Research from JAMA Internal Medicine — Racial Differences in Trust in Health Care Providers

BACKGROUND: Mistrust of healthcare providers and systems is a significant barrier to quality healthcare. However, limited empirical data are available on perceptions of medical mistrust among individuals who are diagnosed with cancer. The objective of this study was to identify sociodemographic, clinical, and cultural determinants of mistrust among men diagnosed with prostate cancer.METHODS: The authors conducted an observational study among 196 African‐American men (n = 71) and white men (n = 125) who were newly diagnosed with prostate cancer during 2003 through 2007.RESULTS: Race, education, healthcare experiences, and cultural factors had significant effects on mistrust. African‐American men (P = .01) and men who had fewer years of formal education (P = .001) reported significantly greater levels of mistrust compared with white men and men who had more education. Mistrust also was greater among men who had been seeing their healthcare provider for a longer period (P = .01) and among men with lower perceptions of interdependence (P = .01).CONCLUSIONS: The current findings suggested that efforts to enhance trust among men who are diagnosed with prostate cancer should target African‐American men, men with fewer socioeconomic resources, and men with lower perceptions of interdependence. Reasons for deterioration in trust associated with greater experience with specialty providers should be explored along with the effects of interventions that are designed to address the concerns of individuals who have greater mistrust. Cancer 2009. © 2009 American Cancer Society.

Hereditary nonpolyposis colon cancer (HNPCC) is the most common autosomal dominantly inherited cancer syndrome that predisposes to colorectal cancer (CRC).^1,2 Estimates of its frequency range from 2% to as much as 7% of the total CRC burden.^2-5 Colorectal cancer occurs at an early age (mean age, about 44 years), with right-sided predominance (approximately 70% of tumors are proximal to the splenic flexure)^1,2,6 and a significant occurrence of synchronous and metachronous lesions.^2,7 There is an excess of extracolonic cancers, the most common of which is carcinoma of the endometrium, followed by cancers of the ovary, stomach (particularly in Asian countries such as Japan and Korea), small bowel, pancreas, hepatobiliary tract, and brain, and transitional cell carcinoma of the upper uroepithelial tract.^8,9 Some families also have phenotypic manifestations such as sebaceous adenomas or sebaceous carcinomas consistent with the Muir-Torre syndrome.^2,10-16 Profuse colonic adenomas consonant with classic familial adenomatous polyposis and its attenuated variant must be excluded in the medical evaluation of patients at high risk for HNPCC.^1,2

Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives. We used data from the Medical University of South Carolina (MUSC) Research Data Warehouse. Patients 18 years-of-age or older who were diagnosed with prostate cancer between January 1, 2014 and May 31, 2017 were eligible for this study. NLP pipelines identifying social isolation were developed via extraction of notes on progress, history and physical, consult, emergency department provider, telephone encounter, discharge summary, plan of care, and radiation oncology. Of 4195 eligible prostate cancer patients, we randomly sampled 3138 patients (75%) as a training dataset. The remaining 1057 patients (25%) were used as a test dataset to evaluate NLP algorithm performance. Standard performance measures for the NLP algorithm, including precision, recall, and F-measure, were assessed by expert manual review using the test dataset. A total of 55,516 clinical notes from 3138 patients were included to develop the lexicon and NLP pipelines for social isolation. Of those, 35 unique patients (1.2%) had social isolation mention(s) in 217 notes. Among 24 terms relevant to social isolation, the most prevalent were “lack of social support,” “lonely,” “social isolation,” “no friends,” and “loneliness”. Among 1057 patients in the test dataset, 17 patients (1.6%) were identified as having social isolation mention(s) in 40 clinical notes. Manual review identified four false positive mentions of social isolation and one false negatives in 154 notes from randomly selected 52 controls. The NLP pipeline demonstrated 90% precision, 97% recall, and 93% F-measure. The major reasons for a false positive included the ambiguities of the experiencer of social isolation, negation, and alternate meaning of words. Our NLP algorithms demonstrate a highly accurate approach to identify social isolation.