Categorising patient concerns using natural language processing techniques

Fairie, Paul; Zhang, Zilong; D’Souza, Adam G.; Walsh, Tara; Quan, Hude; Santana, Maria

doi:10.1136/bmjhci-2020-100274

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…35 That said, the generalization of digital health records at the hospital level and the development of artificial intelligence algorithms applied to electronic medical records could create a pathway to automated clinical prediction models in the near future. 36,37…”

Section: Discussionmentioning

confidence: 99%

“…While this option remains possible, it would also require more detailed clinical information than is available in administrative claims-based data . That said, the generalization of digital health records at the hospital level and the development of artificial intelligence algorithms applied to electronic medical records could create a pathway to automated clinical prediction models in the near future …”

Section: Discussionmentioning

confidence: 99%

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Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

et al. 2022

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IMPORTANCE Rates of 30-day readmissions following hospitalization for pneumonia are used to publicly report on hospital performance and to set financial penalties for the worst-performing hospitals. However, the rate of avoidable readmission following hospitalization for pneumonia is undefined. OBJECTIVETo assess how often 30-day readmissions following hospitalization for communityacquired pneumonia (CAP) are avoidable. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed the results of an independent review of readmissions following hospitalization for CAP within 30 days among patients discharged from 2 large hospitals in France in 2014. Structured clinical records including clinical information (ie, baseline characteristics, physical examination, laboratory findings, x-ray or computed tomography scan findings, discharge plan, and treatments) for both index and readmission stays were independently reviewed by 4 certified board physicians. All consecutive adult patients hospitalized in 2014 with a diagnosis of CAP in our 2 eligible hospitals were eligible. All analyses presented were performed in March 2021. MAIN OUTCOMES AND MEASURESAvoidable readmission within 30 days of discharge from index hospitalization. The likelihood that a readmission was avoidable was quantified using latent class analysis based on the independent reviews. A readmission was considered avoidable if Bayes posterior probability exceeded 50%. RESULTSThe total analytical sample consisted of 1150 index hospital stays with a diagnosis of CAP, which included 651 (56.6%) male patients. The median (IQR) age for all patients was 77.8 (IQR, 62.7-86.4) years. Out of the 1150 index hospital stays, 98 patients (8.5%) died in hospital, and 108 (9.4%) unplanned readmissions were found. Overall, 15 readmissions had a posterior probability of avoidability exceeding 0.50 (13.9% of the 108 unplanned readmissions; 95% CI, 8.0%-21.9%). The median (IQR) delay between the hospital discharge index and readmission was considerably shorter when readmission was deemed avoidable (4 [6-21] days vs 12 [2-18] days; P = .02). CONCLUSIONS AND RELEVANCEOnly a small number of readmissions following hospitalization for CAP were deemed avoidable, comprising less than 10% of all readmissions. Shorter time interval between hospitalization discharge and readmission was associated with avoidability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

et al. 2022

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“…Many patient concerns were categorized into multiple topics. Some were more specific versions of categories from the existing framework (e.g., communication issues causing delays), while others were novel (e.g., smoking in inappropriate settings) [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

“…Percent agreement between the reviewers was measured, and disagreements were resolved during a consensus meeting. Recognizing that this is a very labour-intensive process, our team has explored the use of natural language processing to analyze patient concerns data [ 27 ]. This second method is quite advantageous as it can be done in real-time.…”

Section: Methodsmentioning

confidence: 99%

How to analyze and link patient experience surveys with administrative data to drive health service improvement -- examples from Alberta, Canada

Kemp

Fairie

Steele

et al. 2022

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The ability of hospitals and health systems to learn from those who use its services (i.e., patients and families) is crucial for quality improvement and the delivery of high-quality patient-centered care. To this end, many hospitals and health systems regularly collect survey data from patients and their families, and are engaged in activities to publicly report the results. Despite this, there has been limited research into the experiences of patients and families, and how to improve them. Since 2015, our research team has conducted a variety of studies which have explored patient experience survey data, in isolation, and in linkages with routinely-captured administrative data sets across Alberta; a Canadian province of 4.4 million residents. Via secondary analyses, these studies have shed light upon the drivers of inpatient experience, the specific aspects of care which are most correlated with one's overall experiences, and the association of elements of the patient experience with other measures, such as patient safety indicators and unplanned hospital readmissions. The aim of this paper is to provide an overview of the methods we have used, including further details about the data sets and linkage protocol. The main findings from these papers have been presented for readers and those who wish to conduct their own work in this area.

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“…Studies cite a range of critical themes in negative online reviews: discordant expectations (education, support, and promises) and sub-optimal communication and quality of care (management, organization, staff, and equipment) [ 12 , 60 , 61 ]. These were only partly reflected in our findings.…”

Section: Discussionmentioning

confidence: 99%

How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers

et al. 2023

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To reach large groups of vaccine recipients, several high-income countries introduced mass vaccination centers for COVID-19. Understanding user experiences of these novel structures can help optimize their design and increase patient satisfaction and vaccine uptake. This study drew on user online reviews of vaccination centers to assess user experience and identify its key determinants over time, by sentiment, and by interaction. Machine learning methods were used to analyze Google reviews of six COVID-19 mass vaccination centers in Berlin from December 2020 to December 2021. 3647 user online reviews were included in the analysis. Of these, 89% (3261/3647) were positive according to user rating (four to five of five stars). A total of 85% (2740/3647) of all reviews contained text. Topic modeling of the reviews containing text identified five optimally latent topics, and keyword extraction identified 47 salient keywords. The most important themes were organization, friendliness/responsiveness, and patient flow/wait time. Key interactions for users of vaccination centers included waiting, scheduling, transit, and the vaccination itself. Keywords connected to scheduling and efficiency, such as “appointment” and “wait”, were most prominent in negative reviews. Over time, the average rating score decreased from 4.7 to 4.1, and waiting and duration became more salient keywords. Overall, mass vaccination centers appear to be positively perceived, yet users became more critical over the one-year period of the pandemic vaccination campaign observed. The study shows that online reviews can provide real-time insights into newly set-up infrastructures, and policymakers should consider their use to monitor the population’s response over time.

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Categorising patient concerns using natural language processing techniques

Cited by 11 publications

References 43 publications

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

How to analyze and link patient experience surveys with administrative data to drive health service improvement -- examples from Alberta, Canada

How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers

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