Content Analysis of Negative Online Reviews of Hospice Agencies in the United States

Brereton, Elinor; Matlock, Daniel D.; Fitzgerald, Monica D.; Venechuk, Grace; Knoepke, Chris; Allen, Larry A.; Tate, Channing E.

doi:10.1001/jamanetworkopen.2019.21130

Cited by 11 publications

(11 citation statements)

References 22 publications

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“…30 Online hospice reviewers report marked differences in communication between for-profit and not-for-profit hospices, with reviewers reporting higher rates of "promises made but not kept" and poorer responsiveness to telephone inquiries at for-profit hospices than at not-forprofit hospices. 30 Finally, not-for-profit hospices are more likely than for-profit hospices to have open-access policies that allow patients to receive treatments, such as blood transfusions and palliative radiation therapy, that address symptoms or improve the quality of life but are usually not provided by hospices due to their cost; not-for-profit hospices have more flexibility to offer these unreimbursed services than forprofit hospices, which must be attentive to their profit margins. 31 While a greater proportion of for-profit than not-forprofit hospices perform substantially worse than the national hospice average, it is important to note that some for-profit hospices (between 15.8% and 31.1% across measures) perform substantially better than the national average.…”

Section: Discussionmentioning

confidence: 99%

“…For example, employees of not-for-profit hospices rate their hospices as better working environments than employees of for-profit hospices; work environment ratings may reflect differences in organizational culture, compensation, and workload, among other considerations. In online reviews of hospices, families frequently mention characteristics of hospice staff, such as professionalism, knowledge, and skill, which vary across hospices, and express concerns when staff seem overworked, the hospice is understaffed, or there are high rates of staff turnover . Online hospice reviewers report marked differences in communication between for-profit and not-for-profit hospices, with reviewers reporting higher rates of “promises made but not kept” and poorer responsiveness to telephone inquiries at for-profit hospices than at not-for-profit hospices .…”

Section: Discussionmentioning

confidence: 99%

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Association of Hospice Profit Status With Family Caregivers’ Reported Care Experiences

et al. 2023

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ImportanceExpansive growth in the US hospice market has been driven almost exclusively by an increase in for-profit hospices. Prior research found that, in contrast to not-for-profit hospices, for-profit hospices focus on delivering care to patients in nursing homes, provide fewer nursing visits, and use less skilled staff. However, prior studies have not reported on the associations of these differences in care patterns with hospice care quality. Patient- and family-centeredness is a core element of hospice care quality that is measured through surveys of care experiences.ObjectiveTo examine whether differences in profit status are associated with family caregivers’ reports of hospice care experiences and assess factors that may be associated with observed differences in care experiences by profit status.Design, Setting, and ParticipantsConsumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey data from 653 208 caregiver respondents, reflecting care received from 3107 hospices between April 2017 and March 2019, were used for a cross-sectional examination of hospice care experiences by profit status. Data analysis was performed from January 2020 to November 2022.Main Outcomes and MeasuresOutcomes were case-mix–adjusted and mode-adjusted top-box scores for 8 measures of hospice care experiences, including communication, timely care, symptom management, and emotional and religious support, as well as a summary score averaging across measures. Linear regression examined the association between profit status and hospice-level scores, adjusting for other organizational and structural hospice characteristics.ResultsThere were 906 not-for-profit and 1761 for-profit hospices with mean (SD) time in operation of 25.7 (7.8) years and 13.8 (8.0) years, respectively. Mean (SD) decedent age at death was 82.8 (2.3) years, similar for not-for-profit and for-profit hospices. The mean proportion of patients who were Black, Hispanic, and White was 4.9%, 0.9%, and 91.4% for not-for-profit hospices and 9.0%, 2.2%, and 85.4% for for-profit hospices, respectively. Family caregivers reported worse care experiences at for-profit hospices than at not-for-profit hospices for all measures. Significant differences in average hospice performance by profit status remained after adjusting for hospice characteristics. However, for-profit hospice performance varied, with 548 of 1761 (31.1%) for-profit hospices scoring 3 or more points below the national hospice average of overall performance and 386 of 1761 (21.9%) scoring 3 or more points above the average. In contrast, only 113 of 906 (12.5%) not-for-profit hospices scored 3 or more points below the average, and 305 of 906 (33.7%) scored 3 or more points above the average.Conclusions and RelevanceIn this cross-sectional study of CAHPS Hospice Survey data, caregivers of patients receiving hospice care reported substantially worse care experiences in for-profit than in not-for-profit hospices; however, there was variation in reported experiences among both types of hospices. Public reporting of hospice quality is important.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association of Hospice Profit Status With Family Caregivers’ Reported Care Experiences

et al. 2023

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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%

“…Patient online expressions (e.g., via tweets or review platforms such as Google) are a readily available resource for user feedback. Their availability has grown exponentially in the past decade [ 11 ] and they have proven to provide valuable insights into patient experience and satisfaction [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] and also towards vaccines and vaccinations, in the context of measles or more recently COVID-19 [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Vaccination experiences or temporary pandemic response structures within or outside of the context of COVID-19, however, have not yet been studied using patient online expressions.…”

Section: Introductionmentioning

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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“…We focused on labeling the severity of the complaints, whereas these studies focused on patient opinions and sentiments using fine-grained doctor visit–related topics in the online reviews. An interesting path for future work would be to extend the current severity label to a more fine-grained severity label that combines severity with the type of complaint to create an automatic method similar to that established by Brereton et al [ 31 ] who performed a manual qualitative analysis of negative reviews to identify the most frequent actionable criticisms in patient reviews of hospices.…”

Section: Discussionmentioning

confidence: 99%

Using Text Mining Techniques to Identify Health Care Providers With Patient Safety Problems: Exploratory Study

Hendrickx¹,

Voets²,

Dyk³

et al. 2021

J Med Internet Res

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Background Regulatory bodies such as health care inspectorates can identify potential patient safety problems in health care providers by analyzing patient complaints. However, it is challenging to analyze the large number of complaints. Text mining techniques may help identify signals of problems with patient safety at health care providers. Objective The aim of this study was to explore whether employing text mining techniques on patient complaint databases can help identify potential problems with patient safety at health care providers and automatically predict the severity of patient complaints. Methods We performed an exploratory study on the complaints database of the Dutch Health and Youth Care Inspectorate with more than 22,000 written complaints. Severe complaints are defined as those cases where the inspectorate contact point experts deemed it worthy of a triage by the inspectorate, or complaints that led to direct action by the inspectorate. We investigated a range of supervised machine learning techniques to assign a severity label to complaints that can be used to prioritize which incoming complaints need the most attention. We studied several features based on the complaints’ written content, including sentiment analysis, to decide which were helpful for severity prediction. Finally, we showcased how we could combine these severity predictions and automatic keyword analysis on the complaints database and listed health care providers and their organization-specific complaints to determine the average severity of complaints per organization. Results A straightforward text classification approach using a bag-of-words feature representation worked best for the severity prediction of complaints. We obtained an accuracy of 87%-93% (2658-2990 of 3319 complaints) on the held-out test set and an F1 score of 45%-51% on the severe complaints. The skewed class distribution led to only reasonable recall (47%-54%) and precision (44%-49%) scores. The use of sentiment analysis for severity prediction was not helpful. By combining the predicted severity outcomes with an automatic keyword analysis, we identified several health care providers that could have patient safety problems. Conclusions Text mining techniques for analyzing complaints by civilians can support inspectorates. They can automatically predict the severity of the complaints, or they can be used for keyword analysis. This can help the inspectorate detect potential patient safety problems, or support prioritizing follow-up supervision activities by sorting complaints based on the severity per organization or per sector.

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Content Analysis of Negative Online Reviews of Hospice Agencies in the United States

Cited by 11 publications

References 22 publications

Association of Hospice Profit Status With Family Caregivers’ Reported Care Experiences

Association of Hospice Profit Status With Family Caregivers’ Reported Care Experiences

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

Using Text Mining Techniques to Identify Health Care Providers With Patient Safety Problems: Exploratory Study

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