adverse event reporting
system and thus prevent
patient harm

Causal Associa- CARD demonstrated higher accuracy in identifying known
tion Rule Discov- drug interactions compared to the traditional method (20%ery (CARD)
vs 10%);

Dandala et al[102]

To extract adverse drug
Drug safety
events from clinical narratives and automate pharmacovigilance.

BilSTMkk, CRF- Joint modeling improved the identification of adverse drug
events from 0.62 to 0.65
NNll

Dey et al [103]

To predict and prevent ad- Drug safety
verse drug reactions at an
early stage to enhance drug
safetyDeep learning

CARD yielded a lower number of drug combinations thatare unknown to interact (50% for CARD vs 79% for association rule mining).

Neural fingerprints from the deep learning model(AUC=0.72) outperformed all other methods in predicting
adverse drug reactions.
The model identified important molecular substructures thatare associated with specific adverse drug reactions

Yang et al [104] To identify medications,
Drug safety
adverse drug effects, andtheir relations with clinicalnotes

MADEx, LSTM- MADEx achieved the top-three best performances (F1 score
RNNmm, CRFnn, of 0.8233) for clinical name entity recognition, adverse drug
effect, and relations from clinical texts, which outperformed
SVM, RF
traditional methods

Chapman et al[105]

To identify adverse drug
Drug safety
effect symptoms and drugs
in clinical notes

NLP

Lian et al [106]To detect adverse drug reactions

LRMoo, BNMpp, Experimental results showed the usefulness of the proposed
pattern discovery method by improving the standard baseline
BCP-NNqq
adverse drug reaction by 23.83%

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Drug safety

The micro-averaged F1 score was 80.9% for named entity
recognition, 88.1% for relation extraction, and 61.2% for the
integrated systems

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JMIR MEDICAL INFORMATICSa

Choudhury & Asan

Reference

Objective

Study theme

AIa method

Findings (patient safety outcomes)

Huang et al[107]

To predict adverse drug
effects

Drug safety

SVM, LR

The proposed computational framework showed that an insilico model built on this framework can achieve satisfactory
cardiotoxicity adverse drug reaction prediction performance(median AUC=0.771, accuracy=0.675, sensitivity=0.632,
and specificity=0.789).

AI: artificial intelligence.

b

KNN: K-nearest neighbor.

c

NB: naive Bayes.

dLR: logistic regression.

e

SVM: support vector machine.

f

RF: random forest.

g

AUC: area under the curve.

h

ICU: intensive care unit.

i

MMD: multimodal section.

j

DT: decision tree.

k

BCT: binary classification tree.

l

RDAC: regularized discriminant analysis classifier.

m

NN: neural network.

n

DEWS: deep learning–based early warning system.

o

MEWS: modified early warning system.

p

AUPRC: area under the precision-recall curve.

q

J48: decision tree algorithm.

r

CC: closure classifier.

s

MLP: multilayer perceptron.

t

NLP: natural language processing.

u

CNN: convolutional neural network.

v

ET: extra tree.

w

RNN: recurrent neural network.

x

NCPS: National Center for Patient Safety.

y

XGB: extreme gradient boosting.

z

LASSO: least absolute shrinkage and selection operator.

aa

HIT: health information technology.

bb

PSE: patient safety event.

cc

PANDIT: Patient Assisting Net-Based Diabetes Insulin Titration.

dd

CHAID: Chi square automatic interaction detector.

ee

CART: classification and regression tree.

ff

SVR: support vector regression.

gg

NN-BP: neural network-back propagation.

hh

MT: model tree.

ii

ABC4D: Advanced Bolus Calculator For Diabetes.

jj

CSS: clinical support system.

kk

BiLSTM: bi-long short-term memory neural network.

ll

CRF-NN: conditional random field neural network.

mm

LSTM-RNN: long short-term memory-recurrent neural network.

nn

CRF: conditional random field neural network.

oo

LRM: logistic regression probability model.

pp

BNM: Bayesian network model.

qq

BCP-NN: Bayesian confidence propagation neural network.

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Choudhury & Asan

Table 2. Performance of artificial intelligence.
Reference

Best model recommended

Comparison/othermodelsPerformance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measureother

Huanget al [107]SVMb

Logistic regression

0.675

0.771

0.632

0.789

N/Ac

N/A

N/A

Lian et al [106]Ensemble ofthree models

Bayesian networkmodel; likelihood
ratio model;

N/A

N/A

N/A

N/A

N/A

N/A

Chisquare
improvedby28.83%

CRF; RF model for
relation extraction

N/A

N/A

N/A

N/A

N/A

0.612

N/A

Yang et al [104]MADEx (longRNNh; CRF; SVM;
short-term memo- RF
ry CRF+SVM)

N/A

N/A

0.6542

N/A

0.57580.6125

N/A

Dey et al [103]

Neural finger10 other chemical
print (deep learn- fingerprints
ing)

0.91

0.82

0.50

0.93

N/A

0.400

N/A

Dandala et al[102]

BiLSTMi+CRF(joint and external resources)

BiLSTM+CRF (sequential); BiLSTM+CRF (joint)

N/A

N/A

0.822
N/A
concept
extraction;
0.855 relation classification

0.846
concept
extraction;
0.888 relation classification

0.83 concept
extraction;
0.87 relation
classification

N/A

Cai et al [101]CARDj

Association rulemining

N/A

N/A

N/A

N/A

N/A

N/A

Identifying druginteraction
20%

Onay et al [100]LSVMk

Boosted and bagged 0.89
trees (ensemble)

0.88

0.83

1.00

N/A

0.91

N/A

Tinoco et al [99]Computerized
surveillance system

Manual chart review N/A

N/A

N/A

N/A

N/A

N/A

Numberof
eventsdetected
92%

BCPNNdChapman et al[105]

Integrated NLPefwith RF model
for relation extraction and CRFg
model

(HAIl),
82%(SSIm),
91%(LRTIn),99%(UTIo),
100%(BSIp),
52%(ADEq)

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Reference

Best model recommended

Comparison/othermodelsChoudhury & Asan
Performance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measure

other

Carrel et al [98]

NLP-assisted
manual review

Manual chart review N/A

N/A

N/A

N/A

N/A

N/A

Identified
3.1%
additional patientswithopioidproblems

Li et al [97]

NLP-based hybrid model

Rule-based method; N/A
CRF

N/A

0.907

N/A

0.924

0.915

N/A

Schiff et al [96]

MedAware, aTraditional CDS
probabilistic machine-learning

0.75

N/A

N/A

N/A

N/A

N/A

75% ofthe identified
alertswere
clinically meaningful

N/A

N/A

N/A

N/A

N/A

N/A

ABC4D
was superior to
nonadaptive bolus calculator
and also
more user
friendly

N/A

N/A

N/A

N/A

N/A

N/A

100%
adherence in
the intervention
group

Logistic regression; N/A
KNNv and popu- KNN; random algorithm; co-occurlar algorithm
rence; drug popularity

N/A

N/A

N/A

N/A

N/A

Simplealgorithms
such aspopularalgorithm,
co-occurrence,
andKNN
performed
betterthan
morecomplex logistic regression

CDSr system

Reddy et al [95]

ABC4Ds smart- N/Aphone app (basedon CBRt, an AIu
technique)Long et al [93]AI smartphoneapp

Hasan et al [92]Co-occurrence

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N/A

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Reference

Best model recommended

Comparison/othermodelsChoudhury & Asan
Performance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measureother

Hu et al [91]Bagged SVRw
and bagged voting

MLPx; model tree;
KNN

N/A

N/A

N/A

N/A

N/A

N/A

Mean
absolute
error for
both
0.210

Tang et al [90]

NLP

N/A

N/A

N/A

0.59

N/A

0.75

N/A

N/A

Hu et al [89]RF

C4.5; KNN;

0.839

0.912

0.782

0.888

N/A

N/A

N/A

Logistic regression; N/A
SVM; decision tree;
NLP

0.92

N/A

N/A

N/A

N/A

N/A

Hamma et al [87] CART

CART and CHAIDz 0.902

N/A

N/A

N/A

N/A

N/A

CHAID
outperformed
CART
only incentralnervoussystemclassification

Song et al [86]Similarity-based
SVM

Analogous machine- N/Alearning algorithms(not mentioned)

N/A

0.24

0.97

0.68

N/A

N/A

Simon et al [85]PANDITaa

Nurses

0.635

N/A

N/A

N/A

N/A

N/A

36.5%
PANDIT recommendation
did not
matchwith thenurses;1.4% ofthe recommendations
were unsafe.

Fong et al [84]Unigram logistic
regression

Unigram, bigram,
N/Aand combined logistic regression and
SVM

0.914

0.830

N/A

0.838

0.765

Unigram
SVM
and logistic regression
werecomparable

Ye et al [83]RFLinear and nonlinear N/Amachine-learning algorithms

N/A

N/A

N/A

N/A

N/A

C-statistic of
0.884

0.927

N/A

N/A

N/A

0.877

N/A

Labeled
N/A
0.52; unlabeled
0.80

N/A

N/A

N/A

N/A

N/A

CARTy; MLP; logistic regression
Bean et al [88]Own model

Marella et al [82] Naïve Bayes ker- Naïve Bayes; KNN
nel
and rule induction
McKnight [81]NLP; SELFbb

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N/A

0.855

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Reference

Best model recommended

Comparison/othermodels

Logistic regression

Choudhury & Asan
Performance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measureother

Rosenbaum andBaron [80]

SVM

N/A

0.97

0.80

0.96

N/A

N/A

Positive
predictive value 0.52

Wang et al [79]Binary SVM with Regularized logistic N/Aradial basis func- regression; linear
tion kernel
SVM

N/A

0.783

N/A

0.783

0.783

N/A

Gupta andPatrick [66]Naïve Bayes
multinomial

N/A

0.96

0.78

0.98

0.79

0.78

Kappa
0.76;
mean
absolute
error
0.03

Wang et al [67]Ensemble classifi- Binary relevance ofer chain of SVM SVM, classifier
with radial basis chain of SVM
function kernel

0.654

N/A

0.791

N/A

0.689

0.736

Hammingloss
0.80

Zhou et al [49]

SVM and RFNaïve Bayes and
MLP

N/A

N/A

0.769SVM N/A
for event
type;
0.927 RFfor even
cause

0.788
SVM forevent
type;
0.927 RFfor eventcause

0.758 SVM
N/A
for event type;0.925 RF for
event cause

Fong et al [68]

NLP with SVM

NLP with decision
tree

0.990

0.960

0.920

1.00

1.000

0.960

N/A

El Messiry et al
[69]

NLP

Scaled linear discrim- 0.730
inant analysis; SVM;

N/A

0.770

0.696

N/A

N/A

N/A

J48; naïve Bayes;
SVM

LASSOcc and elastic-net regularized
generalized linear
models; max entropy; RF; neuralnetwork
Chondrogiannis
et al [70]

NLP

N/A

N/A

N/A

N/A

N/A

N/A

Model
developed in
this
study
identified thateachclinicalreportcontains
about
6.8 abbreviations

Liang and Gong
[71]Naïve Bayes with SVM; decision rule; N/Abinary relevance decision tree; KNN

N/A

N/A

N/A

N/A

N/A

Micro Fmeasure0.212

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N/A

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JMIR MEDICAL INFORMATICS
ReferenceOng et al [72]Best model recommended

Text classifier
with SVM

Taggart et al [73] Rule-based NLP

Comparison/othermodelsChoudhury & Asan
Performance measures of the best model
Accuracy AUROCa

Recall

N/A

0.920
multitypedataset;0.980 patient
misidentification
datasetSVM; extra trees;
N/A
convolutional neuralnetwork

Text classifier withnaïve Bayes

Specificity

Precision

F measure

other

0.830
N/A
multitypedataset;0.940 patient
misidentification
dataset0.880
multitypedataset;0.990 patient
misidentification
dataset0.860 multitype dataset;
0.960 patient
misidentification dataset

N/A

N/A

N/A

0.846

N/A

N/A

Positive
predictive value
0.627;
negative
predictive value
0.971

Denecke et al[74]

AIMLdd

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Minimize information loss
duringclinicalvisitsEvans et al [75]SVM

J48; naïve Bayes

0.728

0.891 inci- N/Adent type;
0.708
severityof harm

N/A

N/A

N/A

N/A

Wang et al [76]Convolutional
neural network

SVM

N/A

N/A

N/A

N/A

N/A

0.850

N/A

Klock et al [47]

SVM and RNNee RF

0.899
SVM;
0.900
RNN

N/A

N/A

N/A

N/A

0.648899
SVM; 0.889
RNN

N/A

Li et al [77]Ensemble machine learning(bagging, boosting, and random
feature method)

N/A

N/A

N/A

0.572
N/A
from 0.10
riskscore;
0.855
from 0.04
risk score

N/A

N/A

C-statistic
0.880

Muff et al [78]

NLP

Patient safety indica- N/A
tors

N/A

0.770

0.938

N/A

N/A

N/A

Kwon et al [65]Deep learningModified earlyN/A
based early warn- warning system; RF;ing system
logistic regression

0.850

0.757

0.765

N/A

1.000

AUPRCff

Hu et al [64]Neural network
model

ViEWSgg

0.880

N/A

N/A

N/A

0.81

Positive
predictive value
0.726

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N/A

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Reference

Segal et al [63]Best model recommended

MedAware (aComparison/othermodelsLegacy CDS

Choudhury & Asan
Performance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measure

other

N/A

N/A

N/A

N/A

N/A

N/A

Clinically relevant
85%,
alert
burden
0.04%

N/A

0.970

N/A

N/A

N/A

N/A

N/A

CDSShh) + EHRii

Menard et al [62] Machine learning N/A(name not disclosed)
Eerikainen et al
[61]

RF

Binary classification N/Atree; regularized discriminant analysis
classifier; SVM; RF

N/A

0.950

0.780

N/A

0.782

N/A

Antink et al [60]Combined (selecting the best machine- learningalgorithm foreach alarm type)

Binary classification N/Atree; regularized discriminant analysis
classifier; SVM; RF

N/A

0.950

0.780

N/A

0.782

N/A

Zhang et al [59]Cost-sensitive
SVM

N/A

N/A

N/A

0.950

0.850

N/A

0.809

N/A

Ansari et al [58]Multimodal ma- N/Achine learning using decision tree

N/A

N/A

0.890

0.850

N/A

0.762

N/A

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Reference

Chen et al [57]aBest model recommendedRF

Comparison/othermodelsN/A

Choudhury & Asan
Performance measures of the best model
Accuracy AUROCa

RecallSpecificity

Precision

F measure

other

N/A

N/A

N/A

N/A

N/A

N/A

0.870

AUROC: area under the receiver operating characteristic curve.

b

SVM: support vector machine.

c

N/A: not applicable (Not reported).

d

BCPNN: Bayesian confidence propagation neural network.

e

NLP: natural language processing.

f

RF: random forest.

g

CRF: conditional random field.

h

RNN: recurrent neural network.

i

BiLSTM: Bi-long short-term memory neural network.

j

CARD: casual association rule discovery.

k

LSVM: linear support vector machine.

l

HAI: hospital-associated infection.

m

SSI: surgical site infection.

n

LRTI: lower respiratory tract infection.

o

UTI: urinary tract infection.

p

BSI: bloodstream infection.

q

ADE: adverse drug event.

r

CDS: clinical decision support.

s

ABC4D: Advanced Bolus Calculator For Diabetes.

t

CBR: case-based reasoning.

u

AI: artificial intelligence.

v

KNN: K-nearest neighbor.

w

SVR: support vector regression.

x

MLP: multilayer perceptron.

y

CART: classification and regression tree.

z

CHAID: Chi square automatic interaction detector.

aa

PANDIT: Patient Assisting Net-Based Diabetes Insulin Titration.

bb

SELF: semisupervised local Fisher discriminant analysis.

cc

LASSO: least absolute shrinkage and selection operator.

dd

AIML: artificial intelligence markup language.

ee

RNN: recurrent neural network.

ff

AUPRC: area under the precision-recall curve.

gg

VieWS: VitalPac Early Warning Score.

hh

CDSS: clinical decision support system.

ii

EHR: electronic health record.

Study Themes and Findings
Clinical Alarms and Alerts
Nine publications addressed clinical alarms/alerts using AI
techniques. The most widely used method was random forest(n=5) followed by support vector machine (n=3) and neuralnetwork/deep learning (n=3).
Studies under this category used electrocardiogram data fromthe PhysioNet Challenge public database and PhysioNet MIMIC
II database. Five studies focused on reducing false alarm rates
arising due to cardiac ailments such as arrhythmia and cardiac
arrest in an intensive care unit setting [58-61,65]. The remaining
four studies focused on improving the performance of clinicalalarms in classifying clinical deterioration such as fluctuation
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adverse medication events [63], and deterioration of patient
health with hematologic malignancies [64].

Clinical Reports
We identified 21studies concerning clinical reports. Studies inthis group primarily focused on extracting information fromclinical reports such as safety reports (internal to the hospital),patient feedback, EHR notes, and others typically derived fromincident monitoring systems and patient safety organizations.
The most widely used method was support vector machine(n=11), followed by natural language processing (n=7) andnaïve Bayes (n=5). We also identified decision trees (n=4), deeplearning models (n=3), J48 (n=2), and other (n=9) algorithms.

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JMIR MEDICAL INFORMATICS
The majority of articles focused on automating the process ofpatient safety classifications. These studies used machinelearning and natural language processing techniques to classify
clinical incidents [66] from the Incident Information
Management System and to identify risky incidents[71,79,81,108] in patient safety reports retrieved from different
sources, including the university database and the Veterans
Affairs National Center for Patient Safety database. Somestudies also analyzed medication reports [49] from structured
and unstructured data obtained from the patient safety
organization, and evaluated patient feedback [69] retrieved fromthe Patient Advocacy Reporting System developed at Vanderbilt
and associated institutions.
Several studies focused on classifying the type and severity ofpatient safety incident reports using data collected by different
sources such as universities [75], and incident reporting systems
such as Advanced Incident Management Systems (acrossAustralia) and Riskman [67,75,76]. Others analyzed hospital
clinical notes internally (manually annotated by clinicians anda quality committee) and data retrieved from patient safety
organizations to identify adverse incidents such as delayed
medication [68], fall risks [47,67], near misses, patient
misidentification, spelling errors, and ambiguity in clinical notes[109]. One study analyzed clinical study descriptions fromclinicaltrials.gov and implemented an AI system to detect all
abbreviations and identify their meaning to minimize incorrectinterpretations [70]. Another study used inpatient laboratory
test reports from Sunquest Laboratory Information System andidentified wrong blood in tube errors [80].
Studies used clinical reports from various sources, including
patient safety organizations, EHR data from Veterans HealthAdministration and Berkshire Health Systems, and deidentified
notes from the Medical Information Mart for Intensive Care.
These studies focused on extracting relevant information[74,77,82,84] to predict bleeding risks among critically illpatients [73], postoperative surgical complications [78],mortality risk [83], and other factors such as lab test results andvital signs [77] influencing patient safety outcomes.

Choudhury & Asan

Adverse Drug Events or Drug Safety
Twenty-three publications were classified under drug safety.
These studies primarily addressed adverse effects related todrug reactions. The most widely used method was random forest(n=8), followed by natural language processing (n=7) andlogistic regression (n=6). Algorithms including natural language
processing (n=5), logistic regression (n=4), mobile or web apps(n=3), AI devices (n=2), and others (n=5) were also used.
Studies in this category retrieved data from different repositories
such as DrugBank, Side Effect Resource, the Food and Drug
Administration (FDA)’s adverse event reporting system,
University of Massachusetts Medical School, Observational
Medical Outcomes Partnership database, and Human
Protein-Protein Interaction database to identify adverse druginteractions and reactions that can potentially negatively
influence patient health [86-88,101,102,105-107,110]. Somestudies also used AI to predict drug interactions by analyzing
EHR data [88], unstructured discharge notes [90], and clinicalcharts [99,104]. One study also used AI to identify drugs thatwere withdrawn from the commercial markets by the FDA[100].
Some studies used AI to predict the dosage of medicines suchas insulin, digoxin, and warfarin [85,89,91,95]. AI in drug safety
was also used to scan through the hospital’s EHR data andidentify medication errors (ie, wrong medication prescriptions)[96]. One study used AI to monitor stroke patients and track
their medication (anticoagulation) intake [93]. Several studies
used AI to predict a medication that a patient could be
consuming but was missing from their medication list or healthrecords [92,94,97]. Another study used AI to review clinicalnotes and identify evidence of opioid abuse [98].

Visual Representations of Safety and Chronology of theStudiesFigure 5 illustrates the details of patient safety issues/outcomesstudied and reported under each classified theme using AI
algorithms at the clinical level.

Figure 5. Identified factors influencing patient safety outcomes. EHR: electronic health record.

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Figure 6 further shows how the application of AI in studies
reporting patient safety outcomes in our review evolved over

Choudhury & Asantime between January 2009 and August 2019.

Figure 6. Timeline of artificial intelligence application to address factors influencing patient safety (clinical reports, drug safety, and clinical alarms)
between 2009 and August 2019. ABC4D: Advanced Bolus Calculator For Diabetes; AI: artificial intelligence; BCP-NN: Bayesian confidence propagationneural network; BCT: binary classification tree; BiLSTM: bi-long short-term memory neural network; BNM: Bayesian network model; CART:
classification and regression tree; CHAID: Chi-square automatic interaction detector; CRF-NN: conditional random field neural network; DEWS: deeplearning-based early warning system; DT: decision tree; KNN, K-nearest neighbor; LASSO: least absolute shrinkage and selection operator; LR: logistic
regression; LSTM-RNN: long short-term memory-recurrent neural network; MEWS: modified early warning system; ML: machine learning; MLP:
multilayer perception; MMD; multimodal detection; MT: model tree; NB: naive Bayes; NLP: natural language processing; NN: neural network; NN-BP:
neural network back propagation; PANDIT: Patient Assisting Net-Based Diabetes Insulin Titration; RF: random forest; RNN: recurrent neural network;
SVM: support vector machine; SVR, support vector regression; XGB; extreme gradient boosting.

Discussion
Principal Findings
Many studies have been conducted to exhibit the analytical
performance of AI in health care, particularly as a diagnostic
and prognostic tool. To our knowledge, this is the firstsystematic review exploring and portraying studies that show
the influence of AI (machine-learning and natural language
processing techniques) on clinical-level patient safety outcomes.
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We identified 53 studies within the scope of the review. These
53 studies used 38 different types of AI systems/models to
address patient safety outcomes, among which support vector
machine (n=17) and natural language processing (n=12) werethe most frequently used. Most of the reviewed studies reported
positive changes in patient safety outcomes.
Analysis of all studies showed that there is a lack of astandardized benchmark among reported AI models. Despite
varying AI performance, most studies have reported a positive
impact on safety outcomes (Table 2), thus indicating that safety
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JMIR MEDICAL INFORMATICS
outcomes do not necessarily correlate to AI performancemeasures [26]. For example, one identified study with anaccuracy of 0.63 that implemented Patient Assisting Net-Based
Diabetes Insulin Titration (PANDIT) reported a negative impact
of AI on safety outcomes. The PANDIT-generated
recommendations that did not match with the recommendationsof nurses (1.4% of the recommendations) were identified as
unsafe [85]. In contrast, the study implementing natural language
processing to extract clinical information from patient safety
reports showed a positive impact on patient safety outcomeswith accuracy of 0.53 [81]. Similarly, the FDA-approved
computer-aided diagnosis of the 1990s, which significantly
increased the recall rate of diagnosis, did not improve safety or
patient outcomes [111]. According to our review, AI algorithms
are rarely scrutinized against a standard of care (clinicians or
clinical gold standard). Relying on AI outcomes that have notbeen evaluated against a standard benchmark that meets clinicalrequirements can be misleading. A study conducted in 2008[112] developed and validated an advanced version of theQRISK cardiovascular disease risk algorithm (QRISK2). The
study reported improved performance of QRISK2 whencompared to its earlier version. However, QRISK2 was notcompared against any clinical gold standard. Eight years later,in 2016, The Medicines & Healthcare Products Regulatory
Agency identified an error in the QRISK 2 calculator [113];QRISK2 underestimated or overestimated the potential risk ofcardiovascular disease. The regulatory agency reported that athird of general practitioner surgeries in England might havebeen affected [113] due to the error in QRISK2. Globally, there
are several Standards Development Organizations developing
information technology and AI standards to address varying
standardization needs in the domain of cloud computing,cybersecurity, and the internet of things [114]. However, there
has been minimal effort to standardize AI in the field of healthcare. Health care comprises multiple departments, each having
unique or different requirements (clinical standards). Thus,
health care requires so-called “vertical standards,” which arestandards developed for specific application areas such as drug
safety (pharmaceuticals), specific surgeries, outpatients andinpatients with specific health concerns, and emergency
departments [114]. In contrast, standards that are not correctly
tailored for a specific purpose may hamper patient safety.
Without a standardized benchmark, it becomes challenging toevaluate whether a particular AI system meets clinicalrequirements (gold standard) or performs significantly better(improves patient safety) or worse (harms patient) than othersimilar systems in a given health care context. To generate thebest possible (highest) performance outcome, AI algorithms
may include unreliable confounders into the computing process.
For instance, in one study, an algorithm was more likely toclassify a skin lesion as malignant if an image (input data) had
a ruler in it because the presence of a ruler correlated with anincreased likelihood of a cancerous lesion [115]. The presenceof surgical skin markings has also been shown to falsely increasea deep-learning model’s melanoma probability scores and hence
the false-positive rate [116]. Moreover, there has been greatemphasis focused on the importance to standardization of AI
by developed countries such as the European Union, UnitedStates, China, and Japan. For instance, on February 11, 2019,http://medinform.jmir.org/2020/7/e18599/

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Choudhury & Asanthe President of the United States issued an Executive Order(EO 13859) [117] directing federal agencies to actively
participate in AI standards development. According to the Center
for Data Innovation and The National Institute of Standards andTechnology, a standardized AI benchmark can serve as amechanism to evaluate and compare AI systems [114]. FDA
Commissioner Scott Gottlieb acknowledged the importance ofAI standardization that can assure that ongoing algorithm
changes follow prespecified performance objectives and use avalidation process that ensures safety [118].
Another major finding of this review is high heterogeneity in
AI reporting. AI systems have been developed to help cliniciansin estimating risks and making informed decisions. However,
the evidence indicates that the quality of reporting of AI modelstudies is heterogeneous (not standard). Table 2 demonstrates
how different studies that implemented the same AI useddifferent evaluation metrics to measure its performance.
Heterogeneity in AI reporting also makes the comparison ofalgorithms across studies challenging and might cause
difficulties in obtaining consensus while attempting to selectthe best AI for a given situation. Algorithms not only need tobe subjected to comparison on the same data that are
representative of the target population but also the sameevaluation metrics; thus, standardized reporting of AI studies
would be beneficial. The current Transparent Reporting of amultivariable prediction model for Individual Prognosis Or
Diagnosis (TRIPOD) consists of 22-item checklists that aim to
improve the reporting of studies developing or validating a
prediction model [119,120]. Studies in our review did not use
TRIPOD to report findings. The possible reason behind this canbe the design of TRIPOD, which focuses on a regression-based
prediction model.
However, the explanation and elaboration document provides
examples of good reporting methods, which are focused onmodels developed using regression. Therefore, a new versionof the TRIPOD statement that is specific to AI/machine-learning
systems (TRIPOD-ML) is in development. It will focus on the
introduction of machine-learning prediction algorithms toestablish methodological and reporting standards formachine-learning studies in health care [121].
Our findings also identified the need to determine the importanceof an AI evaluation metric. In particular, it is important todetermine which evaluation metric(s) should be measured in agiven health care context. AUROC is considered to be a superior
metric for classification accuracy, particularly when unbalanced
datasets are used [122,123] because it is unaffected byunbalanced data, which is typical in health care. However, 36
studies in our review did not report AUROC. Evaluation
measures such as precision-recall can also reflect model
performance accurately [123]; however, only 11 studies in ourreview evaluated AI based on precision-recall. Using
inappropriate measures to evaluate AI performance might
impose a threat to patient safety. However, no threat to patient
safety due to the use of inappropriate AI evaluation metric was
identified in our review. Future studies should report theimportance of evaluation metrics and determine which measure(single or multiple measures) is more important and a better
representation of patient safety outcomes. More studies are
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JMIR MEDICAL INFORMATICS

Choudhury & Asanneeded to explore the evaluation metric(s) that should beconsidered before recommending an AI model.

unenriched representative sample data that are not available totrain algorithms.

The findings of our review demonstrate that drug safety,
followed by the analysis of clinical reports, has been the mostcommon area of interest for the use of AI to address
clinical-level patient safety concerns. The wrong medication or
improper dosage can result in fatal patient health outcomes andmedical malpractice [91]. Of all drug safety concerns, issues
related to inappropriate doses of high-alert medications are ofgreat interest to the Joint Commission on Accreditation of
Healthcare Organizations [91,124]. Medical errors are reported
as the third leading cause of death in the United States. The
majority of the papers in our review implemented AI to address
drug safety (n=23) concerns, which is one of the most significant
contributors to overall medical errors. These publications
improved patient safety by identifying adverse drug reactions
and preventing incorrect medications or overdoses. Futurestudies should further explore how to use AI systems on a larger
scale to diminish medication errors at hospitals and clinics tosave more lives.

Our review acknowledges that no single measure captures allof the desirable properties of a model, and multiple measures
are typically required to summarize model performance.
However, different measures are indicative of different typesof analytical performance. Future studies should develop astandard framework that can guide clinicians in interpreting theclinical meaning of AI’s evaluation metrics before integrating
it into the clinical workflow. Future studies should also report
a quantifiable measure of AI demonstrating not only its
analytical performance but also its impact on patient safety(long and short term), reliability, domain-specific risks, anduncertainty. Additionally, studies should also ensure data
standardization.

Finally, the studies reviewed in this paper have addressed safetyissues as identified by the Health Insurance Portability andAccountability Act (HIPAA) and the US Department of Health& Human Services (HHS). The HIPAA regulations identify
risk analysis as part of the administrative safeguard requirement
to improve patient safety. The HHS advocates analysis ofclinical notes to track, detect, and evaluate potential risks topatients. Many studies (n=21) in our review used AI to identify
patient risk from clinical notes. These studies used AI andclinical reports to extract safety-related information such as fallrisks, pyxis discrepancies, patient misidentification, patient
severity, and postoperative surgical complications. Our findings
exhibit how, with the help of AI techniques such as natural
language processing, clinical notes and reports have been used
as a data source to extract patient data regarding a broad rangeof safety issues, including clinical notes, discharge notes, andother issues [69,70,73,84]. Our review also indicates that AI
has the potential to provide valuable insights to treat patients
correctly by identifying future health or safety risks [125], to
improve health care quality, and reduce clinical errors [126].
Despite being recognized as one of the major factors responsible
for fatigue, burnout in clinicians, and patient harm [61,127-129],
only 9 studies in our review used AI to improve clinical alarms.
Although studies addressing clinical alarms reported positive
outcomes by minimizing false alarms and identifying patient
health deterioration, the limited number of studies (n= 9)
addressing these issues shows that the field is still in a nascentperiod of investigation. Thus, more research is needed to confirm
the impact of AI on patient safety outcomes.

Recommendations for Future Research
Future studies should work toward establishing a gold standard(for various health care contexts/ disease types/problem types)
against which AI performance can be measured. Future research,as suggested by Kelly and others in 2019 [119], should also
develop a common independent test (preferably for different
problem types, drug safety/clinical alarms/clinical reports) using

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Health databases or storage systems are often not compatible(integratable) across different hospitals, care providers, ordifferent departments in the same hospital. Data in health careare largely unorganized and unstructured [9,50]. Since theperformance of AI heavily depends on data, regulatory bodies
should invest in data infrastructure such as standardization of
EHRs and integration of different health databases. AI trainedon unstructured or biased data might generate misleading results[51]. According to the National Institute of Standards andTechnology (NIST), standardized data can make the training
data (machine learning input) more visible and usable toauthorized users. It can also ensure data quality and improve
AI performance.
Most of the safety initiatives implemented in health care overthe last decade have been focused on analyzing historical events
to learn and evolve [130,131]. The same was also observed inour review. AI models were trained on past data. However, in
health care, outcomes are satisfactory because providers makesensible and just-in-time adjustments according to the demandsof the situation. Future work should train AI on the criticaladjustments made by clinicians, so that AI can adapt to different
conditions in the same manner as clinicians.
The integration of AI systems into the health system will alter
the role of providers. Ideally, AI systems are expected to assist
providers in making faster and more accurate decisions and todeliver personalized patient care. However, lack of appropriate
knowledge of using complex AI systems and interpreting theiroutcome might impose a high cognitive workload on providers.
Thus, the medical education system should incorporate
necessary AI training for providers so that they can better
understand the basic functioning of AI systems and extract
clinically meaningful insight from the outcomes of AI.

Limitation of this Review
This study encompasses publications that matched our inclusion
criteria and operational definition of AI and patient safety. In
addition, we limited the scope of AI to only machine learningand natural language processing at a clinical level. This review
also only included studies published in English in the last 10
years.

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Conclusion
This systematic review identified critical research gaps thatneed attention from the scientific community. The majority ofthe studies in the review have not highlighted significant aspectsof AI, such as (a) heterogeneity in AI reporting, (b) lack of astandardized benchmark, and (c) need to determine theimportance of AI evaluation metric. The identified flaws of AI

Choudhury & Asansystems indicate that further research is needed, as well as theinvolvement of the FDA and NIST to develop a framework
standardizing AI evaluation measures and set a benchmark toensure patient safety. Thus, our review encourages the healthcare domain and AI developers to adopt an interdisciplinaryand systems approach to study the overall impact of AI on
patient safety outcomes and other contexts in health care.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist.
[DOC File , 65 KB-Multimedia Appendix 1]

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Abbreviations
AI: artificial intelligence
AUROC: area under the receiver operating characteristic curve
EHR: electronic health record
FDA: Food and Drug Administration
HHS: US Department of Health and Human Services
HIPAA: Health Insurance Portability and Accountability Act
MeSH: Medical Subject Headings
NIST: National Institute of Standards and Technology
PANDIT: Patient Assisting Net-Based Diabetes Insulin Titration
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analysis
TRIPOD: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis

Edited by C Lovis, G Eysenbach; submitted 06.03.20; peer-reviewed by E Chiou, A Anastasiou, S Pitoglou; comments to author
30.03.20; revised version received 26.05.20; accepted 13.06.20; published 24.07.20
Please cite as:
Choudhury A, Asan O
Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
JMIR Med Inform 2020;8(7):e18599
URL: http://medinform.jmir.org/2020/7/e18599/
doi: 10.2196/18599
PMID: 32706688©Avishek Choudhury, Onur Asan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 24.07.2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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JMIR Med Inform 2020 | vol. 8 | iss. 7 | e18599 | p. 30(page number not for citation purposes)