Can decision support combat incompleteness and bias in routine primary care data?

ObjectiveRoutine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and MethodsWe used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.ResultsSupported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.ConclusionsWe provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.     

Do community pharmacists in Nepal have a role in adverse drug reaction reporting systems?

Community pharmacies in Nepal serve both rural and urban populations and are an integral part of the Nepalese healthcare system. These community pharmacies are run by non-pharmacist professionals with orientation training on pharmacology and drug dispensing. Graduate pharmacists’ involvement in community pharmacy will help with patient counselling, dispensing of medication and promotion of safe and appropriate medicine use. Nepal has an organised pharmacovigilance system which incorporates adverse drug reaction (ADRs) from hospitals and tertiary care centres but not from the community. Involvement of pharmacists in community pharmacy will help in ADR reporting and, monitoring at community level and will help in promoting medication safety in the community. This article describes the community pharmacovigilance program in Nepal and the prospects for community pharmacists.     

PDCA循环在降低住院药房口服摆药调剂内差差错率中的应用效果

目的 探讨PDCA循环在住院药房口服摆药调剂内差中的应用情况。方法 收集2015年7月至2016年12月由复核药师在《中心药房口服摆药调配差错记录本》登记的资料,运用PDCA方法,分析调剂内差原因,针对数量差错和药品破损进行优化和改善,对改善前后的有形成果和无形成果进行比较分析。结果 实施PDCA循环管理模式后,口服摆药调剂内差差错率显著降低,由改善前的0.166%降低至0.034%,目标达标率为135.766%,进步率为79.674%。药师在和谐程度、积极性、责任感、沟通配合、愉悦感和团队凝聚力等无形成果方面较改善前有很大的提高。结论 运用PDCA循环管理方法,使发药差错问题得到不断纠正和解决,保障了我院住院患者的口服用药安全与切实利益。     

Effects of technology-supported exercise programs on the knee pain,physical function,and quality of life of individuals with knee osteoarthritis and/or chronic knee pain: A systematic review and meta-analysis of randomized controlled trials

ObjectiveThe study sought to examine the effects of technology-supported exercise programs on the knee pain, physical function, and quality of life of individuals with knee osteoarthritis and/or chronic knee pain by a systematic review and meta-analysis of randomized controlled trials.Materials and MethodsWe searched MEDLINE, EMBASE, CINAHL Plus, and the Cochrane Library from database inception to August 2020. A meta-analysis and subgroup analyses, stratified by technology type and program feature, were conducted.ResultsTwelve randomized controlled trials were reviewed, all of which implemented the programs for 4 weeks to 6 months. Telephone, Web, mobile app, computer, and virtual reality were used to deliver the programs. The meta-analysis showed that these programs were associated with significant improvements in knee pain (standardized mean difference [SMD] = −0.29; 95% confidence interval [CI], −0.48 to −0.10; P = .003) and quality of life (SMD = 0.25; 95% CI, 0.04 to 0.46; P = .02) but not with significant improvement in physical function (SMD = 0.22; 95% CI, 0 to 0.43; P = .053). Subgroup analyses showed that some technology types and program features were suggestive of potential benefits.ConclusionsUsing technology to deliver the exercise programs appears to offer benefits. The technology types and program features that were associated with health values have been identified, based on which suggestions are discussed for the further research and development of such programs.     

Association between limiting the number of open records in a tele-critical care setting and retract–reorder errors

BackgroundWrong patient selection errors may be tracked by retract–reorder (RAR) events. The aim of this quality improvement study was to assess the impact of reducing the number of concurrently open electronic health records from 4 to 2 on RAR errors generated by a tele-critical care service.MethodsThe study encompassed 32 months before and 21 months after restriction. Chi-Square test of proportions and T statistical process control chart for rare events were used.ResultsThere were 156 318 orders with 57 RAR errors (36.5/100 000 orders) before restriction, and 122 587 orders with 34 errors (27.7/100 000 orders) after. Rates were not statistically different (P = .20), but analysis was underpowered. When plotted on a T control chart, random variation was detected between RAR errors.ConclusionWe found no significant difference in RAR errors in the tele-critical care setting after open record limitation. Other strategies should be studied to reduce wrong patient selection errors.     

Risk factors associated with medication ordering errors

ObjectiveWe utilized a computerized order entry system–integrated function referred to as “void” to identify erroneous orders (ie, a “void” order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors.Materials and MethodsWe collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors–based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems–based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety.ResultsDuring the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors.ConclusionsThe void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.     

An assessment of pharmacists’ readiness for paperless labeling: a national survey

Objective

To assess the state of readiness for the adoption of paperless labeling among a nationally representative sample of pharmacies, including chain pharmacies, independent retail pharmacies, hospitals, and other rural or urban dispensing sites.

Methods

Both quantitative and qualitative analyses were used to analyze responses to a cross-sectional survey disseminated to American Pharmacists Association pharmacists nationwide. The survey assessed factors related to pharmacists’ attitudinal readiness (ie, perceptions of impact) and pharmacies’ structural readiness (eg, availability of electronic resources, internet access) for the paperless labeling initiative.

Results

We received a total of 436 survey responses (6% response rate) from pharmacists representing 44 US states and territories. Across the spectrum of settings we studied, pharmacists had work access to computers, printers, fax machines and access to the internet or intranet. Approximately 79% of respondents believed that the initiative would improve the adequacy of drug information available in their work site and 95% believed it would either not change (33%) or would improve (62%) communication to patients. Overall, respondents’ comments supported advancing the initiative; however, some comments revealed reservations regarding corporate or pharmacy buy-in, success of implementation, and ease of adoption.

Conclusions

This is the first nationwide study to report about pharmacists’ perspectives on paperless labeling. In general, pharmacists believe they are ready and that their pharmacies are well equipped for the transition to paperless labeling. Further exploration of perspectives from product label manufacturers and corporate pharmacy offices is needed to understand fully what will be necessary to complete this transition.     

Baseline left ventricular ejection fraction associated with symptom improvements in both children and adolescents with postural tachycardia syndrome under metoprolol therapy

Background:Postural tachycardia syndrome (POTS) is a common childhood disease that seriously affects the patient''s physical and mental health. This study aimed to investigate whether pre-treatment baseline left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS) values were associated with symptom improvement after metoprolol therapy for children and adolescents with POTS.Methods:This retrospective study evaluated 51 children and adolescents with POTS who received metoprolol therapy at the Peking University First Hospital between November 2010 and July 2019. All patients had completed a standing test or basic head-up tilt test and cardiac echocardiography before treatment. Treatment response was evaluated 3 months after starting metoprolol therapy. The pre-treatment baseline LVEF and LVFS values were evaluated for correlations with decreases in the symptom score after treatment (ΔSS). Multivariable analysis was performed using factors with a P value of <0.100 in the univariate analyses and the demographic characteristics.Results:A comparison of responders and non-responders revealed no significant differences in demographic, hemodynamic characteristics, and urine specific gravity (all P > 0.050). However, responders had significantly higher baseline LVEF (71.09% ± 4.44% vs. 67.17% ± 4.88%, t = −2.789, P = 0.008) and LVFS values (40.00 [38.00, 42.00]% vs. 36.79% ± 4.11%, Z = −2.542, P = 0.010) than the non-responders. The baseline LVEF and LVFS were positively correlated with ΔSS (r = 0.378, P = 0.006; r = 0.363, P = 0.009), respectively. Logistic regression analysis revealed that LVEF was independently associated with the response to metoprolol therapy in children and adolescents with POTS (odds ratio: 1.201, 95% confidence interval: 1.039–1.387, P = 0.013).Conclusions:Pre-treatment baseline LVEF was associated with symptom improvement after metoprolol treatment for children and adolescents with POTS.     

Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank

ObjectiveThe objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank.Materials and MethodsWe built a fully automated image curation and labeling technique using deep learning and distributive computing to identify subcutaneous and visceral abdominal fat compartments from 52,844 computed tomography scans in 13,502 patients in the Penn Medicine Biobank (PMBB). A classification network identified the inferior and superior borders of the abdomen, and a segmentation network differentiated visceral and subcutaneous fat. Following technical evaluation of our method, we conducted studies to validate known relationships with visceral and subcutaneous fat.ResultsWhen compared with 100 manually annotated cases, the classification network was on average within one 5-mm slice for both the superior (0.4 ± 1.1 slice) and inferior (0.4 ± 0.6 slice) borders. The segmentation network also demonstrated excellent performance with intraclass correlation coefficients of 1.00 (P < 2 × 10^-16) for subcutaneous and 1.00 (P < 2 × 10^-16) for visceral fat on 100 testing cases. We performed integrative analyses of abdominal fat with the phenome extracted from the electronic health record and found highly significant associations with diabetes mellitus, hypertension, and renal failure, among other phenotypes.ConclusionsThis work presents a fully automated and highly accurate method for the quantification of abdominal fat that can be applied to routine clinical imaging studies to fuel translational scientific discovery.     

Impact of a problem-oriented view on clinical data retrieval

ObjectiveThe electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).MethodsSimple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.ResultsWith POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001).DiscussionThe study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.ConclusionEHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.     

How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis

ObjectiveTo conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS.Materials and MethodsWe searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed.ResultsThere was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18–8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72–0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence.Discussion and ConclusionDespite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.     

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)

ObjectiveThis study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses.Materials and MethodsUsing an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated.ResultsIn general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased.DiscussionAnalyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression.ConclusionIn general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression—an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.     

The design and evaluation of a novel algorithm for automated preference card optimization

BackgroundInaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.MethodsThe algorithm cross-references the supplies used in each procedure and listed on each preference card and uses a time-series regression to estimate the likelihood that each quantity listed on the preference card is inaccurate. Algorithm performance was evaluated by measuring changes in direct costs between preference cards revised with the algorithm and preference cards that were not revised or revised without use of the algorithm. Results were evaluated with a difference-in-differences (DID) multivariate fixed-effects model of costs during an 8-month pre-intervention and a 15-month post-intervention period.ResultsThe accuracies of the quantities of 469 155 surgeon–procedure-specific items were estimated. Nurses used these estimates to revise 309 preference cards across eight surgical services corresponding to, respectively, 1777 and 3106 procedures in the pre- and post-intervention periods. The average direct cost of supplies per case decreased by 8.38% ($352, SD $6622) for the intervention group and increased by 13.21% ($405, SD $14 706) for the control group (P < .001). The DID analysis showed significant cost reductions only in the intervention group during the intervention period (P < .001).ConclusionThe optimization of preference cards with a variety of institution-specific, manually intensive approaches has led to cost savings. The automated algorithm presented here produced similar results that may be more readily reproducible.     

Clinical decision support system,using expert consensus-derived logic and natural language processing,decreased sedation-type order errors for patients undergoing endoscopy

ObjectiveDetermination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy.Materials and MethodsThe CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods.Results22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54).DiscussionAt our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors.ConclusionA workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.     

Effects of secukinumab and adalimumab on serum uric acid level in patients with plaque psoriasis

Background:Psoriasis is a chronic systemic inflammatory disease, and hyperuricemia is a common comorbidity in patients with psoriasis. However, there are limited reports on the relationship between serum uric acid levels and biological treatment efficacy. The purposes of this study were to compare the differences in serum uric acid levels between patients with psoriasis and healthy controls and analyze the risk of hyperuricemia.Methods:A total of 196 patients with psoriasis and 191 age- and sex-matched healthy controls were enrolled in this retrospective cohort study. One hundred and twenty-seven patients with severe psoriasis were treated with biologics. Sixty-eight patients received adalimumab, and 59 patients received secukinumab. Serum uric acid levels were measured at baseline, week 24, and week 48 of treatment.Results:Patients with psoriasis had higher serum uric acid levels than healthy controls (6.4 ± 1.7 mg/dL vs. 5.7 ± 1.5 mg/dL, P < 0.001). Hyperuricemia was found in 33.7% (66/196) of patients with psoriasis, which was significantly higher than that in healthy controls (13.1% [25/191], P < 0.001). Serum uric acid levels and hyperuricemia were not related to the severity of psoriasis (P > 0.05). No significant changes in serum uric acid levels and hyperuricemia were observed following adalimumab treatment (P > 0.05). The serum uric acid level in patients treated with secukinumab was 6.7 ± 1.6 mg/dL at week 24, which was not statistically different from that at baseline (6.6 ± 1.4 mg/dL, P = 0.885). Serum uric acid levels were significantly decreased at week 48 (6.3 ± 1.5 mg/dL vs. 6.6 ± 1.4 mg/dL, P = 0.007) in patients treated with secukinumab. Secukinumab had no significant effect on hyperuricemia either (P > 0.05).Conclusions:The serum uric acid levels and prevalence of hyperuricemia in patients with psoriasis were significantly higher than those in healthy controls. Secukinumab treatment for 48 weeks successfully decreased serum uric acid levels in patients with psoriasis, whereas adalimumab had no significant effect on serum uric acid levels.