Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics.

BACKGROUND: There is a lack of research studying patient-generated data on Reddit, one of the world's most popular forums with active users interested in dermatology. Techniques within natural language processing, a field of artificial intelligence, can analyze large amounts of text information and extract insights. OBJECTIVE: To apply natural language processing to Reddit comments about dermatology topics to assess for feasibility and potential for insights and engagement. METHODS: A software pipeline preprocessed Reddit comments from 2005 to 2017 from 7 popular dermatology-related subforums on Reddit, applied latent Dirichlet allocation, and used spectral clustering to establish cohesive themes and the frequency of word representation and grouped terms within these topics. RESULTS: We created a corpus of 176,000 comments and identified trends in patient engagement in spaces such as eczema and acne, among others, with a focus on homeopathic treatments and isotretinoin. LIMITATIONS: Latent Dirichlet allocation is an unsupervised model, meaning there is no ground truth to which the model output can be compared. However, because these forums are anonymous, there seems little incentive for patients to be dishonest. CONCLUSIONS: Reddit data has viability and utility for dermatologic research and engagement with the public, especially for common dermatology topics such as tanning, acne, and psoriasis.

Engaging Housestaff as Informatics Collaborators: Educational and Operational Opportunities.

BACKGROUND: In academic hospitals, housestaff (interns, residents, and fellows) are a core user group of clinical information technology (IT) systems, yet are often relegated to being recipients of change, rather than active partners in system improvement. These information systems are an integral part of health care delivery and formal efforts to involve and educate housestaff are nascent. OBJECTIVE: This article develops a sustainable forum for effective engagement of housestaff in hospital informatics initiatives and creates opportunities for professional development. METHODS: A housestaff-led IT council was created within an academic medical center and integrated with informatics and graduate medical education leadership. The Council was designed to provide a venue for hands-on clinical informatics educational experiences to housestaff across all specialties. RESULTS: In the first year, five housestaff co-chairs and 50 members were recruited. More than 15 projects were completed with substantial improvements made to clinical systems impacting more than 1,300 housestaff and with touchpoints to nearly 3,000 staff members. Council leadership was integrally involved in hospital governance committees and became the go-to source for housestaff input on informatics efforts. Positive experiences informed members' career development toward informatics roles. Key lessons learned in building for success are discussed. CONCLUSION: The council model has effectively engaged housestaff as learners, local champions, and key informatics collaborators, with positive impact for the participating members and the institution. Requiring few resources for implementation, the model should be replicable at other institutions.

Observations in lung cancer over multiple decades: an analysis of outcomes and cost at a single high-volume institution.

OBJECTIVES: This study reviews survival outcomes and cost of lung cancer care over multiple decades at a single high-volume institution. METHODS: All patients with a diagnosis of lung cancer were analysed at a single institution from 1959 to 2010. Data were extracted from a tumour registry, which was linked to a longitudinal medical record, clinical data repository and social security master death index. In-depth survival analyses by stage were performed using Kaplan-Meier methods from 1981 to 2010. The analysis contains hospital billing data on 1025 lung cancer patients from 2004 to 2010. RESULTS: A total of 17 025 patients with lung cancer were identified over the study period. The 1-year, 5-year and 10-year all-cause mortality rates were 41, 78 and 87%, respectively. Non-small-cell lung cancer comprised 73% (n = 12 361) of cases where the median survival = 2.5 years and the population was 94% Caucasian. Lung cancer was most prevalent between ages 60-79 years of life. Female gender and adenocarcinoma were increasingly more prevalent over the decades. The 5-, 10- and 15-year survival for non-small-cell lung cancer (NSCLC) patients were 27, 15 and 5%, respectively. Death rates measured at 1 year after diagnosis were reduced; however, 5-year survival over each subsequent decade did not significantly change. In patients where the full scope of cost data were available, the median cost/patient with any stage NSCLC = $40 500, where 63% of the cost is expended in the first year after diagnosis. The average length of treatment for NSCLC was 20.2 months. The greatest single category of expense was chemotherapy (31%), followed by surgery (24%), inpatient medical (17%), radiation therapy (12%) and diagnostics (5%). For surgically treated patients, Stage II-IV costs were roughly twice those of Stage I. CONCLUSIONS: There has been no evident improvement over the past 3 decades in 5-year survival (∼27%) in patients diagnosed with NSCLC at a single high-volume institution. Improvement in 1-year survival is thought to be attributed to improvements in diagnosing lung cancer earlier. Most of the healthcare expenditure for lung cancer is incurred during the first year after diagnosis despite stage.