Computerized Decision Support for Medication Dosing in Renal Insufficiency: A Randomized, Controlled Trial

doi:10.1016/j.annemergmed.2010.03.025

Annals of Emergency Medicine

Volume 56, Issue 6, December 2010, Pages 623-629.e2

https://doi.org/10.1016/j.annemergmed.2010.03.025 Get rights and content

Study objective

Emergency physicians prescribe several discharge medications that require dosage adjustment for patients with renal disease. The hypothesis for this research was that decision support in a computerized physician order entry system would reduce the rate of excessive medication dosing for patients with renal impairment.

Methods

This was a randomized, controlled trial in an academic emergency department (ED), in which computerized physician order entry was used to write all prescriptions for patients being discharged from the ED. The sample included 42 physicians who were randomized to the intervention (21 physicians) or control (21 physicians) group. The intervention was decision support that provided dosing recommendations for targeted medications for patients aged 18 years and older when the patient's estimated creatinine clearance level was below the threshold for dosage adjustment. The primary outcome was the proportion of targeted medications that were excessively dosed.

Results

For 2,783 (46%) of the 6,015 patient visits, the decision support had sufficient information to estimate the patient's creatinine clearance level. The average age of these patients was 46 years, 1,768 (64%) were women, and 1,523 (55%) were black. Decision support was provided 73 times to physicians in the intervention group, who excessively dosed 31 (43%) prescriptions. In comparison, control physicians excessively dosed a significantly larger proportion of medications: 34 of 46, 74% (effect size=31%; 95% confidence interval 14% to 49%; P=.001).

Conclusion

Emergency physicians often prescribed excessive doses of medications that require dosage adjustment for renal impairment. Computerized physician order entry with decision support significantly reduced excessive dosing of targeted medications.

Introduction

Emergency physicians prescribe many medications that require dosage adjustment in the presence of renal insufficiency.¹ When prescribing these medications, physicians must correctly identify which patients need dosage adjustments. Serum creatinine level is a poor marker of clinically important renal insufficiency.2, 3, 4 Its sensitivity in identifying clinically important renal impairment ranges from 32% to 60%.2, 5, 6 Instead, serum creatinine level should be used in combination with other variables to estimate creatinine clearance level, which can then be used to guide medication dosing. We recently reported the first study to test the use of computer-assisted decision support in an emergency department (ED) and found that decision support significantly reduced prescribing of potentially unsafe medications to older ED patients.⁷

Computerized decision support systems may also facilitate proper dosing of renally cleared medications. Decision support systems can estimate a patient's creatinine clearance level and make a specific dosing recommendation when a medication is prescribed that requires dosage adjustment. Although previous research supports the use of such dosage adjustment systems, the most rigorous research has been carried out using time-series study designs.8, 9 To our knowledge, there have been no randomized, controlled trials of computerized decision support to more safely determine medication dosage in patients with renal impairment. In addition, this type of computer-assisted decision support has not been tested in an ED setting.

The purpose of this study was to examine, in a randomized, controlled fashion, the extent to which computerized decision support in an ED enhances the safety of prescribing to patients with renal disease. The hypothesis was that decision support for emergency physicians in an established computerized physician order entry system would reduce excessive medication dosing for patients with clinically important renal impairment.

Section snippets

Study Design

This was a randomized controlled trial and was approved by the Indiana University institutional review board before implementation of the intervention.

Setting

The Wishard Memorial Hospital is a 450-bed, urban, public hospital on the Indiana University Medical Center campus. The Wishard Emergency Department is a Level I trauma center and has approximately 100,000 annual visits. During routine patient care, clinicians electronically write all discharge orders, including prescriptions, using a

Characteristics of Study Subjects

Figure 3 shows the flow of physicians and patients through the study. Pertinent characteristics of the 42 physicians and the 2,783 patient visits in which there was sufficient information to estimate creatinine clearance level are provided in Table 1. There were no important differences in the characteristics of intervention and control physicians or the 2 groups of patients who received their care. The average age of patients was 46 years; 1,768 (64%) were women, and 1,523 (55%) were black.

Main Results

Limitations

There are several potential limitations of the current study. First, this study was carried out in a single site and included a small sample of residents and academic emergency physicians who provide care within an established health information technology infrastructure.¹¹ Our findings may not be generalizable to other providers or to other settings. However, a survey carried out more than 7 years ago found that 35% of academic EDs use computerized prescribing systems,¹³ and we expect the

Discussion

Computer-assisted decision support resulted in a statistically significant reduction in excessive dosing of renally cleared medications in the ED. To our knowledge, this is the first randomized, controlled trial of computer-assisted decision support to reduce excessive prescribing to patients with clinically significant renal disease. There have been 3 nonrandomized studies of computerized decision support to facilitate proper medication dosing for patients with renal insufficiency or failure.8

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Cited by (53)

VALIDATE approach on medication adequacy clinical decision support systems: Holistic assessment for optimal technology adoption
2023, Health Policy and Technology
Lately, Health Technology Assessment (HTA) has narrowed its scope to analyses of mainly clinical and economic benefits. Recent challenges emphasize the need for holistic assessments to obtain accurate recommendations for decision-making considering empirical facts and stakeholder's values. VALIDATE approach considers stakeholder's views and empirical facts allowing for more comprehensive HTAs. This study frames the assessment for clinical decision support systems (CDSS) using VALIDATE.
A systematic review of the literature was conducted to gather evidence on the CDSS's effectiveness and published stakeholder perspectives. Considering the retrieved information, semi-structured interviews with stakeholders were conducted to uncover issues around CDSSs such as problem definition regarding the occurrence of medication errors, judgement of existing preventive methods and previous experiences with CDSSs, background theories regarding thoughts on future impact and personal beliefs, and barriers/facilitators for implementation.
When including multi-stakeholder views, reasons different from the literature are shown to interfere with CDSS acceptance/implementation, such as: i) Occurrence of ME (no traceability of medication taken/poor patient empowerment), ii) Technology as a tool to prevent ME (insufficient if only implemented at one point-of-care), iii) Previous experiences with CDSSs (low CDSSs development due to drug prescription being lastly digitalized in hospitals) and iv) CDSSs metrics (data inputted should be measured to control CDSSs performance).
Including multi-stakeholders views in scoping technology evaluation brings added value and new information for the assessment, resulting in more comprehensive assessment; otherwise, it can lead to inaccurate information resulting in inaccurate decisions on if, when and how to adopt CDSS.
Including varied stakeholders into health technology assessment provides a deeper understanding of what value can a technology bring in. The present study develops a framework to assess clinical decision support systems (CDSS) considering different stakeholders. They were interviewed to understand their perspective on different parts of the problem CDSSs try to solve (medication errors) and the solution itself (CDSSs). Some reasons on the interviews were different from the literature published so far, that are shown to hinder CDSS acceptance/implementation, such as: no traceability of medication taken/poor patient empowerment, CDSSs being insufficient if only implemented at one point-of-care, low CDSSs development due to drug prescription being lastly digitalized in hospitals and CDSS metrics (data inputted should be measured to control CDSSs performance). This approach can improve both implementation chances and posterior assessment of CDSSs.
Impact of a Clinical Decision Support System on Inappropriate Prescription of Glucose-lowering Agents for Patients With Renal Insufficiency in an Ambulatory Care Setting
2022, Clinical Therapeutics
Citation Excerpt :
If the dose is inappropriate, an alert window pops up to remind physicians of that patient's recommended dose and frequency. However, although some studies have indicated significant reductions in inappropriate prescriptions,11,15,17 others have found no or only partial effects.13,14,16 Some of these studies were limited to short observation periods such as a few days to a few months before and after CDSS implementation.
Inappropriate dosing of glucose-lowering drugs in patients with renal insufficiency can cause severe harm. This study evaluated the short- and long-term effects of clinical decision support systems (CDSS) on inappropriate prescriptions of glucose-lowering agents for patients with renal insufficiency in an ambulatory care setting.
This retrospective longitudinal observational study was conducted by using an electronic medical record database and the CDSS log data at Taipei Veterans General Hospital between January 1, 2015, and December 31, 2018. Outpatients who received 7 target glucose-lowering medications and had an estimated glomerular filtration rate <50 mL/min/1.73 m² were included. Inappropriate prescriptions were defined as a dose, frequency, or daily dose of target drugs that exceeded the dosing recommendations based on renal function. Inappropriate monthly rates were calculated, and the interrupted time series analysis method was used to explore the 1- and 3-year post-implementation effects of CDSS. The major outcome measurements were the level changes and the inappropriate prescription rate trend changes after renal CDSS implementation. The acceptance rates of alerts were also analyzed.
A total of 141,037 drug prescriptions were obtained during the study period. In the short-term analysis, the baseline inappropriate rate for overall medications was estimated to range from 30.54% in the first month to 27.06% in month 12. The predicted inappropriate rate 12 months after implementation was 19.35%, corresponding to an estimated 28.49% [(27.06 – 19.35)/27.06] decrease in inappropriate rate. However, after long-term analysis, the predicted inappropriate rate at the end of the study (36 months after implementation) was 18.02%. A total of 27,189 alerts were generated and 628 were accepted during the study period. Thus, after short- and long-term analysis, the overall acceptance rate was 3.06% and 2.31%, respectively.
Implementing a CDSS for renal dosing adjustment could significantly decrease the inappropriate prescription rate of glucose-lowing agents among patients with renal insufficiency in an ambulatory setting in the short term, while the long-term effect of a CDSS is limited.
Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction
2021, Kidney International Reports
Acute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.
A CNN prediction system was developed to use EHR data gathered during patients’ stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.
On a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.
A CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
Scope and Influence of Electronic Health Record–Integrated Clinical Decision Support in the Emergency Department: A Systematic Review
2019, Annals of Emergency Medicine
As electronic health records evolve, integration of computerized clinical decision support offers the promise of sorting, collecting, and presenting this information to improve patient care. We conducted a systematic review to examine the scope and influence of electronic health record–integrated clinical decision support technologies implemented in the emergency department (ED).
A literature search was conducted in 4 databases from their inception through January 18, 2018: PubMed, Scopus, the Cumulative Index of Nursing and Allied Health, and Cochrane Central. Studies were included if they examined the effect of a decision support intervention that was implemented in a comprehensive electronic health record in the ED setting. Standardized data collection forms were developed and used to abstract study information and assess risk of bias.
A total of 2,558 potential studies were identified after removal of duplicates. Of these, 42 met inclusion criteria. Common targets for clinical decision support intervention included medication and radiology ordering practices, as well as more comprehensive systems supporting diagnosis and treatment for specific disease entities. The majority of studies (83%) reported positive effects on outcomes studied. Most studies (76%) used a pre-post experimental design, with only 3 (7%) randomized controlled trials.
Numerous studies suggest that clinical decision support interventions are effective in changing physician practice with respect to process outcomes such as guideline adherence; however, many studies are small and poorly controlled. Future studies should consider the inclusion of more specific information in regard to design choices, attempt to improve on uncontrolled before-after designs, and focus on clinically relevant outcomes wherever possible.
Automated acute kidney injury alerts
2018, Kidney International
Acute kidney injury (AKI) is one of the most common and probably one of the more consequential complications of critical illnesses. Recent information indicates that it is at least partially preventable; however, progress in its prevention, management, and treatment has been hindered by the scarcity of knowledge for effective interventions, inconsistencies in clinical practices, late identification of patients at risk for or with AKI, and limitations of access to best practices for prevention and management of AKI. Growing use of electronic health records has provided a platform for computer science to engage in data mining and processing, not only for early detection of AKI but also for the development of risk-stratification strategies and computer clinical decision–support (CDS) systems. Despite promising perspectives, the literature regarding the impact of AKI electronic alerts and CDS systems has been conflicting. Some studies have reported improvement in care processes and patient outcomes, whereas others have shown no effect on clinical outcomes and yet demonstrated an increase in the use of resources. These discrepancies are thought to be due to multiple factors that may be related to technology, human factors, modes of delivery of information to clinical providers, and level of expectations regarding the impact on patient outcomes. This review appraises the current body of knowledge and provides some outlines regarding research into and clinical aspects of CDS systems for AKI.
COMBAT study – Computer based assessment and treatment – A clinical trial evaluating impact of a computerized clinical decision support tool on pain in cancer patients
2017, Scandinavian Journal of Pain
The prevalence of pain in cancer patients are relatively high and indicate inadequate pain management strategies. Therefore, it is necessary to develop new methods and to improve implementation of guidelines to assess and treat pain. The vast improvement in information technology facilitated development of a computerized symptom assessment and decision support system (CCDS) – the Combat system – which was implemented in an outpatient cancer clinic to evaluate improvement in pain management.
We conducted a controlled before-and-after study between patient cohorts in two consecutive study periods: before (n = 80) and after (n = 134) implementation of the Combat system. Patients in the first cohort completed questionnaires with the paper-and-pencil method and this data was not shown to physicians. Patients in the latter cohort completed an electronic questionnaire by using an iPad and the data were automatically transferred and presented to physicians at point of care. Additionally, the system provided computerized decision support at point of care for the physician based on the electronic questionnaires completed by the patients, an electronic CRF completed by physicians and clinical guidelines.
The Combat system did not improve pain intensity and there were no significant alterations in the prescribed dose of opiates compared to the cohort of patients managed without the Combat system.
The Combat system did not improve pain management. This may be explained by several factors, however, we consider lack of proper implementation of the CCDS in the clinic to be the most important factor. As a result, we did not manage to change the behaviour of the physicians in the clinic.
There is a need to conduct larger prospective studies to evaluate the efficacy of modern information technology to improve pain management in cancer patients. Before introducing new information technology in the clinics, it is important to have a well thought out implementation strategy.
The trial is registered at Clinialtrials.gov, number NCT01795157.

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: Provide feedback on this article at the journal's Web site, www.annemergmed.com.

: Author contributions: All authors participated in conception of the study and designing the trial. KMT, AJP, SLH, and CMC obtained research funding. KMT, AJP, CMC, PRD, and DKM supervised the conduct of the trial and data collection. AJP and SLH managed the data and analyzed the data. KMT drafted the manuscript, and all authors contributed substantially to its revision. KMT takes responsibility for the paper as a whole.

: Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article that might create any potential conflict of interest. See the Manuscript Submission Agreement in this issue for examples of specific conflicts covered by this statement. This research was supported by Dr. Terrell's Dennis W. Jahnigen Career Development Award, which is funded by the American Geriatrics Society, the John A. Hartford Foundation, and Atlantic Philanthropies Inc.

: Supervising editor: Steven M. Green, MD

: Publication dates: Available online May 8, 2010.

: Reprints not available from authors.

: Please see page 624 for the Editor's Capsule Summary of this article.

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Patient safety/original researchComputerized Decision Support for Medication Dosing in Renal Insufficiency: A Randomized, Controlled Trial

Study objective

Methods

Results

Conclusion

Introduction

Section snippets

Study Design

Setting

Characteristics of Study Subjects

Main Results

Limitations

Discussion

Am J Emerg Med

Int J Med Inform

J Am Med Inform Assoc

Micromedex Healthcare Series USP DI

Magnitude of underascertainment of impaired kidney function in older adults with normal serum creatinine

J Am Geriatr Soc

Clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratificationPart 5. Evaluation of laboratory measurements for clinical assessment of kidney disease. Guideline 4. Estimation of GFR

Am J Kidney Dis

Assessing kidney function—measured and estimated glomerular filtration rate

N Engl J Med

Patient safety/original research
Computerized Decision Support for Medication Dosing in Renal Insufficiency: A Randomized, Controlled Trial