Digital Drugs: an anatomy of new medicines

Medicines are digitalized as aspects of their regulation and use are embodied in or draw from interlinked computerized systems and databases. This paper considers how this development changes the delivery of health care, the pharma industry, and regulatory and professional structures, as it reconfigures the material character of drugs themselves. It draws on the concept of assemblage in presenting a theory-based analysis that explores digital drugs’ ontological status including how they embody benefit and value. The paper addresses three interconnected domains – that of use of drugs (practice), of research (epistemology) and of regulation (structures)

Patient free text reporting of symptomatic adverse events in cancer clinical research using the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Objective: The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies. Materials and Methods: Patients in 3 multicenter trials electronically completed surveys during cancer treatment. Each survey included a prespecified subset of items from the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Upon completion of the survey items, patients could add supplemental symptomatic AE information in a free text box. As patients typed into the box, structured dropdown terms could be selected from the PRO-CTCAE item library or Medical Dictionary for Regulatory Activities (MedDRA), or patients could type unstructured free text for submission. Results: Data were pooled from 1760 participants (48% women; 78% White) who completed 8892 surveys, of which 2387 (26.8%) included supplemental symptomatic AE information. Overall, 1024 (58%) patients entered supplemental information at least once, with an average of 2.3 per patient per study. This encompassed 1474 of 8892 (16.6%) dropdowns and 913 of 8892 (10.3%) unstructured free text entries. One-third of the unstructured free text entries (32%) could be mapped post hoc to a PRO-CTCAE term and 68% to a MedDRA term. Discussion: Participants frequently added supplemental information beyond study-specific survey items. Almost half selected a structured dropdown term, although many opted to submit unstructured free text entries. Most free text entries could be mapped post hoc to PRO-CTCAE or MedDRA terms, suggesting opportunities to enhance the system to perform real-time mapping for AE reporting. Conclusions: Patient reporting of symptomatic AEs using a text box functionality with mapping to existing terminologies is both feasible and informative

Comparison of consumers’ views on electronic data sharing for healthcare and research

New models of healthcare delivery such as accountable care organizations and patient-centered medical homes seek to improve quality, access, and cost. They rely on a robust, secure technology infrastructure provided by health information exchanges (HIEs) and distributed research networks and the willingness of patients to share their data. There are few large, in-depth studies of US consumers’ views on privacy, security, and consent in electronic data sharing for healthcare and research together. Objective This paper addresses this gap, reporting on a survey which asks about California consumers’ views of data sharing for healthcare and research together. Materials and Methods The survey conducted was a representative, random-digit dial telephone survey of 800 Californians, performed in Spanish and English. Results There is a great deal of concern that HIEs will worsen privacy (40.3%) and security (42.5%). Consumers are in favor of electronic data sharing but elements of transparency are important: individual control, who has access, and the purpose for use of data. Respondents were more likely to agree to share deidentified information for research than to share identified information for healthcare (76.2% vs 57.3%, p < .001). Discussion While consumers show willingness to share health information electronically, they value individual control and privacy. Responsiveness to these needs, rather than mere reliance on Health Insurance Portability and Accountability Act (HIPAA), may improve support of data networks. Conclusion Responsiveness to the public’s concerns regarding their health information is a pre-requisite for patient-centeredness. This is one of the first in-depth studies of attitudes about electronic data sharing that compares attitudes of the same individual towards healthcare and research

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BackgroundThe Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources.MethodsDEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes.ResultsIn the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8).ConclusionMost C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs