RESUMO
Machine learning has the potential to improve identification of patients for appropriate diagnostic testing and treatment, including those who have rare diseases for which effective treatments are available, such as acute hepatic porphyria (AHP). We trained a machine learning model on 205â571 complete electronic health records from a single medical center based on 30 known cases to identify 22 patients with classic symptoms of AHP that had neither been diagnosed nor tested for AHP. We offered urine porphobilinogen testing to these patients via their clinicians. Of the 7 who agreed to testing, none were positive for AHP. We explore the reasons for this and provide lessons learned for further work evaluating machine learning to detect AHP and other rare diseases.
RESUMO
Background: U.S. Preventive Services Task Force (USPSTF) recommendations for mammography screening, genetic counseling and testing for pathogenic BRCA1/2 mutations, and use of risk-reducing medications require assessment of breast cancer risk for clinical decision-making, but efficient methods for risk assessment in clinical practice are lacking. Materials and Methods: A cross-sectional study evaluating a web-based breast cancer risk assessment and decision aid (MammoScreen) was conducted in an academic general internal medicine clinic. All eligible women, 40-74 years of age without previous diagnosis of breast or ovarian cancer and who were enrolled in the Epic MyChart patient portal were invited. MammoScreen uptake and completion rates and consistency between breast cancer risk determined by MammoScreen and existing risk information in the Epic record were measured. Patient and physician experiences were summarized from interviews. Results: Of 448 invited participants, 339 (75.7%) read their MyChart invitation and 125 (36.9%) who read invitations enrolled in the study; 118 (94.4% of enrolled) completed MammoScreen. Twenty-one women were categorized as above-average risk from either MammoScreen data or the chart review and 7 (33.3%) were identified by both sources. Physicians and patients believed MammoScreen was easy to use and was helpful in identifying risks and facilitating shared decision-making. Conclusions: Breast cancer risk assessment and mammography screening decision support were efficiently implemented through a web-based tool for patients sent through an electronic patient portal. Integration of patient decision aids with risk algorithms in clinical practice may help support the implementation of USPSTF recommendations that include risk assessment and shared decision-making.
