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Provides a 3,000-year history of the census, chronicling the practices of the ancient world through the Supreme Court rulings of today, examining how censuses have been used as tools of democracy, exclusion and mass surveillance.

Background Whilst interest in efficient trial design has grown with the use of electronic health records (EHRs) to collect trial outcomes, practical challenges remain. Commonly raised concerns often revolve around data availability, data quality and issues with data validation. This study aimed to assess the agreement between data collected on clinical trial participants from different sources to provide empirical evidence on the utility of EHRs for follow-up in randomised controlled trials (RCTs). Methods This retrospective, participant-level data utility comparison study was undertaken using data collected as part of a UK primary care-based, randomised controlled trial (OPTiMISE). The primary outcome measure was the recording of all-cause hospitalisation or mortality within 3 years post-randomisation and was assessed across (1) Coded primary care data; (2) Coded-plus-free-text primary care data; and (3) Coded secondary care and mortality data. Agreement levels across data sources were assessed using Fleiss’ Kappa (K). Kappa statistics were interpreted using an established framework, categorising agreement strength as follows: <0 (poor), 0.00–0.20 (slight), 0.21–0.40 (fair), 0.41–0.60 (moderate), 0.61–0.80 (substantial), and 0.81–1.00 (almost perfect) agreement. The impact of using different data sources to determine trial outcomes was assessed by replicating the trial’s original analyses. Results Almost perfect agreement was observed for mortality outcome across the three data sources (K = 0.94, 95%CI 0.91–0.98). Fair agreement (weak consistency) was observed for hospitalisation outcomes, including all-cause hospitalisation or mortality (K = 0.35, 95%CI 0.28–0.42), emergency hospitalisation (K = 0.39, 95%CI 0.33–0.46), and hospitalisation or mortality due to cardiovascular disease (K = 0.32, 95%CI 0.19–0.45). The overall trial results remained consistent across data sources for the primary outcome, albeit with varying precision. Conclusion Significant discrepancies according to data sources were observed in recording of secondary care outcomes. Investigators should be cautious when choosing which data source(s) to use to measure outcomes in trials. Future work on linking participant-level data across healthcare settings should consider the variations in diagnostic coding practices. Standardised definitions for outcome measures when using healthcare systems data and using data from different data sources for cross-checking and verification should be encouraged.

Chest decompression can be performed by different techniques, like needle thoracocentesis (NT), lateral thoracostomy (LT), or tube thoracostomy (TT). The aim of this study was to report the incidence of prehospital chest decompression and to analyse the effectiveness of these techniques. In this retrospective case series study, all medical records of adult trauma patients undergoing prehospital chest decompression and admitted to the resuscitation area of a level-1 trauma center between 2009 and 2015 were reviewed and analysed. Only descriptive statistics were applied. In a 6-year period 24 of 2261 (1.1%) trauma patients had prehospital chest decompression. Seventeen patients had NT, six patients TT, one patient NT as well as TT, and no patients had LT. Prehospital successful release of a tension pneumothorax was reported by the paramedics in 83% (5/6) with TT, whereas NT was effective in 18% only (3/17). In five CT scans all thoracocentesis needles were either removed or extrapleural, one patient had a tension pneumothorax, and two patients had no pneumothorax. No NT or TT related complications were reported during hospitalization. Prehospital NT or TT is infrequently attempted in trauma patients. Especially NT is associated with a high failure rate of more than 80%, potentially due to an inadequate ratio between chest wall thickness and catheter length as previously published as well as a possible different pathophysiological cause of respiratory distress. Therefore, TT may be considered already in the prehospital setting to retain sufficient pleural decompression upon admission.

This observational study showed that measures of quality of care for patients with diabetes are better in practices using electronic health records (EHRs) than in those using paper records. Although not definitive, the data suggest that EHRs may improve quality of care. Incentives to increase adoption and meaningful use of electronic health records (EHRs) anticipate a quality-related financial return. 1 , 2 However, empirical data showing either quality improvement or cost savings from EHR adoption are scarce. Available studies have shown few quality-related advantages of current EHR systems over traditional paper-based medical-record systems. 1 – 5 Projected cost savings are mostly based on models with largely unsupported assumptions about adherence to and the effect of fully functional EHR systems. 6 , 7 Data are particularly scarce on EHR adoption by “priority primary care providers” — health care professionals practicing in small groups and those serving vulnerable populations, as . . .

Objective To assess the short term association of inpatient implementation of electronic health records (EHRs) with patient outcomes of mortality, readmissions, and adverse safety events.Design Observational study with difference-in-differences analysis.Setting Medicare, 2011-12.Participants Patients admitted to 17 study hospitals with a verifiable “go live” date for implementation of inpatient EHRs during 2011-12, and 399 control hospitals in the same hospital referral region.Main outcome measures All cause readmission within 30 days of discharge, all cause mortality within 30 days of admission, and adverse safety events as defined by the patient safety for selected indicators (PSI)-90 composite measure among Medicare beneficiaries admitted to one of these hospitals 90 days before and 90 days after implementation of the EHRs (n=28 235 and 26 453 admissions), compared with the control group of all contemporaneous admissions to hospitals in the same hospital referral region (n=284 632 and 276 513 admissions). Analyses were adjusted for beneficiaries’ sociodemographic and clinical characteristics.Results Before and after implementation, characteristics of admissions were similar in both study and control hospitals. Among study hospitals, unadjusted 30 day mortality (6.74% to 7.15%, P=0.06) and adverse safety event rates (10.5 to 11.4 events per 1000 admissions, P=0.34) did not significantly change after implementation of EHRs. There was an unadjusted decrease in 30 day readmission rates, from 19.9% to 19.0% post-implementation (P=0.02). In difference-in-differences analysis, however, there was no significant change in any outcome between pre-implementation and post-implementation periods (all P≥0.13).Conclusions Despite concerns that implementation of EHRs might adversely impact patient care during the acute transition period, we found no overall negative association of such implementation on short term inpatient mortality, adverse safety events, or readmissions in the Medicare population across 17 US hospitals.

Current electronic health record (EHR) user interfaces are suboptimally designed and may be associated with excess cognitive workload and poor performance. To assess the association between the usability of an EHR system for the management of abnormal test results and physicians' cognitive workload and performance levels. This quality improvement study was conducted in a simulated EHR environment. From April 1, 2016, to December 23, 2016, residents and fellows from a large academic institution were enrolled and allocated to use either a baseline EHR (n = 20) or an enhanced EHR (n = 18). Data analyses were conducted from January 9, 2017, to March 30, 2018. The EHR with enhanced usability segregated in a dedicated folder previously identified critical test results for patients who did not appear for a scheduled follow-up evaluation and provided policy-based decision support instructions for next steps. The baseline EHR displayed all patients with abnormal or critical test results in a general folder and provided no decision support instructions for next steps. Cognitive workload was quantified subjectively using NASA-Task Load Index and physiologically using blink rates. Performance was quantified according to the percentage of appropriately managed abnormal test results. Of the 38 participants, 25 (66%) were female. The 20 participants allocated to the baseline EHR compared with the 18 allocated to the enhanced EHR demonstrated statistically significantly higher cognitive workload as quantified by blink rate (mean [SD] blinks per minute, 16 [9] vs 24 [7]; blink rate, -8 [95% CI, -13 to -2]; P = .01). The baseline group showed statistically significantly poorer performance compared with the enhanced group who appropriately managed 16% more abnormal test results (mean [SD] performance, 68% [19%] vs 98% [18%]; performance rate, -30% [95% CI, -40% to -20%]; P < .001). Relatively basic usability enhancements to the EHR system appear to be associated with better physician cognitive workload and performance; this finding suggests that next-generation systems should strip away non-value-added EHR interactions, which may help physicians eliminate the need to develop their own suboptimal workflows.

Background The use of historical external control data in clinical trials has grown in interest and needs when considering the design of future trials. Hybrid control designs can be more efficient to achieve the same power with fewer patients and limited resources. The literature is sparse on appropriate statistical methods which can account for the differences between historical external controls and the control patients in a study. In this article, we illustrate the analysis framework of a clinical trial if a hybrid control design was used after determining an RCT may not be feasible. Methods We utilize two previously completed RCTs in nonsquamous NSCLC and a nationwide electronic health record derived de-identified database as examples and compare 5 analysis methods on each trial, as well as a set of simulations to determine operating characteristics of such designs. Results In single trial estimation, the Case Weighted Adaptive Power Prior provided estimated treatment hazard ratios consistent with the original trial’s conclusions with narrower confidence intervals. The simulation studies showed that the Case Weighted Adaptive Power Prior achieved the highest power (and well controlled type-1 error) across all 5 methods with consistent study sample size. Conclusions By following the proposed hybrid control framework, one can design a hybrid control trial transparently and accounting for differences between control groups while controlling type-1 error and still achieving efficiency gains from the additional contribution from external controls.

There is growing interest in leveraging real-world data to complement knowledge gained from randomized clinical trials and inform the design of prospective randomized studies in oncology. The present study compared clinical outcomes in women with metastatic breast cancer who received letrozole as first-line monotherapy in oncology practices across the United States versus patients in the letrozole-alone cohort of the PALOMA-2 phase 3 trial. The real-world cohort (N = 107) was derived from de-identified patient data from the Flatiron Health electronic health record database. The clinical trial cohort (N = 222) comprised postmenopausal women in the letrozole-alone arm of PALOMA-2. Patients in the real-world cohort received letrozole monotherapy per labeling and clinical judgment; patients in PALOMA-2 received letrozole 2.5 mg/d, continuous. Real-world survival and response rates were based on evidence of disease burden curated from clinician notes, radiologic reports, and pathology reports available in the electronic health record. Progression-free survival and objective response rate in PALOMA-2 were based on Response Evaluation Criteria in Solid Tumors v1.1. Concordance of survival and response rates were retrospectively assessed using inverse probability of treatment weighting-adjusted Cox regression analysis. Inverse probability of treatment weighting-adjusted Cox regression results showed similar median progression-free survival in the real-world and PALOMA-2 cohorts (18.4 and 16.6 months, respectively): the hazard ratio using real-world data as reference was 1.04 (95% CI, 0.69-1.56). No significant difference was observed in response rates: 41.8% in the real-world cohort vs 39.4% in the PALOMA-2 cohort (odds ratio using real-world data as reference: 0.91 [95% CI, 0.57-1.44]). These findings indicate that data abstracted from electronic health records with proper quality controls can yield meaningful information on clinical outcomes. These data increase confidence in the use of real-world assessments of progression and response as efficacy endpoints. Trial registration NCT01740427; Funding: Pfizer.

Background Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. Methods Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. Results The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. Conclusions Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Background Inappropriate medication and polypharmacy increase morbidity, hospitalisation rate, costs and mortality in multimorbid patients. At hospital discharge of elderly patients, polypharmacy is often even more pronounced than at admission. However, the optimal discharge strategy in view of sustained medication appropriateness remains unclear. In particular, unreflectingly switching back to the pre-hospitalisation medication must be avoided. Therefore, both the patients and the follow-up physicians should be involved in the discharge process. In this study, we aim to test whether a brief medication review which takes the patients’ priorities into account, combined with a standardised communication strategy at hospital discharge, leads to sustained medication appropriateness and extends readmission times among elderly multimorbid patients. Methods The study is designed as a two-armed, double-blinded, cluster-randomised trial, involving 42 senior hospital physicians (HPs) with their junior HPs and 2100 multimorbid patients aged 60 years or older. Using a randomised minimisation strategy, senior HPs will be assigned to either intervention or control group. Following instructions of the study team, the senior HPs in the intervention group will teach their junior HPs how to integrate a simple medication review tool combined with a defined communication strategy into their ward’s discharge procedure. The untrained HPs in the control group will provide data on usual care, and their patients will be discharged following usual local routines. Primary outcome is the time until readmission within 6 months after discharge, and secondary outcomes cover readmission rates, number of emergency and GP visits, classes and numbers of drugs prescribed, proportions of potentially inappropriate medications, and the patients’ quality of life after discharge. Additionally, the characteristics of both the HPs as well as the patients will be collected before the intervention. Process evaluation outcomes will be assessed parallel to the ongoing core study using qualitative research methods. Discussion So far, interventions to reduce polypharmacy are still scarce at the crucial interface between HPs and GPs. To our knowledge, this trial is the first to analyse the combination of a brief deprescribing intervention with a standardised communication strategy at hospital discharge and in the early post-discharge period. Trial registration ISRCTN, ISRCTN18427377 . Registered 11 January 2018