Effects and characteristics of clinical decision support systems on the outcomes of patients with kidney disease: a systematic review

OBJECTIVES

This systematic review was conducted to investigate the characteristics and effects of clinical decision support systems (CDSSs) on clinical and process-of-care outcomes of patients with kidney disease.

METHODS

A comprehensive systematic search was conducted in electronic databases to identify relevant studies published until November 2020. Randomized clinical trials evaluating the effects of using electronic CDSS on at least one clinical or process-of-care outcome in patients with kidney disease were included in this study. The characteristics of the included studies, features of CDSSs, and effects of the interventions on the outcomes were extracted. Studies were appraised for quality using the Cochrane risk-of-bias assessment tool.

RESULTS

Out of 8722 retrieved records, 11 eligible studies measured 32 outcomes, including 10 clinical outcomes and 22 process-of-care outcomes. The effects of CDSSs on 45.5% of the process-of-care outcomes were statistically significant, and all the clinical outcomes were not statistically significant. Medication-related process-of-care outcomes were the most frequently measured (54.5%), and CDSSs had the most effective and positive effect on medication appropriateness (18.2%). The characteristics of CDSSs investigated in the included studies comprised automatic data entry, real-time feedback, providing recommendations, and CDSS integration with the Computerized Provider Order Entry system.

CONCLUSION

Although CDSS may potentially be able to improve processes of care for patients with kidney disease, particularly with regard to medication appropriateness, no evidence was found that CDSS affects clinical outcomes in these patients. Further research is thus required to determine the effects of CDSSs on clinical outcomes in patients with kidney diseases.

Renally inappropriate medications in elderly outpatients and inpatients with an impaired renal function

BACKGROUND AND AIMS

The purpose of this study was to investigate differences in the frequency of renally inappropriate medications (RIMs) in outpatient and inpatient among three institutions.

METHODS

We collected prescription and renal function data for patients over 65 years of age from the drug department system. We selected 50 kinds of the most frequently used medicines which require dose adjustment according to a patient's renal function.

RESULTS

Outpatient RIM was seen in 611 cases (6.17%), and inpatient prescription RIM was seen in 317 cases (5.29%), showing a significant difference between the groups (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.02-1.35). However, in a multivariate analysis, when the renal function was included, that difference between outpatients and inpatients became insignificant (OR 1.16, 95% CI 0.98-1.37). The distribution of prescription with or without RIM in outpatient and inpatient settings depended on the CKD stage. Outpatients with a better CKD stage (stage 1-3) had a higher rate of RIM than inpatients, while patients with a worse CKD stage (stage 4 or 5) had a higher rate of RIM than outpatients.

CONCLUSION

The rate of RIM in outpatients tends to be high, and attention should be paid to RIM in inpatients with a severe CKD stage.

Scoping review exploring the impact of digital systems on processes and outcomes in the care management of acute kidney injury and progress towards establishing learning healthcare systems

OBJECTIVES

Digital systems have long been used to improve the quality and safety of care when managing acute kidney injury (AKI). The availability of digitised clinical data can also turn organisations and their networks into learning healthcare systems (LHSs) if used across all levels of health and care. This review explores the impact of digital systems i.e. on patients with AKI care, to gauge progress towards establishing LHSs and to identify existing gaps in the research.

METHODS

Embase, PubMed, MEDLINE, Cochrane, Scopus and Web of Science databases were searched. Studies of real-time or near real-time digital AKI management systems which reported process and outcome measures were included.

RESULTS

Thematic analysis of 43 studies showed that most interventions used real-time serum creatinine levels to trigger responses to enable risk prediction, early recognition of AKI or harm prevention by individual clinicians (micro level) or specialist teams (meso level). Interventions at system (macro level) were rare. There was limited evidence of change in outcomes.

DISCUSSION

While the benefits of real-time digital clinical data at micro level for AKI management have been evident for some time, their application at meso and macro levels is emergent therefore limiting progress towards establishing LHSs. Lack of progress is due to digital maturity, system design, human factors and policy levers.

CONCLUSION

Future approaches need to harness the potential of interoperability, data analytical advances and include multiple stakeholder perspectives to develop effective digital LHSs in order to gain benefits across the system.

Medication-related problems in older people in Catalonia: A real-world data study

PURPOSE

The aim of this study was to determine medication-related problems (MRPs) in primary care patients over 65 years of age.

METHODS

Cross-sectional study based on the electronic health records of patients (65-99 years of age) visited in 284 primary health care centers during 2012 in Catalonia.

VARIABLES

age, sex, sociodemographic variables, number of drugs, kidney and liver function and MRPs (duplicate therapy, drug-drug interactions, potentially inappropriate medications [PIMs] and drugs contraindicated in chronic kidney disease and in liver diseases). Unconditional logistic regression models were used to identify the factors associated with MRPs in patients with multimorbidity.

RESULTS

916 619 older people were included and 853 085 of them met the criteria for multimorbidity. Median age was 75 years and 57.7% of them were women. High percentages of MRPs were observed: PIMs (62.8%), contraindicated drugs in chronic kidney disease (12.1%), duplicate therapy (11.1%), contraindicated drugs in liver diseases (4.2%), and drug-drug interactions (1.0%). These numbers were higher in the subgroup of patients with ≥10 diseases. The most common PIMs were connected to drugs that increase the risk of fall (66.8%), antiulcer agents without criteria for gastroprotection (40.6%), and the combination of drugs with anticholinergic effects (39.7%). In the multivariate analysis, the variables associated with all MRPs among the patients with multimorbidity were the number of drugs and the number of visits.

CONCLUSIONS

The coexistence of multimorbidity and polypharmacy is associated with an elevated risk of MRPs in older people. Medication safety for older patients constitutes a pressing concern for health services.

The JAPAN-FORTA (Fit fOR The Aged) list: Consensus validation of a clinical tool to improve drug therapy in older adults

PURPOSE

Multimorbidity and subsequent polypharmacy are highly prevalent in older people. To improve inappropriate drug treatment, listing approaches such as the Beers or FORTA lists have been developed. Latter is the only clinically validated drug list issuing both positive (FORTA labels A, B) and negative (FORTA labels C, D) recommendations. Several country-specific FORTA lists have been developed to acknowledge national prescription habits, drug availabilities, and expert opinions. Here, this approach was applied to Japan.

METHODS

13 Japanese experts in geriatric pharmacotherapy participated as raters in a 2-step Delphi consensus validation of the FORTA list. The proposal of FORTA labels was based on the EURO-FORTA List and raters were asked to add, delete or re-evaluate medications, add relevant diagnoses and comments.

RESULTS

The final JAPAN-FORTA list contains 210 items aligned to 24 main indication groups. 15 items were added to the proposal and the 71 items either not used/approved in Japan or not evaluated by any rater (oncological drugs) were removed. Excluding latter, the JAPAN-FORTA list differs from the EURO-FORTA list by 23 %. Removals mainly concerned psychotropic drugs. A maximum of one label was changed per indication. The majority (96.9 percent) of the proposed FORTA labels were confirmed, only 6 labels had to be changed.

CONCLUSION

The new JAPAN-FORTA list addresses the appropriateness of drug treatment in older people in Japan. This unique listing approach issuing both positive and negative medication recommendations has been shown to improve of drug therapy in older adults and its country-specific version is now available for Japan.

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review

BACKGROUND

Laboratory and medication data in electronic health records create opportunities for clinical decision support (CDS) tools to improve medication dosing, laboratory monitoring, and detection of side effects. This systematic review evaluates the effectiveness of such tools in preventing medication-related harm.

METHODS

We followed the Laboratory Medicine Best Practice (LMBP) initiative's A-6 methodology. Searches of 6 bibliographic databases retrieved 8508 abstracts. Fifteen articles examined the effect of CDS tools on (a) appropriate dose or medication (n = 5), (b) laboratory monitoring (n = 4), (c) compliance with guidelines (n = 2), and (d) adverse drug events (n = 5). We conducted meta-analyses by using random-effects modeling.

RESULTS

We found moderate and consistent evidence that CDS tools applied at medication ordering or dispensing can increase prescriptions of appropriate medications or dosages [6 results, pooled risk ratio (RR), 1.48; 95% CI, 1.27-1.74]. CDS tools also improve receipt of recommended laboratory monitoring and appropriate treatment in response to abnormal test results (6 results, pooled RR, 1.40; 95% CI, 1.05-1.87). The evidence that CDS tools reduced adverse drug events was inconsistent (5 results, pooled RR, 0.69; 95% CI, 0.46-1.03).

CONCLUSIONS

The findings support the practice of healthcare systems with the technological capability incorporating test-based CDS tools into their computerized physician ordering systems to (a) identify and flag prescription orders of inappropriate dose or medications at the time of ordering or dispensing and (b) alert providers to missing laboratory tests for medication monitoring or results that warrant a change in treatment. More research is needed to determine the ability of these tools to prevent adverse drug events.

Early recognition of multiple sclerosis using natural language processing of the electronic health record

BACKGROUND

Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.

METHODS

An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.

RESULTS

Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.

CONCLUSIONS

Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease

OBJECTIVE

Electronic alerts are often ignored by physicians, which is partly due to the large number of unspecific alerts generated by decision support systems. The aim of the present study was to analyze critical drug prescriptions in a university-based nephrology clinic and to evaluate the effect of different alerting strategies on the alert burden.

METHODS

In a prospective observational study, two advanced strategies to automatically generate alerts were applied when medication regimens were entered for discharge letters, outpatient clinic letters, and written prescriptions and compared to two basic reference strategies. Strategy A generated alerts whenever drug-specific information was available, whereas strategy B generated alerts only when the estimated glomerular filtration rate of a patient was below a drug-specific value. Strategies C and D included further patient characteristics and drug-specific information to generate even more specific alerts.

RESULTS

Overall, 1012 medication regimens were entered during the observation period. The average number of alerts per drug preparation in medication regimens entered for letters was 0.28, 0.080, 0.019, and 0.011, when using strategy A, B, C, or D (P<0.001, for comparison between the strategies), leading to at least one alert in 87.5%, 39.3%, 13.5%, or 7.81 % of the regimens. Similar average numbers of alerts were observed for medication regimens entered for written prescriptions.

CONCLUSIONS

The prescription of potentially hazardous drugs is common in patients with renal impairment. Alerting strategies including patient and drug-specific information to generate more specific alerts have the potential to reduce the alert burden by more than 90 %.

Drug prescribing in patients with renal impairment optimized by a computer-based, semi-automated system

BACKGROUND

According to several studies, despite of the existence of several published guidelines for dosing adjustments based on renal function, inappropriate prescribing is a common drug-related problem in inpatient care.

OBJECTIVE

We developed and implemented a system for drug dosage adjustment integrated into the Hospital computer provider order entry system. This system allows pharmacists to identify patients with reduced renal function, identify medication orders that may require dosage modifications based on renal function, and generate an alert with a recommendation of specific dosage adjustment. Using the Summary of Product Characteristics and two drug databases (Micromedex 2.0® and Lexicomp®), specific dosage guidelines for drugs used in patients with renal impairment were established.

SETTING

A 264-bed tertiary teaching hospital.

METHODS

We performed a quasi-experimental, one-group, pretest-posttest study to assess the efficacy of this intervention program. We compared the differences between the frequency of appropriate orders pre- and post-test using the McNemar test.

MAIN OUTCOME MEASURES

the frequency of appropriate orders before the recommendation (pre-test) and after the recommendation (post-test).

RESULTS

Before the intervention, the frequency of appropriate prescribing based on renal function was 65 %. After the intervention, this frequency was 86 % (p < 0.001). The interventions were more frequent in the emergency department (45 %). The program required 30-45 min of pharmacist time per day. The average number of patients reviewed daily was 28. This study found that a computer-based, semi-automated drug-dosage program for renal failure patients was able to reduce the number of inappropriate orders due to renal insufficiency.

Systematic review of clinical decision support interventions with potential for inpatient cost reduction

Background

Healthcare costs are increasing rapidly and at an unsustainable rate in many countries, and inpatient hospitalizations are a significant driver of these costs. Clinical decision support (CDS) represents a promising approach to not only improve care but to reduce costs in the inpatient setting. The purpose of this study was to systematically review trials of CDS interventions with the potential to reduce inpatient costs, so as to identify promising interventions for more widespread implementation and to inform future research in this area.

Methods

To identify relevant studies, MEDLINE was searched up to July 2013. CDS intervention studies with the potential to reduce inpatient healthcare costs were identified through titles and abstracts, and full text articles were reviewed to make a final determination on inclusion. Relevant characteristics of the studies were extracted and summarized.

Results

Following a screening of 7,663 articles, 78 manuscripts were included. 78.2% of studies were controlled before-after studies, and 15.4% were randomized controlled trials. 53.8% of the studies were focused on pharmacotherapy. The majority of manuscripts were published during or after 2008. 70.5% of the studies resulted in statistically and clinically significant improvements in an explicit financial measure or a proxy financial measure. Only 12.8% of the studies directly measured the financial impact of an intervention, whereas the financial impact was inferred in the remainder of studies. Data on cost effectiveness was available for only one study.

Conclusions

Significantly more research is required on the impact of clinical decision support on inpatient costs. In particular, there is a remarkable gap in the availability of cost effectiveness studies required by policy makers and decision makers in healthcare systems.

Impact of vendor computerized physician order entry on patients with renal impairment in community hospitals

BACKGROUND

Adverse drug events (ADEs) are common among hospitalized patients with renal impairment.

OBJECTIVE

To determine whether computerized physician order entry (CPOE) systems with clinical decision support capabilities reduce the frequency of renally related ADEs in hospitals.

DESIGN, SETTING, AND PATIENTS

Quasi-experimental study of 1590 adult patients with renal impairment who were admitted to 5 community hospitals in Massachusetts from January 2005 to September 2010, preimplementation and postimplementation of CPOE.

INTERVENTION

Varying levels of clinical decision support, ranging from basic CPOE only (sites 4 and 5), rudimentary clinical decision support (sites 1 and 2), and advanced clinical decision support (site 3).

MEASUREMENTS

Primary outcome was the rate of preventable ADEs from nephrotoxic and/or renally cleared medications. Similarly, secondary outcomes were the rates of overall ADEs and potential ADEs.

KEY RESULTS

There was a 45% decrease in the rate of preventable ADEs following implementation (8.0/100 vs 4.4/100 admissions; P < 0.01), and the impact was related to the level of decision support. Basic CPOE was not associated with any significant benefit (4.6/100 vs 4.3/100 admissions; P = 0.87). There was a nonsignificant decrease in preventable ADEs with rudimentary clinical decision support (9.1/100 vs 6.4/100 admissions; P = 0.22). However, substantial reduction was seen with advanced clinical decision support (12.4/100 vs 0/100 admissions; P = 0.01). Despite these benefits, a significant increase in potential ADEs was found for all systems (55.5/100 vs 136.8/100 admissions; P < 0.01).

CONCLUSION

Vendor-developed CPOE with advanced clinical decision support can reduce the occurrence of preventable ADEs but may be associated with an increase in potential ADEs.

Inappropriate prescribing: a systematic overview of published assessment tools

BACKGROUND

Criteria to assess the appropriateness of prescriptions might serve as a helpful guideline during professional training and in daily practice, with the aim to improve a patient's pharmacotherapy.

OBJECTIVE

To create a comprehensive and structured overview of existing tools to assess inappropriate prescribing.

METHOD

Systematic literature search in Pubmed (1991-2013). The following properties of the tools were extracted and mapped in a structured way: approach (explicit, implicit), development method (consensus technique, expert panel, literature based), focused patient group, health care setting, and covered aspects of inappropriate prescribing.

RESULTS

The literature search resulted in 46 tools to assess inappropriate prescribing.Twenty-eight (61%) of 46 tools were explicit, 8 (17%) were implicit and 10 (22%) used a mixed approach. Thirty-six (78%) tools named older people as target patients and 10 (22%) tools did not specify the target age group. Four (8.5%) tools were designed to detect inappropriate prescribing in hospitalised patients, 9 (19.5%) focused on patients in ambulatory care and 6 (13%) were developed for use in long-term care. Twenty-seven (59%) tools did not specify the health care setting. Consensus methods were applied in the development of 19 tools (41%), the others were based on either simple expert panels (13; 28%) or on a literature search (11; 24%). For three tools (7%) the development method was not described.

CONCLUSION

This overview reveals the characteristics of 46 assessment tools and can serve as a summary to assist readers in choosing a tool, either for research purposes or for daily practice use.

Use of clinical decision support systems for kidney-related drug prescribing: a systematic review

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to improve kidney-related drug prescribing by supporting the appropriate initiation, modification, monitoring, or discontinuation of drug therapy.

STUDY DESIGN

Systematic review. We identified studies by searching multiple bibliographic databases (eg, MEDLINE and EMBASE), conference proceedings, and reference lists of all included studies.

SETTING & POPULATION

CDSSs used in hospital or outpatient settings for acute kidney injury and chronic kidney disease, including end-stage renal disease (chronic dialysis patients or transplant recipients).

SELECTION CRITERIA FOR STUDIES

Studies prospectively using CDSSs to aid in kidney-related drug prescribing.

INTERVENTION

Computerized or manual CDSSs.

OUTCOMES

Clinician prescribing and patient-important outcomes as reported by primary study investigators. CDSS characteristics, such as whether the system was computerized, and system setting.

RESULTS

We identified 32 studies. In 17 studies, CDSSs were computerized, and in 15 studies, they were manual pharmacist-based systems. Systems intervened by prompting for drug dosing adjustments in relation to the level of decreased kidney function (25 studies) or in response to serum drug concentrations or a clinical parameter (7 studies). They were used most in academic hospital settings. For computerized CDSSs, clinician prescribing outcomes (eg, frequency of appropriate dosing) were considered in 11 studies, with all 11 reporting statistically significant improvements. Similarly, manual CDSSs that incorporated clinician prescribing outcomes showed statistically significant improvements in 6 of 8 studies. Patient-important outcomes (eg, adverse drug events) were considered in 7 studies of computerized CDSSs, with statistically significant improvements in 2 studies. For manual CDSSs, 6 studies measured patient-important outcomes and 5 reported statistically significant improvements. Cost-savings also were reported, mostly for manual CDSSs.

LIMITATIONS

Studies were heterogeneous in design and often limited by the evaluation method used. Benefits of CDSSs may be reported selectively in this literature.

CONCLUSION

CDSSs are available for many dimensions of kidney-related drug prescribing, and results are promising. Additional high-quality evaluations will guide their optimal use.

Computerized decision support systems: improving patient safety in nephrology

Incorrect prescription and administration of medications account for a substantial proportion of medical errors in the USA, causing adverse drug events (ADEs) that result in considerable patient morbidity and enormous costs to the health-care system. Patients with chronic kidney disease or acute kidney injury often have impaired drug clearance as well as polypharmacy, and are therefore at increased risk of experiencing ADEs. Studies have demonstrated that recognition of these conditions is not uniform among treating physicians, and prescribed drug doses are often incorrect. Early interventions that ensure appropriate drug dosing in this group of patients have shown encouraging results. Both computerized physician order entry and clinical decision support systems have been shown to reduce the rate of ADEs. Nevertheless, these systems have been implemented at surprisingly few institutions. Economic stimulus and health-care reform legislation present a rare opportunity to refine these systems and understand how they could be implemented more widely. Failure to explore this technology could mean that the opportunity to reduce the morbidity associated with ADEs is missed.

Using technology to prevent adverse drug events in the intensive care unit

Critically ill patients are particularly susceptible to adverse drug events (ADEs) due to their rapidly changing and unstable physiology, complex therapeutic regimens, and large percentage of medications administered intravenously. There are a wide variety of technologies that can help prevent the points of failure commonly associated with ADEs (i.e., the five "Rights": right patient; right drug; right route; right dose; right frequency). These technologies are often categorized by their degree of complexity to design and engineer and the type of error they are designed to prevent. Focusing solely on the software and hardware design of technology may over- or underestimate the degree of difficulty to avoid ADEs at the bedside. Alternatively, we propose categorizing technological solutions by identifying the factors essential for success. The two major critical success factors are: 1) the degree of clinical assessment required by the clinician to appropriately evaluate and disposition the issue identified by a technology; and 2) the complexity associated with effective implementation. This classification provides a way of determining how ADE-preventing technologies in the intensive care unit can be successfully integrated into clinical practice. Although there are limited data on the effectiveness of many technologies in reducing ADEs, we will review the technologies currently available in the intensive care unit environment. We will also discuss critical success factors for implementation, common errors made during implementation, and the potential errors using these systems.