A gap exists between the evidence for reducing risk of knee osteoarthritis (KOA) progression and its application in patients’ daily lives. We aimed to bridge this gap by identifying patient and family physician (FP) self-management priorities to conceptualize and develop a mobile-health application (m-health app). Our co-design approach combined priorities and concerns solicited from patients and FPs with evidence on risk of progression to design and develop a KOA self-management tool.

Parallel qualitative research of patient and FP perspectives was conducted to inform the co-design process. Researchers from the Enhancing Alberta Primary Care Research Networks (EnACT) evaluated the mental models of FPs using cognitive task analysis through structured interviews with four FPs. Using grounded theory methods, patient researchers from the Patient and Community Engagement Research (PaCER) program interviewed five patients to explore their perspectives about needs and interactions within primary care. In three co-design sessions relevant stakeholders (four patients, five FPs, and thirteen researchers) participated to: (i) identify user needs with regard to KOA self-management; and (ii) conceptualize and determine design priorities and functionalities of an m-health app using a modified nominal group process.

Priority measures for symptoms, activities, and quality of life from the user perspective were determined in the first two sessions. The third co-design session with our industry partner resulted in finalization of priorities through interactive patient and FP feedback. The top three features were: (i) a symptoms graph and summary; (ii) information and strategies; and (iii) setting goals. These features were used to inform the development of a minimum viable product.

The novel use of co-design created directive dialog around the needs of patients, highlighting the contrasting views that exist between patients and FPs and emphasizing how exploring these differences might lead to strong design options for patient-oriented m-health apps. Characterizing these disjunctions has important implications for operationalizing patient-centered health care.

Artificial intelligence (AI) and innovative technology offer opportunities for enhanced health care during the COVID-19 pandemic. Populations living in low-income countries do not have access to reverse transcription polymerase chain reaction (RT-PCR) testing for COVID-19 and, thus, depend on the scarce resources of their health system. In this context, an automated screening system for COVID-19 based on AI for a telemedicine platform could be directed towards alleviating the current lack of trained radiologists who can interpret computed tomography images at countryside hospitals.

This descriptive study was carried out in Paraguay by the Telemedicine Unit of the Ministry of Public Health and Social Welfare in collaboration with the Department of Biomedical Engineering and Imaging of the Health Science Research Institute and the University of the Basque Country. The utility of the screening system for COVID-19 was analyzed by dividing the results from two tailored AI systems implemented in 14 public hospitals into four likelihood levels for COVID-19.

Between March and October 2020, 911 COVID-19 diagnoses were performed in 14 regional hospitals (62.6% were men and 37.4% were women). The average age of the patients diagnosed with COVID-19 was 50.7 years; 59.1% were aged 19 to 59 years. The two AI systems used have different background information for detecting COVID-19. The most common findings were severe pneumonia and bilateral pneumonia with pleural effusions. The role of computed tomography was to find lesions and evaluate the effects of treatment. The sensitivity of AI for detecting COVID-19 was 93%.

AI technology could help in developing a screening system for COVID-19 and other respiratory pathologies. It could speed up medical imaging diagnosis at regional hospitals for patients with suspected infection during the COVID-19 pandemic and rationalize scarce RT-PCR and specialized human resources in low-income countries. These results must be contextualized with the local or regional epidemiological profile before widespread implementation.

To estimate the minimum percent change in failed extubation to make a tool designed to reduce extubation failure (Extubation Advisor [EA]) economically viable.

We conducted an early return on investment (ROI) analysis using data from intubated intensive care unit (ICU) patients at a large Canadian tertiary care hospital. We obtained input parameters from the hospital database and published literature. We ran generalized linear models to estimate the attributable length of stay, total hospital cost, and time to subsequent extubation attempt following failure. We developed a Markov model to estimate the expected ROI and performed probabilistic sensitivity analyses to assess the robustness of findings. Costs were presented in 2020 Canadian dollars (C$).

The model estimated a 1 percent reduction in failed extubation could save the hospital C$289 per intubated patient (95 percent CI: 197, 459). A large center seeing 2,500 intubated ICU patients per year could save C$723,124/year/percent reduction in failed extubation. At the current annual price of C$164,221, the EA tool must reduce extubation failure by at least 0.24 percent (95 percent CI: .14, .41) to make the tool cost-effective at our site.

Clinical decision-support tools like the EA may play an important role in reducing healthcare costs by reducing the rate of extubation failure, a costly event in the ICU.

Digital medication health service (DMHS) platforms are increasingly used by pharmaceutical companies to provide direct medication health services through digital methods like apps, hotlines, and web services, etcetera. However, the implications of such platforms in supporting health technology assessment (HTA) are rarely discussed in the literature. This presentation sets out the opportunities for using the DMHS platforms of pharmaceutical companies as real-world data sources for HTA.

A mixed-method qualitative study combining literature review and case study was conducted. Relevant literature was identified by searching the Web of Science and PubMed databases. A case study on current DMHS platforms in China was carried out using an inductive approach to identify the key elements emerging from these platforms.

DMHS platforms of pharmaceutical companies can identify multiple attributes of medication information needs regarding medical products, including effectiveness, safety, and economic factors. The platforms can respond to different stakeholders, including patients and their carers, doctors, nurses, pharmacists, etcetera. As one kind of interactive process, DMHS platforms can provide further services, including patient education, consultation, and evaluation, follow-up visits, chronic disease management, promotion of the rational use of drugs, therapeutic drug monitoring, and adverse drug reaction surveillance and reporting.

The DMHS platforms of pharmaceutical companies provide a unique and valuable real-world data source for HTA. These types of self-reported outcomes have not gained enough attention in HTA. Collective efforts by HTA agencies and pharmaceutical companies are needed to set strategies for integrating DMHS platforms into HTA.

Publication bias has the potential to adversely impact clinical decision making and patient health if alternative decisions would have been made had there been complete publication of evidence.

The objective of our analysis was to determine if earlier publication of the complete evidence on rosiglitazone’s risk of myocardial infarction (MI) would have changed clinical decision making at an earlier point in time. We tested several methods for adjustment of publication bias to assess the impact of potential time delays to identifying the MI effect. We then performed a cumulative meta-analysis (CMA) for both published studies (published-only data set) and all studies performed (comprehensive data set). We then created an adjusted data set using existing methods of adjustment for publication bias (Harbord regression, Peter’s regression, and the nonparametric trim and fill method) applied to the limited data set. Finally, we compared the time to the decision threshold for each data set using CMA.

Although published-only and comprehensive data sets did not provide notably different final summary estimates [OR = 1.4 (95 percent confidence interval [CI]: .95–2.05) and 1.42 (95 percent CI: 1.03–1.97)], the comprehensive data set reached the decision threshold 36 months earlier than the published-only data set. All three adjustment methods tested did not show a differential time to decision threshold versus the published-only data set.

Complete access to studies capturing MI risk for rosiglitazone would have led to the evidence reaching a clinically meaningful decision threshold 3 years earlier.

In a recently published review of supplementary search methods, we proposed that researchers could usefully record time taken to search and present outcome values in similar way to existing studies, to facilitate generalisability of outcomes, where appropriate. We also discuss the idea of linking literature search effectiveness to study value. In this vignette, we discuss which outcomes we believe are important to measure and why. We discuss this in the context of the review of supplementary search methods and using a recently submitted evaluation of contacting study authors for context.

In a recently completed systematic review, we contacted eighty-two study authors to ask three questions. We aimed to measure the following outcomes when contacting study authors: Effectiveness - determined as number of contacts compared to number of replies; Efficiency - i) time to make contact and ii) time between contact and reply. We determined this in hours, minutes and seconds, in line with other studies; Cost - determined by comparing the efficiency of contacting authors with the effectiveness; and Value - determined by reading and comparing the published studies with the replies received to see if any unique data were identified.

Effectiveness: thirty-eight answers were received from eighty-two possible contacts. Efficiency: In total, author contact took six hours, fifty-four minutes and twenty-five seconds across thirty-nine weeks. Replies were received across zero to thirty-nine days (median fourteen days). Cost: Cost for staff time was GBP 80.33 (EUR 91.20) or GBP 2.11 (EUR 2.40) per e-mail reply received. Value: We were able to identify value in author replies for each of the questions asked.

In a recently published review of supplementary search methods, and a linked evaluation of the effectiveness of contacting study authors, we suggest outcomes that should be measured to determine effectiveness of literature search methods. We conclude that measuring these outcomes demonstrate both effectiveness and value.

To assess convergent validity of stated preference methods in studies where they were used to elicit patient preferences for informing medical product decisions.

In four studies, two stated preference methods were used to elicit preferences of patients with neuromuscular diseases (NMD; n = 140, Discrete Choice Experiment [DCE] and Best-Worst Scaling [BWS] case 2), diabetes (n = 495, DCE and swing weighting [SW]), myocardial infarction (MI; n = 335, DCE and BWS case 1), and rheumatoid arthritis (RA; n = 982, DCE and probabilistic threshold technique [PTT]). In each study, results of the two methods were compared using a normalized preference measure for which confidence intervals (CIs) were estimated using nonparametric bootstrapping of 500 samples. Normalized preference measures comprised of mean relative attribute importance weights (NMD and diabetes studies), attribute uptake probability (MI study), or maximum acceptable risk (RA study).

In all four studies, attribute ranking showed similar patterns between DCE and other methods for the most important attributes. The same attribute had highest importance in three out of four studies. Significant differences were found in ranges of normalized preference measures of each study between DCE and the other methods: 4.1–43.4 versus 8.9–24.7 for DCE and BWS case 2 in NMD; 3.8–49.7 versus 11.9–16.8 for DCE and SW in diabetes; 2.0–85.5 versus 0.2–69.0 for DCE and BWS case 1 in MI; -3.5–49.2 versus 1.1–18.1 for DCE and PTT in RA.

Preferences differed significantly between DCE and other preference methods implying limited convergent validity. The substantially larger ranges in normalized outcome measures in DCE compared to other methods, are likely due to differences in mechanics and bias related to the methods. Since none of the methods is considered the golden standard for measuring stated preferences as true preferences are unknown, further studies are necessary to compare stated preference methods, determine internal validity and data quality, and potentially measure external validity.

The Scottish Medicines Consortium (SMC) provides advice on which new medicines should be accepted for routine use by the NHS in Scotland. To help increase the accessibility of the advice, SMC produces public information summaries, which have been published on the SMC website since 2018. We conducted an evaluation to investigate if the public summaries are achieving their purpose and subsequently help inform improvements from a user perspective. The objectives were to determine how the public summaries are being used; what users like and what could be improved; and if they have achieved a greater understanding of decisions.

The first stage of the evaluation involved surveying patient groups (organizations that represent the interests of patients, families and carers) to investigate how they use the public summaries. We then conducted workshops with patient groups and Public Partners (members of the public that volunteer with Healthcare Improvement Scotland) to gather perspectives on the content, language and layout of a selection of public summaries.

The survey responses (n = 14) illustrate that the public summaries are being used in a variety of ways. The majority (n = 10) of patient groups reported using the public summaries to help explain SMC decisions to the people they support.

The workshops highlighted that participants found the public summaries clear and helpful. In general, patient groups felt the level of detail and language used in the public summaries improved their understanding of SMC decisions compared to other sources of information, such as the press release or Detailed Advice Document.

There were a number of suggested improvements, including changing the layout (so the SMC decision appears first) and providing definitions for some technical terms. Where actionable, these recommendations have been implemented.

Working in partnership with patient groups and Public Partners has enabled SMC to further strengthen public summaries, and patient engagement more broadly. Improvements have ensured that SMC's decisions are communicated clearly, helping to increase accessibility.

Randomized controlled trials (RCTs) are considered the gold standard in the hierarchy of research designs for evaluating the efficacy and safety of a treatment intervention. The low external validity of RCTs and the general shortage of clinical evidence available to support the use of many medical devices have emphasized the necessity for exploring the use of real-world data (RWD) as a complementary source to RCTs data for establishing a more robust evidence base on the effectiveness of medical devices. The aim of the present project is to assess in a comprehensive way the existing sources of real world data on medical devices in Europe.

The guidelines to the mapping exercise have been outlined in a research protocol. First, all national relevant sources (e.g. website of Ministry of Health, national institutions, research bodies) are screened, both in local language and English. Second, we perform a systematic search on PubMed using a set of key words for each case study, adapted to each country setting. Finally, we seek advice from key actors in the field of the device and clinical conditions, such as manufacturers or clinicians.

Information on existing sources of RWD for each case studies are provided in a template including details on the key features of the source (e.g. data producer, data collection period, sample size, study design, geographical coverage) and the main content of the dataset, distinguishing socio-demographic information, clinical and epidemiological data, data on resource use and health outcomes. The data mapping includes all countries of the project participants, i.e. Italy, UK, Netherlands, Switzerland, Germany, Hungary, and we enlarge the scope of our mapping including other countries: Spain, France, Denmark, Finland, Sweden, Poland and Hungary as well as international databases at pan-EU level. The number of available sources of RWD and their quality vary depending on case study and across countries. For example, in the case of orthopaedics, many countries have a national registry and administrative data, such as hospital discharge, contain useful information, although not as detailed. When a registry is not available, it is often the case that more observational studies are available; this occurs for example in France.

In this work we shows the importance of RWE and map in an accurate and comprehensive way which source of RWD are currently available and to what extent they are known and used in medical, epidemiological and economic research.

Limited public money is available for funding research and the majority of clinical research undertaken is funded by industry. Mechanisms to regulate conflicts of interest within the research process have been implemented. However, these policies by themselves do not protect against potential sponsorship bias that would affect research results to inform decision makers when using the results of these trials. Therefore, the main aim of this study was to evaluate the influence of sponsorship bias on the treatment effects of RCTs.

This was a meta-epidemiological study. A random sample of RCTs included in meta-analyses of physical therapy (PT) area were identified. Data extraction including assessments of appropriate influence of funders was conducted independently by two reviewers. To determine the association between biases related to sponsorship biases and effect sizes, a two-level analysis was conducted using a meta-meta-analytic approach.

We analysed 393 trials included in forty-three meta-analyses. The most common sources of sponsorship for this sample of PT trials were government (n = 205, 52.16 percent) followed by academic (n = 44, 11.2 percent), and industry (n = 39, 10 percent). The funding was not declared in a high percentage of the trials (n = 85, 22 percent). The influence of the trial sponsor was assessed as being appropriate in 246 trials (63 percent) and considered inappropriate/unclear in 147 (37 percent) of them. There was a significant difference in effects estimates between trials with appropriate and inappropriate influence of funders (ES= 0.15; 95% CI -0.03, 0.33;). Trials with inappropriate/unclear influence of funders tended to have on average a larger effect size than those with appropriate influence of funding

Treatment effect size estimates were 0.15 larger in trials with lack of appropriate influence of funders. Systematic reviewers should perform sensitivity analyses based on appropriateness of influence of sponsorship in included trials.