Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial.

BACKGROUND: Tobacco smoking is an important risk factor for disease, but inaccurate smoking history data in the electronic medical record (EMR) limits the reach of lung cancer screening (LCS) and tobacco cessation interventions. Patient-generated health data is a novel approach to documenting smoking history; however, the comparative effectiveness of different approaches is unclear. OBJECTIVE: We designed a quality improvement intervention to evaluate the effectiveness of portal questionnaires compared to SMS text message-based surveys, to compare message frames, and to evaluate the completeness of patient-generated smoking histories. METHODS: We randomly assigned patients aged between 50 and 80 years with a history of tobacco use who identified English as a preferred language and have never undergone LCS to receive an EMR portal questionnaire or a text survey. The portal questionnaire used a "helpfulness" message, while the text survey tested frame types informed by behavior economics ("gain," "loss," and "helpfulness") and nudge messaging. The primary outcome was the response rate for each modality and framing type. Completeness and consistency with documented structured smoking data were also evaluated. RESULTS: Participants were more likely to respond to the text survey (191/1000, 19.1%) compared to the portal questionnaire (35/504, 6.9%). Across all text survey rounds, patients were less responsive to the "helpfulness" frame compared with the "gain" frame (odds ratio [OR] 0.29, 95% CI 0.09-0.91; P<.05) and "loss" frame (OR 0.32, 95% CI 11.8-99.4; P<.05). Compared to the structured data in the EMR, the patient-generated data were significantly more likely to be complete enough to determine LCS eligibility both compared to the portal questionnaire (OR 34.2, 95% CI 3.8-11.1; P<.05) and to the text survey (OR 6.8, 95% CI 3.8-11.1; P<.05). CONCLUSIONS: We found that an approach using patient-generated data is a feasible way to engage patients and collect complete smoking histories. Patients are likely to respond to a text survey using "gain" or "loss" framing to report detailed smoking histories. Optimizing an SMS text message approach to collect medical information has implications for preventative and follow-up clinical care beyond smoking histories, LCS, and smoking cessation therapy.

Personalized hypertension treatment recommendations by a data-driven model.

BACKGROUND: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS: The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION: Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.

A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.

BACKGROUND: Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due to the lack of tools for the management of such findings and the time required to maintain up-to-date lists. Natural language processing (NLP) is capable of extracting information from free-text clinical documents and could provide the basis for software solutions that do not require changes to clinical workflows. OBJECTIVES: In this manuscript we present (1) a machine learning algorithm we trained to identify radiology reports documenting the presence of a newly discovered adrenal incidentaloma, and (2) the web application and results database we developed to manage these clinical findings. METHODS: We manually annotated a training corpus of 4,090 radiology reports from across our institution with a binary label indicating whether or not a report contains a newly discovered adrenal incidentaloma. We trained a convolutional neural network to perform this text classification task. Over the NLP backbone we built a web application that allows users to coordinate clinical management of adrenal incidentalomas in real time. RESULTS: The annotated dataset included 404 positive (9.9%) and 3,686 (90.1%) negative reports. Our model achieved a sensitivity of 92.9% (95% confidence interval: 80.9-97.5%), a positive predictive value of 83.0% (69.9-91.1)%, a specificity of 97.8% (95.8-98.9)%, and an F1 score of 87.6%. We developed a front-end web application based on the model's output. CONCLUSION: Developing an NLP-enabled custom web application for tracking and management of high-risk adrenal incidentalomas is feasible in a resource constrained, safety net hospital. Such applications can be used by an institution's quality department or its primary care providers and can easily be generalized to other types of clinical findings.

Novel tools for a learning health system: a combined difference-in-difference/regression discontinuity approach to evaluate effectiveness of a readmission reduction initiative.

Current methods used to evaluate the effects of healthcare improvement efforts have limitations. Designs with strong causal inference-such as individual patient or cluster randomisation-can be inappropriate and infeasible to use in single-centre settings. Simpler designs-such as prepost studies-are unable to infer causal relationships between improvement interventions and outcomes of interest, often leading to spurious conclusions regarding programme success. Other designs, such as regression discontinuity or difference-in-difference (DD) approaches alone, require multiple assumptions that are often unable to be met in real world improvement settings. We present a case study of a novel design in improvement and implementation research-a hybrid regression discontinuity/DD design-that leverages risk-targeted improvement interventions within a hospital readmission reduction programme. We demonstrate how the hybrid regression discontinuity-DD approach addresses many of the limitations of either method alone, and represents a useful method to evaluate the effects of multiple, simultaneous heath system improvement activities-a necessary capacity of a learning health system. Finally, we discuss some of the limitations of the hybrid regression discontinuity-DD approach, including the need to assign patients to interventions based upon a continuous measure, the need for large sample sizes, and potential susceptibility of risk-based intervention assignment to gaming.

Membrane indentation triggers clathrin lattice reorganization and fluidization.

Clathrin-mediated endocytosis involves the coordinated assembly of clathrin cages around membrane indentations, necessitating fluid-like reorganization followed by solid-like stabilization. This apparent duality in clathrin's in vivo behavior provides some indication that the physical interactions between clathrin triskelia and the membrane effect a local response that triggers fluid-solid transformations within the clathrin lattice. We develop a computational model to study the response of clathrin protein lattices to spherical deformations of the underlying flexible membrane. These deformations are similar to the shapes assumed during intracellular trafficking of nanoparticles. Through Monte Carlo simulations of clathrin-on-membrane systems, we observe that these membrane indentations give rise to a greater than normal defect density within the overlaid clathrin lattice. In many cases, the bulk surrounding lattice remains in a crystalline phase, and the extra defects are localized to the regions of large curvature. This can be explained by the fact that the in-plane elastic stress in the clathrin lattice are reduced by coupling defects to highly curved regions. The presence of defects brought about by indentation can result in the fluidization of a lattice that would otherwise be crystalline, resulting in an indentation-driven, defect-mediated phase transition. Altering subunit elasticity or membrane properties is shown to drive a similar transition, and we present phase diagrams that map out the combined effects of these parameters on clathrin lattice properties.

Membrane fluctuations destabilize clathrin protein lattice order.

We develop a theoretical model of a clathrin protein lattice on a flexible cell membrane. The clathrin subunit is modeled as a three-legged pinwheel with elastic deformation modes and intersubunit binding interactions. The pinwheels are constrained to lie on the surface of an elastic sheet that opposes bending deformation and is subjected to tension. Through Monte Carlo simulations, we predict the equilibrium phase behavior of clathrin lattices at various levels of tension. High membrane tensions, which correspond to suppressed membrane fluctuations, tend to stabilize large, flat crystalline structures similar to plaques that have been observed in vivo on cell membranes that are adhered to rigid surfaces. Low tensions, on the other hand, give rise to disordered, defect-ridden lattices that behave in a fluidlike manner. The principles of two-dimensional melting theory are applied to our model system to further clarify how high tensions can stabilize crystalline order on flexible membranes. These results demonstrate the importance of environmental physical cues in dictating the collective behavior of self-assembled protein structures.