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.     

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.     

A study on relationship between Neural Response Telemetry and behavioural Threshold/Comfort levels in children with cochlear implant

BackgroundOur study was a prospective, non-randomised, single tertiary care centre study involving 50 children below 10 years, all implanted with Nucleus 24™ cochlear implants to find the relationship between intra-operative NRT (Neural Response Telemetry) values and post-operatively behaviourally obtained Threshold (T) and Comfort (C) levels.MethodsNRT threshold values were obtained intra-operatively by using Custom Sound EP Software V 4.4™ (Cochlear Corporation). At switch-on after three weeks, behavioural T and C levels were measured based on behavioural responses given by patients using Custom Sound Version 4.4 software™ (Cochlear Corporation). NRT values were also measured at switch-on, 2 months, 3 months and 6 months.ResultsIntra-operative NRT levels (187.96 ± 12.48) were higher than both T (160.63 ± 22.69) and C (181.21 ± 22.41) levels obtained after 3 weeks. In addition, there was a weak correlation of NRT values with T (P value: 0.05, r = 0.391) and C (P value: 0.05, r = 0.390) levels.ConclusionNRT is a quick and non-invasive tool to confirm cochlear implant integrity. There is a weak correlation of NRT values with both T and C levels and therefore, intra-operative NRT is a weak predictor for setting both T and C levels at switch-on.     

Public vs physician views of liability for artificial intelligence in health care

The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physicians and the U.S. public, we find that the public is significantly more likely to believe that physicians should be held responsible when an error occurs during care delivered with medical AI, though the majority of both physicians and the public hold this view (66.0% vs 57.3%; P = .020). Physicians are more likely than the public to believe that vendors (43.8% vs 32.9%; P = .004) and healthcare organizations should be liable for AI-related medical errors (29.2% vs 22.6%; P = .05). Views of medical liability did not differ by clinical specialty. Among the general public, younger people are more likely to hold nearly all parties liable.     

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts

ObjectiveTo assess the appropriateness of medication-related clinical decision support (CDS) alerts associated with renal insufficiency and the potential/actual harm from overriding the alerts.Materials and MethodsOverride rate frequency was recorded for all inpatients who had a renal CDS alert trigger between 05/2017 and 04/2018. Two random samples of 300 for each of 2 types of medication-related CDS alerts associated with renal insufficiency—“dose change” and “avoid medication”—were evaluated by 2 independent reviewers using predetermined criteria for appropriateness of alert trigger, appropriateness of override, and patient harm.ResultsWe identified 37 100 “dose change” and 5095 “avoid medication” alerts in the population evaluated, and 100% of each were overridden. Dose change triggers were classified as 12.5% appropriate and overrides of these alerts classified as 90.5% appropriate. Avoid medication triggers were classified as 29.6% appropriate and overrides 76.5% appropriate. We identified 5 adverse drug events, and, of these, 4 of the 5 were due to inappropriately overridden alerts.ConclusionAlerts were nearly always presented inappropriately and were all overridden during the 1-year period studied. Alert fatigue resulting from receiving too many poor-quality alerts may result in failure to recognize errors that could lead to patient harm. Although medication-related CDS alerts associated with renal insufficiency had previously been found to be the most clinically beneficial alerts in a legacy system, in this system they were ineffective. These findings underscore the need for improvements in alert design, implementation, and monitoring of alert performance to make alerts more patient-specific and clinically appropriate.     

Machine-learning model to predict the cause of death using a stacking ensemble method for observational data

ObjectiveCause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient’s last medical checkup.Materials and MethodsTo classify the mortality status and each individual cause of death, we used a stacking ensemble method. The prediction outcomes were all-cause mortality, 8 leading causes of death in South Korea, and other causes. The clinical data of study populations were extracted from the national claims (n = 174 747) and electronic health records (n = 729 065) and were used for model development and external validation. Moreover, we imputed the cause of death from the data of 3 US claims databases (n = 994 518, 995 372, and 407 604, respectively). All databases were formatted to the Observational Medical Outcomes Partnership Common Data Model.ResultsThe generalized area under the receiver operating characteristic curve (AUROC) of the model predicting the cause of death within 60 days was 0.9511. Moreover, the AUROC of the external validation was 0.8887. Among the causes of death imputed in the Medicare Supplemental database, 11.32% of deaths were due to malignant neoplastic disease.DiscussionThis study showed the potential of machine-learning models as a new alternative to address the lack of access to cause-of-death data. All processes were disclosed to maintain transparency, and the model was easily applicable to other institutions.ConclusionA machine-learning model with competent performance was developed to predict cause of death.     

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.     

Joint detection of ERCC1, TUBB3, and TYMS guidance selection of docetaxel, 5-fluorouracil and cisplatin (DDP) individual chemotherapy in advanced gastric cancer patients

Background

To investigate the guidance selection of docetaxel (D), cisplatin (DDP) (C), and 5-fluorouracil (5-FU) (F) as individual chemotherapy agents via joint detection of ERCC1, TUBB3, and TYMS genes in patients with advanced gastric cancer (AGC).

Method

Clinical data of 120 patients with AGC who enrolled in our hospital between May 2009 and May 2012 were analyzed. These patients were randomly assigned to experimental and control groups. The mRNA expression of ERCC1, TUBB3, and TYMS was measured by DNA chip technology in the experimental group. Different chemotherapies were administered according to the mRNA expression levels of the three genes, while DCF chemotherapy was directly applied to the control group. Correlation between the three genes’ mRNA levels, efficiency rate, the median time to progression (MTP), median survival time (MST) and adverse reactions was evaluated.

Results

As a result, there was a significant correlation between ERCC1 and TUBB3 mRNA expression (P = 0.005), but no obvious correlation between TUBB3 and TYMS or ERCC1 and TYMS. There was also no significant difference in the efficiency rate of chemotherapy (50% versus 55%; P = 0.357) and the MTP (10 months versus 7 months; P = 0.091) between the two groups. However, there was obvious significance in MST (13.7 months versus 11.6 months; P = 0.004). Additionally, the experimental group provided us with a more effective way for controlling adverse reactions to chemotherapy.

Conclusion

Combination regimen of D, C, and F in AGC patients according to their ERCC1, TUBB3, and TYMS mRNA expression level may reduce adverse reactions and improve MST.     

Impact of event notification services on timely follow-up and rehospitalization among primary care patients at two Veterans Affairs Medical Centers

ObjectiveTo examine the effectiveness of event notification service (ENS) alerts on health care delivery processes and outcomes for older adults.Materials and methodsWe deployed ENS alerts in 2 Veterans Affairs (VA) medical centers using regional health information exchange (HIE) networks from March 2016 to December 2019. Alerts targeted VA-based primary care teams when older patients (aged 65+ years) were hospitalized or attended emergency departments (ED) outside the VA system. We employed a concurrent cohort study to compare postdischarge outcomes between patients whose providers received ENS alerts and those that did not (usual care). Outcome measures included: timely follow-up postdischarge (actual phone call within 7 days or an in-person primary care visit within 30 days) and all-cause inpatient or ED readmission within 30 days. Generalized linear mixed models, accounting for clustering by primary care team, were used to compare outcomes between groups.ResultsCompared to usual care, veterans whose primary care team received notification of non-VA acute care encounters were 4 times more likely to have phone contact within 7 days (AOR = 4.10, P < .001) and 2 times more likely to have an in-person visit within 30 days (AOR = 1.98, P = .007). There were no significant differences between groups in hospital or ED utilization within 30 days of index discharge (P = .057).DiscussionENS was associated with increased timely follow-up following non-VA acute care events, but there was no associated change in 30-day readmission rates. Optimization of ENS processes may be required to scale use and impact across health systems.ConclusionGiven the importance of ENS to the VA and other health systems, this study provides guidance for future research on ENS for improving care coordination and population outcomes.Trial RegistrationClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02689076","term_id":"NCT02689076"}}NCT02689076. “Regional Data Exchange to Improve Care for Veterans After Non-VA Hospitalization.” Registered February 23, 2016.     

Retrospective analysis of the frequency of centrofacial telangiectasia in systemic sclerosis patients treated with bosentan or ilomedin

Background

Bosentan is a dual endothelin receptor antagonist initially introduced for the treatment of pulmonary arterial hypertension and recently approved for the treatment of digital ulcers in patients with systemic sclerosis (SSc). Our clinical observations indicate that bosentan therapy may be associated with an increased frequency of centrofacial telangiectasia (TAE). Here, we sought to analyze the frequency of TAE in patients with SSc who were treated with either bosentan or the prostacyclin analog iloprost.

Methods

We conducted a retrospective analysis in 27 patients with SSc undergoing therapy with either bosentan (n = 11) or iloprost (n = 16). Standardized photodocumentations of all patients (n = 27) were obtained at a time point ten months after therapy initiation and analyzed. A subgroup of patients (bosentan: n = 6; iloprost: n = 6) was additionally photodocumented prior to therapy initiation, enabling an intraindividual analysis over the course of therapy.

Results

After ten months of therapy patients with SSc receiving bosentan showed a significantly (P = 0.0028) higher frequency of centrofacial TAE (41.6 ± 27.8) as compared to patients with SSc receiving iloprost (14.3 ± 13.1). Detailed subgroup analysis revealed that the frequency of TAE in the bosentan group (n = 6 patients) increased markedly and significantly (P = 0.027) by 44.4 after ten months of therapy (TAE at therapy initiation: 10.8 ± 5.1; TAE after ten months of therapy: 55.2 ± 29.8), whereas an only minor increase of 1.9 was observed in the iloprost group (n = 6 patients; TAE at therapy initiation: 18.3 ± 14.5; TAE after ten months of therapy: 20.2 ± 15.5), yet without reaching statistical significance (P = 0.420).

Conclusions

The use of bosentan may be associated with an increased frequency of TAE in patients with SSc. Patients should be informed about this potential adverse effect prior to therapy. Treatment options may include camouflage or laser therapy.     

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

ObjectiveSocial determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.Materials and MethodsA broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.ResultsSmoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).ConclusionNLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.     

Evaluation of quality of recovery score in mothers and neonatal outcome assessment after surgery using preoperative dexamethasone for caesarean section

BackgroundQuality of recovery (QoR) after surgery is an essential measure of early postoperative health status of the patient. The incidence of caesarean section (CS) has increased in the last few decades. Numerous studies have been conducted for reduction of pain after CS but only a few on the effect of preoperative dexamethasone on QoR after CS. This study is designed to evaluate QoR with dexamethasone and neonate outcome assessment after CS.MethodsThis is a prospective, randomised study in which patients undergo CS under spinal anaesthesia (SA). Patients received either 8 mg (2 ml) of dexamethasone IV (group D, n = 30) or 2 ml of 0.9% normal saline (group C; n = 30) before SA. The QoR-40 and Apgar score, neonatal respiratory distress, haemodynamic response and ill effects were recorded.ResultsThe baseline haemodynamic (heart rate, mean arterial pressure (MRP), Respiratory rate (RR) and pulse oximetry) parameters were comparable in both the groups. The mean global score and each score in group D was better than that of group C (p < 0.001). The mean duration of block in group D (169.83 ± 9.05 min) was more than that in group C (163.33 ± 11.91 min) (p < 0.021). The incidence of neonatal respiratory distress, neonatal intensive care unit admission and Apgar score at 1 min and at 5 min of birth was comparable between the groups.ConclusionDexamethasone has a positive effect on early postoperative recovery of patients undergoing CS with a delay in regression of spinal block and without any significant adverse effects on neonatal outcomes.     

Low-frequency pulsed electromagnetic fields significantly improve time of closure and proliferation of human tendon fibroblasts

Background

The promotion of the healing process following musculoskeletal injuries comprises growth factor signalling, migration, proliferation and apoptosis of cells. If these processes could be modulated, the healing of tendon tissue may be markedly enhanced. Here, we report the use of the Somagen™ device, which is certified for medical use according to European laws. It generates low-frequency pulsed electromagnetic fields that trigger effects of a nature that are yet to be determined.

Methods

A 1.5-cm wide, linear scrape was introduced into patellar tendon fibroblast cultures (N = 5 donors). Treatment was carried out every second day. The regimen was applied three times in total with 30 minutes comprising pulsed electromagnetic field packages with two fundamental frequencies (10 minutes of 33 Hz, 20 minutes of 7.8 Hz). Control cells remained untreated. All samples were analyzed for gap closure time, proliferation and apoptosis one week after induction of the scrape wound.

Results

The mean time for bridging the gap in the nontreated cells was 5.05 ± 0.33 days, and in treated cells, it took 3.35 ± 0.38 days (P <0.001). For cell cultures with scrape wounds, a mean value for BrdU incorporation of OD = 0.70 ± 0.16 was found. Whereas low-frequency pulsed electromagnetic fields treated samples showed OD = 1.58 ± 0.24 (P <0.001). However, the percentage of apoptotic cells did not differ between the two groups.

Conclusions

Our data demonstrate that low-frequency pulsed electromagnetic fields emitted by the Somagen™ device influences the in vitro wound healing of patellar tendon fibroblasts and, therefore, possibly increases wound healing potential.     

Clinical data sharing improves quality measurement and patient safety

ObjectiveAccurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and MethodsData were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.ResultsA total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.DiscussionThese results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.ConclusionsFederal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.     

Integrity of clinical information in radiology reports documenting pulmonary nodules

ObjectiveQuantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.Materials and MethodsThis Institutional Review Board-approved, retrospective cohort study was performed at an academic medical center. Natural language processing was performed on radiology reports of CT scans of chest, abdomen, or spine completed in 2016 to assess presence of pulmonary nodules, excluding patients with lung cancer, of which 300 reports were randomly sampled to form the study cohort. Documentation of nodule characteristics were manually extracted from reports by 2 authors with 20% overlap. CT images corresponding to 60 randomly selected reports were further reviewed by a thoracic radiologist to record nodule characteristics. Documentation completeness for all characteristics were reported in percentage and compared using χ² analysis. Concordance with a thoracic radiologist was reported as percentage agreement; impact on making follow-up recommendations was assessed using kappa.ResultsDocumentation completeness for pulmonary nodule characteristics differed across variables (range = 2%–90%, P < .001). Concordance with a thoracic radiologist was 75% for documenting nodule laterality and 29% for size. Follow-up recommendations were in agreement in 67% and 49% of reports when there was lack of completeness and concordance in documenting nodule size, respectively.DiscussionEssential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up.ConclusionLack of documentation of pulmonary nodule characteristics in radiology reports is common, with potential for compromising patient care and clinical decision support tools.     

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials

ObjectiveThis research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.Materials and MethodsOn June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death.ResultsThere were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event.DiscussionBy adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients.ConclusionsThis research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.     

Primary desmoplastic small round cell tumor of the duodenum

The desmoplastic small round cell tumor (DSRCT) is an extremely rare tumor that mainly affects adolescents and mostly involves the abdominal and pelvic peritoneum. A 14-year-old girl presented with intermittent epigastric pain; abdominal computed tomography and upper gastrointestinal barium X-ray revealed an 8 cm × 10 cm space-occupying mass in the duodenal region. The patient underwent pancreaticoduodenectomy and the final pathologic diagnosis was DSRCT. Although multi-agent systemic chemotherapy was given, the patient died of metastasis 8 months later. Early diagnosis and surgical treatment with adjuvant chemotherapy seems to be the best treatment choice for this disease.