Anish K Agarwal1,2,3,4, Vivien Wong5,6, Arthur M Pelullo5,6, Sharath Guntuku5,6, Daniel Polsky7, David A Asch5,6,7,8,9, Jonathan Muruako5,6, Raina M Merchant10,5,6,7. 1. Department of Emergency Medicine at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Anish.Agarwal@uphs.upenn.edu. 2. Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA. Anish.Agarwal@uphs.upenn.edu. 3. Penn Medicine Center for Healthcare Innovation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Anish.Agarwal@uphs.upenn.edu. 4. Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA. Anish.Agarwal@uphs.upenn.edu. 5. Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA. 6. Penn Medicine Center for Healthcare Innovation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 7. Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA. 8. Division of General Internal Medicine at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 9. Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, USA. 10. Department of Emergency Medicine at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
BACKGROUND: Despite the importance of high-quality and patient-centered substance use disorder treatment, there are no standardized ratings of specialized drug treatment facilities and their services. Online platforms offer insights into potential drivers of high and low patient experience. OBJECTIVE: We sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings. DESIGN: This was a retrospective analysis of online ratings and reviews of specialized drug treatment facilities in Pennsylvania listed within the 2016 National Directory of Drug and Alcohol Abuse Treatment Facilities. Latent Dirichlet Allocation, a machine learning approach to narrative text, was used to identify themes within reviews. Differential Language Analysis was then used to measure correlations between themes and star ratings. SETTING: Online reviews of Pennsylvania's specialized drug treatment facilities posted to Google and Yelp (July 2010-August 2018). RESULTS: A total of 7823 online ratings were posted over 8 years. The distribution was bimodal (43% 5-star and 34% 1-star). The average weighted rating of a facility was 3.3 stars. Online themes correlated with 5-star ratings were the following: focus on recovery (r = 0.53), helpfulness of staff (r = 0.43), compassionate care (r = 0.37), experienced a life-changing moment (r = 0.32), and staff professionalism (r = 0.29). Themes correlated with a 1-star rating were waiting time (r = 0.41), poor accommodations (0.26), poor phone communication (r = 0.24), medications given (0.24), and appointment availability (r = 0.23). Themes derived from review content were similar to 9 of the 14 facility-level services highlighted by the Substance Abuse and Mental Health Services Administration's National Survey of Substance Abuse Treatment Services. CONCLUSIONS: Individuals are sharing their ratings and reviews of specialized drug treatment facilities on online platforms. Organically derived reviews of the patient experience, captured by online platforms, reveal potential drivers of high and low ratings. These represent additional areas of focus which can inform patient-centered quality metrics for specialized drug treatment facilities.
BACKGROUND: Despite the importance of high-quality and patient-centered substance use disorder treatment, there are no standardized ratings of specialized drug treatment facilities and their services. Online platforms offer insights into potential drivers of high and low patient experience. OBJECTIVE: We sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings. DESIGN: This was a retrospective analysis of online ratings and reviews of specialized drug treatment facilities in Pennsylvania listed within the 2016 National Directory of Drug and Alcohol Abuse Treatment Facilities. Latent Dirichlet Allocation, a machine learning approach to narrative text, was used to identify themes within reviews. Differential Language Analysis was then used to measure correlations between themes and star ratings. SETTING: Online reviews of Pennsylvania's specialized drug treatment facilities posted to Google and Yelp (July 2010-August 2018). RESULTS: A total of 7823 online ratings were posted over 8 years. The distribution was bimodal (43% 5-star and 34% 1-star). The average weighted rating of a facility was 3.3 stars. Online themes correlated with 5-star ratings were the following: focus on recovery (r = 0.53), helpfulness of staff (r = 0.43), compassionate care (r = 0.37), experienced a life-changing moment (r = 0.32), and staff professionalism (r = 0.29). Themes correlated with a 1-star rating were waiting time (r = 0.41), poor accommodations (0.26), poor phone communication (r = 0.24), medications given (0.24), and appointment availability (r = 0.23). Themes derived from review content were similar to 9 of the 14 facility-level services highlighted by the Substance Abuse and Mental Health Services Administration's National Survey of Substance Abuse Treatment Services. CONCLUSIONS: Individuals are sharing their ratings and reviews of specialized drug treatment facilities on online platforms. Organically derived reviews of the patient experience, captured by online platforms, reveal potential drivers of high and low ratings. These represent additional areas of focus which can inform patient-centered quality metrics for specialized drug treatment facilities.
Entities:
Keywords:
patient experience; social media; substance abuse; treatment centers
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