Harrison, Andrew M.

Abstract Background The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood. Objective This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR). Methods Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS. Study Eligibility Criteria Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021. Participants All physicians and nurses involved in inpatient and outpatient settings. Study Appraisal and Synthesis Methods A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs. Results Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity. Conclusion This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

Abstract Objective Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. Materials and Methods Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient’s corresponding care team. Results Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an “in-production” AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. Conclusions A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

AbstractBackgroundThe objective of this meta‐analysis of randomised controlled clinical trials (RCT) was to evaluate the effects of febuxostat on kidney function in patients with hyperuricaemia.AimsFebuxostat is a xanthine oxidase inhibitor that decreases uric acid production. Recent studies suggested the renoprotective effect of febuxostat among hyperuricaemia patients. The aim of this study was to evaluate the effects of febuxostat on kidney function in patients with hyperuricaemia.MethodsWe conducted electronic searches in PubMed, Embase and Cochrane Central Register of Controlled Trials from January 1960 to July 2019 to identify RCT that examined the effects of febuxostat in adult patients with hyperuricaemia on serum creatinine, estimated glomerular filtration rate (eGFR), albuminuria, blood pressure parameters, major cardiovascular events, diarrhoea, joint pain, stroke and arrhythmia.ResultsNine RCT with 2141 participants were included in this meta‐analysis. Compared to placebo, the febuxostat group showed a higher eGFR at 6 months with a weighted mean difference (WMD) of 2.86 mL/min/1.73 m2 (P < 0.001), as well as the end of studies (eGFR WMD 2.69 mL/min/1.73 m2, P < 0.001). There was also lower serum creatinine (SrCr WMD = −0.04 mg/dL, P < 0.001), reduction in systolic blood pressure (SBP WMD = −1.18 mmHg, P < 0.001) and diastolic blood pressure (DBP WMD = −1.14 mmHg, P = 0.04). There was no statistical difference between febuxostat and placebo in major cardiovascular events, diarrhoea, joint symptoms, stroke events and arrhythmia. Subgroup analysis among chronic kidney disease showed the febuxostat group had higher eGFR than the placebo group (eGFR WMD = 2.69 mL/min/1.73 m2, P < 0.001).ConclusionTreating hyperuricaemia with febuxostat may slow the progression of chronic kidney disease irrespective of baseline renal function without significantly associated increased risks of major cardiovascular events, diarrhoea, joint symptoms, arrhythmia and stroke, compared to placebo.

AbstractCurrently, the data on independent risk factors for the progression of kidney disease in type 2 diabetes mellitus (T2DM) patients with CKD are limited. This study aimed to investigate CKD progression in T2DM patients who have reduced kidney function with baseline estimated glomerular filtration rate (eGFRs) between 15 and 59 mL/min/1.73 m2. This study was composed of a nationwide retrospective cohort of adult T2DM patients from 831 public hospitals in Thailand during the year 2015. T2DM patients with CKD stages 3 and 4 were followed up, until development of CKD stage 5, requirement of chronic dialysis, loss to follow‐up, death, or 31 May 2018, whichever came first. Cox proportional hazard regression was utilized for analysis. A total of 8464 participants were included; 30.4% were male. The mean age was 69 ± 10 years. The mean eGFR was 45 ± 11 mL/min/1.73 m2. The incidence of CKD stage 5 or the need for chronic dialysis was 16.4 per 1000 person‐years. The annual rate of eGFR decline during a mean follow‐up of 29 months was −2.3 mL/min/1.73 m2; 14.4% had a rapid decline in eGFR. The risk factors associated with progression to CKD stage 5 or the need for chronic dialysis were diabetes duration, systolic blood pressure, serum uric acid, albuminuria, and baseline eGFR. Conversely, older age and the use of renin‐angiotensin aldosterone system blockade were associated with decreased risks for rapid CKD progression and incidence CKD stage 5 or dialysis. This study identifies multiple predictive risk factors that support a multifaceted approach to prevent progression of advanced CKD.

Abstract Objective Despite increased use of electronic health records (EHRs), the clinical impact of system downtime is unknown. Materials and Methods This retrospective matched cohort study evaluated the impact of EHR downtime episodes lasting more than 60 minutes over a 6-year study period. Patients age 18 years or older who underwent surgical procedures at least 60 minutes in duration with an inpatient stay exceeding 24 hours within the study period were eligible for inclusion. Out of 4115 patients exposed to 1 of 176 EHR downtime episodes, 4103 patients were matched to an unexposed cohort in a 1:1 ratio. Multivariable regression analysis, as well as trend analysis for effect of duration of downtime on outcomes, was performed. Results Downtime-exposed patients had operating room duration 1.1 times longer (p &lt; .001) and postoperative length of stay 1.04 times longer (p = .007) compared to unexposed patients. The 30-day mortality rates were similar between these groups (odds ratio 1.26, p &gt; .05). In trend analysis, there was no association between duration of downtime with respect to evaluated outcomes, postoperative length of stay, and 30-day mortality. Conclusion EHR downtime had no impact on 30-day mortality. Potential associations for increased postoperative length of stay and duration of time spent in the operating room were observed among downtime-exposed patients. No trend effect was observed with respect to duration of downtime and postoperative length of stay and 30-day mortality rates.

Background Electronic Health Record (EHR)-based sepsis alert systems have failed to demonstrate improvements in clinically meaningful endpoints. However, the effect of implementation barriers on the success of new sepsis alert systems is rarely explored. Objective To test the hypothesis time to severe sepsis alert acknowledgement by critical care clinicians in the ICU setting would be reduced using an EHR-based alert acknowledgement system compared to a text paging-based system. Study Design In one arm of this simulation study, real alerts for patients in the medical ICU were delivered to critical care clinicians through the EHR. In the other arm, simulated alerts were delivered through text paging. The primary outcome was time to alert acknowledgement. The secondary outcomes were a structured, mixed quantitative/qualitative survey and informal group interview. Results The alert acknowledgement rate from the severe sepsis alert system was 3% ( N = 148) and 51% ( N = 156) from simulated severe sepsis alerts through traditional text paging. Time to alert acknowledgement from the severe sepsis alert system was median 274 min ( N = 5) and median 2 min ( N = 80) from text paging. The response rate from the EHR-based alert system was insufficient to compare primary measures. However, secondary measures revealed important barriers. Conclusion Alert fatigue, interruption, human error, and information overload are barriers to alert and simulation studies in the ICU setting.