Data driven or data informed? How general practitioners use data to evaluate their own and colleagues' clinical work in clusters.

In contemporary policy discourses, data are presented as key assets for improving health-care quality: policymakers want health care to become 'data driven'. In this article, we focus on a particular example of this ambition, namely a new Danish national quality development program for general practitioners (GPs) where doctors are placed in so-called 'clusters'. In these clusters, GPs are obliged to assess their own and colleagues' clinical quality with data derived from their own clinics-using comparisons, averages and benchmarks. Based on semi-structured interviews with Danish GPs and drawing on Science and Technology Studies, we explore how GPs understand these data, and what makes them trust-or question-a data analysis. The GPs describe how they change clinical practices based on these discussions of data. So, when and how do data for quality assurance come to influence their perceptions of quality? By exploring these issues, we carve out a role for a sociological engagement with evidence in everyday medical practices. In conclusion, we suggest a need to move from the aim of being data driven to one of being data informed.

Data as symptom: Doctors' responses to patient-provided data in general practice.

People are increasingly able to generate their own health data through new technologies such as wearables and online symptom checkers. However, generating data is one thing, interpreting them another. General practitioners (GPs) are likely to be the first to help with interpretations. Policymakers in the European Union are investing heavily in infrastructures to provide GPs access to patient measurements. But there may be a disconnect between policy ambitions and the everyday practices of GPs. To investigate this, we conducted semi-structured interviews with 23 Danish GPs. According to the GPs, patients relatively rarely bring data to them. GPs mostly remember three types of patient-generated data that patients bring to them for interpretation: heart and sleep measurements from wearables and results from online symptom checkers. However, they also spoke extensively about data work with patient queries concerning measurements from the GPs' own online Patient Reported Outcome system and online access to laboratory results. We juxtapose GP reflections on these five data types and between policy ambitions and everyday practices. These data require substantial recontextualization work before the GPs ascribe them evidential value and act on them. Even when they perceived as actionable, patient-provided data are not approached as measurements, as suggested by policy frameworks. Rather, GPs treat them as analogous to symptoms-that is to say, GPs treat patient-provided data as subjective evidence rather than authoritative measures. Drawing on Science and Technology Studies (STS) literature,we suggest that GPs must be part of the conversation with policy makers and digital entrepreneurs around when and how to integrate patient-generated data into healthcare infrastructures.

Health app policy: international comparison of nine countries' approaches.

An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, patient care, and health outcomes in this context. This study compares the national policy approaches currently in development/use for health apps in nine countries. We used secondary data, combined with a detailed review of policy and regulatory documents, and interviews with key individuals and experts in the field of digital health policy to collect data about implemented and planned policies and initiatives. We found that most approaches aim for centralized pipelines for health app approvals, although some countries are adding decentralized elements. While the countries studied are taking diverse paths, there is nevertheless broad, international convergence in terms of requirements in the areas of transparency, health content, interoperability, and privacy and security. The sheer number of apps on the market in most countries represents a challenge for clinicians and patients. Our analyses of the relevant policies identified challenges in areas such as reimbursement, safety, and privacy and suggest that more regulatory work is needed in the areas of operationalization, implementation and international transferability of approvals. Cross-national efforts are needed around regulation and for countries to realize the benefits of these technologies.

Re: Hofmann et al. Overdiagnosis, one concept, three perspectives, and a model.

It is with great interest we have read the article "Overdiagnosis: one concept, three perspectives, and a model" by Hofmann and colleagues. We share the authors' ambition of understanding what overdiagnosis is and what it isn't. In our research, we define overdiagnosis on the basis of two interrelated phenomena: overdetection and overdefinition. Overdetection is the labelling of a person with a disease or abnormal condition, that would not have caused the person harm, e.g., symptoms or death, if left undiscovered. Overdefinition is the creation of new diagnoses by overmedicalising ordinary life experiences or expanding existing diagnoses by lowering thresholds or widening diagnostic criteria, without evidence of improved outcomes. These phenomena have different causes and thereby often different drivers. However, they have one important consequence in common: people are turned into patients unnecessarily, i.e., overdiagnosed. On a personal level, overdiagnosis cause various types of harms, including physical, psychological, social and financial harm. On a societal level, overdiagnosis may also cause harm to public health, cause resource waste, and cultural changes with overmedicalisation of normal life events. By definition, none of the aforementioned phenomena lead to any clinical benefit. Therefore, we disagree with Hofmann and colleagues' definition of overdiagnosis as diagnoses that "…on balance, do more harm than good.". We argue that introducing balance and benefits to the definition of overdiagnosis complicates the concept unnecessarily and cause problems operationalising overdiagnosis.

'You should see a doctor', said the robot: Reflections on a digital diagnostic device in a pandemic age.

AIMS: In three days at the beginning of the COVID-19 pandemic, the Copenhagen Emergency Medical Services developed a digital diagnostic device. The purpose was to assess and triage potential COVID-19 symptoms and to reduce the number of calls to public health-care helplines. The device was used almost 150,000 times in a few weeks and was described by politicians and administrators as a solution and success. However, high usage cannot serve as the sole criterion of success. What might be adequate criteria? And should digital triage for citizens by default be considered low risk? METHODS: This paper reflects on the uncertain aspects of the performance, risks and issues of accountability pertaining to the digital diagnostic device in order to draw lessons for future improvements. The analysis is based on the principles of evidence-based medicine (EBM), the EU and US regulations of medical devices and the taxonomy of uncertainty in health care by Han et al. RESULTS: Lessons for future digital devices are (a) the need for clear criteria of success, (b) the importance of awareness of other severe diseases when triaging, (c) the priority of designing the device to collect data for evaluation and (d) clear allocation of responsibilities. CONCLUSIONS: A device meant to substitute triage for citizens according to its own criteria of success should not by default be considered as low risk. In a pandemic age dependent on digitalisation, it is therefore important not to abandon the ethos of EBM, but instead to prepare the ground for new ways of building evidence of effect.

The influence of exercise and dehydration on the urine concentrations of salbutamol after inhaled administration of 1600 µg salbutamol as a single dose in relation to doping analysis.

The present study investigated the influence of exercise and dehydration on the urine concentrations of salbutamol after inhalation of that maximal permitted (1600 µg) on the 2015 World Anti-Doping Agency (WADA) prohibited list. Thirteen healthy males participated in the study. Urine concentrations of salbutamol were measured during three conditions: exercise (EX), exercise+dehydration (EXD), and rest (R). Exercise consisted of 75 min cycling at 60% of VO2max and a 20-km time-trial. Fluid intake was 2300, 270, and 1100 mL during EX, EXD, and R, respectively. Urine samples of salbutamol were collected 0-24 h after drug administration. Adjustment of urine concentrations of salbutamol to a specific gravity (USG) of 1.020 g/mL was compared with no adjustment. The 2015 WADA decision limit (1200 ng/mL) for salbutamol was exceeded in 23, 31, and 10% of the urine samples during EX, EXD, and R, respectively, when unadjusted for USG. When adjusted for USG, the corresponding percentages fell to 21, 15, and 8%. During EXD, mean urine concentrations of salbutamol exceeded (1325±599 ng/mL) the decision limit 4 h after administration when unadjusted for USG. Serum salbutamol Cmax was lower (P<0.01) for R(3.0±0.7 ng/mL) than EX(3.8±0.8 ng/mL) and EXD(3.6±0.8 ng/mL). AUC was lower for R (14.1±2.8 ng/mL·âˆ™h) than EX (16.9±2.9 ng/mL·âˆ™h)(P<0.01) and EXD (16.1±3.2 ng/mL·âˆ™h)(P<0.05). In conclusion, exercise and dehydration affect urine concentrations of salbutamol and increase the risk of Adverse Analytical Findings in samples collected after inhalation of that maximal permitted (1600 µg) for salbutamol. This should be taken into account when evaluating doping cases of salbutamol. Copyright © 2015 John Wiley & Sons, Ltd.

Mechanisms underlying enhancements in muscle force and power output during maximal cycle ergometer exercise induced by chronic ß2-adrenergic stimulation in men.

The study was a randomized placebo-controlled trial investigating mechanisms by which chronic ß2-adrenergic stimulation enhances muscle force and power output during maximal cycle ergometer exercise in young men. Eighteen trained men were assigned to an experimental group [oral terbutaline 5 mg/30 kg body weight (bw) twice daily (TER); n = 9] or a control group [placebo (PLA); n = 9] for a 4-wk intervention. No changes were observed with the intervention in PLA. Isometric muscle force of the quadriceps increased (P ≤ 0.01) by 97 ± 29 N (means ± SE) with the intervention in TER compared with PLA. Peak and mean power output during 30 s of maximal cycling increased (P ≤ 0.01) by 32 ± 8 and 25 ± 9 W, respectively, with the intervention in TER compared with PLA. Maximal oxygen consumption (V̇o2max) and time to fatigue during incremental cycling did not change with the intervention. Lean body mass increased by 1.95 ± 0.8 kg (P ≤ 0.05) with the intervention in TER compared with PLA. Change in single fiber cross-sectional area of myosin heavy chain (MHC) I (1,205 ± 558 µm(2); P ≤ 0.01) and MHC II fibers (1,277 ± 595 µm(2); P ≤ 0.05) of the vastus lateralis muscle was higher for TER than PLA with the intervention, whereas no changes were observed in MHC isoform distribution. Expression of muscle proteins involved in growth, ion handling, lactate production, and clearance increased (P ≤ 0.05) with the intervention in TER compared with PLA, with no change in oxidative enzymes. Our observations suggest that muscle hypertrophy is the primary mechanism underlying enhancements in muscle force and peak power during maximal cycling induced by chronic ß2-adrenergic stimulation in humans.