Targeted Electronic Patient Portal Messaging Increases Hepatitis C Virus Screening in Primary Care: a Randomized Study.

IMPORTANCE: Electronic health record (EHR) tools such as direct-to-patient messaging and automated lab orders are effective at improving uptake of preventive health measures. It is unknown if patient engagement in primary care impacts efficacy of such messaging. OBJECTIVE: To determine whether more engaged patients, defined as those who have an upcoming visit scheduled, are more likely to respond to a direct-to-patient message with an automated lab order for hepatitis C virus (HCV) screening. DESIGN: Randomized trial PARTICIPANTS: One thousand six hundred randomly selected Stanford Primary Care patients, 800 with an upcoming visit within 6 months and 800 without, born between 1945 and 1965 who were due for HCV screening. Each group was randomly divided into cohorts of 400 subjects each. Subjects were followed for 1 year. INTERVENTION: One 400 subject cohort in each group received a direct-to-patient message through the EHR portal with HCV antibody lab order. MAIN OUTCOME AND MEASURE: The EHR was queried on a monthly basis for 6 months after the intervention to monitor which subjects completed HCV screening. For any subjects screened positive for HCV, follow-up through the cascade of HCV care was monitored, and if needed, scheduled by the study team. KEY RESULTS: Of 1600 subjects, 538 (34%) completed HCV screening. In the stratum without an upcoming appointment, 18% in the control group completed screening compared to 26% in intervention group (p<0.01). Similarly, in the stratum with an upcoming appointment, 34% in the control group completed screening compared to 58% in the intervention group (p<0.01). CONCLUSION: Direct-to-patient messaging coupled with automated lab orders improved HCV screening rates compared to standard of care, particularly in more engaged patients. Including this intervention in primary care can maximize screening with each visit, which is particularly valuable in times when physical throughput in the healthcare system may be low.

Improving palliative care with deep learning.

BACKGROUND: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life. METHODS: In this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3-12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care. RESULTS: The EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model's predictions. CONCLUSION: The automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.

Radiographic assessment of the sinuses in patients treated for nasopharyngeal carcinoma.

INTRODUCTION: Patients undergoing therapy for nasopharyngeal carcinoma (NPC) often experience dysfunction of the sinonasal mucosa as a side effect of radiotherapy and chemotherapy. Sinonasal mucosal changes may vary throughout the treatment and posttreatment periods, but little objective data exist characterizing such changes. We evaluated serial radiologic changes of the paranasal sinus mucosa in patients with NPC undergoing treatment. METHODS: Medical and radiographic records were reviewed for all patients treated for NPC between 2004 and 2006 at Stanford University Medical Center. Pretreatment computed tomography (CT) images served as the baseline images for comparison, and posttreatment CT and magnetic resonance imaging (MRI) images were categorized temporally into 3-month intervals, up to 25 months after initiation of treatment. Images were scored in a blinded fashion using the Lund-Mackay (LM) staging system. RESULTS: Thirty-five patients received treatment for NPC during the study period, of whom 27 had adequate data for analysis and inclusion in the study. The mean pretreatment LM score was 1.41, and a statistically significant increase in LM score was observed at 3, 6, 9, 12, 15, and 18, 22, and 28 months. There was continued progression of radiologic sinus opacification over the first 30 months after treatment. CONCLUSIONS: The treatment of NPC with radiotherapy and chemotherapy is associated with radiologic evidence of sinus mucosal thickening. The extent of mucosal thickening can be expected to progress after treatment for up to 30 months. Patients undergoing treatment for NPC should be monitored carefully throughout the posttreatment period for clinical manifestations of dysfunctional sinonasal mucosa.