Evaluation of 'Definite' Anaphylaxis Drug Allergy Alert Overrides in Inpatient and Outpatient Settings

INTRODUCTION

Drug-allergy interaction (DAI) alerts are generated when a known adverse sensitivity-inducing substance is prescribed. A recent study at our institution showed that providers overrode most DAI alerts, including those that warned against potentially life-threatening 'anaphylaxis'.

OBJECTIVE

The aim of this study was to determine the rate of anaphylaxis overrides, the reasons for these overrides, whether the overrides were appropriate, and if harm occurred from overrides.

METHODS

All DAI alerts, with a reaction of 'anaphylaxis', were analysed for inpatients and outpatients within our health system between January 2009 and December 2011. Only alerts that were triggered by 'definite' alerts (i.e. same ordered medication as documented allergen) were included. Patient charts were reviewed to assess the appropriateness of overrides and potential harm, according to a predetermined set of criteria.

RESULTS

A total of 202 inpatient and 16 outpatient alerts met the inclusion criteria. The rate of overrides for 'definite' anaphylaxis DAI alerts was high (inpatient: n = 93, 46.0%; outpatient: n = 11, 68.8%) but appropriate for most overrides in the inpatient (n = 78, 83.9%) and outpatient settings (n = 11, 100%). The most common override reasons in the inpatient and outpatient settings were 'administer per desensitization protocol' (n = 64, 31.7%) and 'patient does not have this allergy' (n = 7, 63.6%), respectively. No harm was associated with overrides in either setting, particularly because many medications were not administered.

CONCLUSIONS

Overrides of 'definite' anaphylaxis DAI alerts were common and often appropriate. Most overrides were due to desensitizations. Allergy reconciliation for patients could further improve critical decision support.

An evaluation of provider-chosen antibiotic indications as a targeted antimicrobial stewardship intervention

BACKGROUND

Provider-entered indications for antibiotics have been recommended as a tracking tool for antibiotic stewardship programs. The accuracy and utility of these indications are unknown.

METHODS

Drug-specific lists of evidence-based indications were integrated into an electronic health system as an ordering hard-stop. We reviewed antibiotic orders with provider-entered indications to determine whether the chosen indication matched the documentation and whether antibiotic use was appropriate.

RESULTS

One hundred fifty-five antibiotic orders were reviewed. Clinical documentation supported the entered indication in 80% of vancomycin orders, 78% of cefepime orders, and 74% of fluoroquinolone orders. The clinical appropriateness for vancomycin, cefepime, and fluoroquinolones were 94%, 100%, and 68%, respectively. When providers chose indications from the list as opposed to choosing "other" and entering free text, antibiotic orders were significantly more likely to be appropriate (odds ratio, 5.8; P = .001) but also less likely to match clinical documentation (odds ratio, 0.25; P = .0043).

DISCUSSION

Provider-chosen indications are, overall, an accurate reflection of the true reason for antibiotic use at our institution. Providers frequently documented reasons for fluoroquinolone use that were not among the provided indications.

CONCLUSION

Selecting an indication from an evidence-based list as opposed to free-text indications increases the odds that antibiotic agents will be used appropriately.

[Factors in the occurrence of medication errors and the use of order sets]

The packaging of the medication and the writing on the label can be the cause of medication errors. Errors in the intake of sodium chloride and potassium chloride have notably been due to the fact that the label of the infusion solute did not explicitly indicate the quantities present in the 500 mL and 1 000 mL bottles. Moreover, the meaning of the description comprising the term 'q.s.p. 1 000 mL' was not known by some of the nursing staff. These two factors, combined with the possibility of prescribing predefined order sets, have led to medication errors which have given rise to a study of how practices can be improved.

Transition to a new electronic health record and pediatric medication safety: lessons learned in pediatrics within a large academic health system

Objective

While the electronic health record (EHR) has become a standard of care, pediatric patients pose a unique set of risks in adult-oriented systems. We describe medication safety and implementation challenges and solutions in the pediatric population of a large academic center transitioning its EHR to Epic.

Methods

Examination of the roll-out of a new EHR in a mixed neonatal, pediatric and adult tertiary care center with staggered implementation. We followed the voluntarily reported medication error rate for the neonatal and pediatric subsets and specifically monitored the first 3 months after the roll-out of the new EHR. Data was reviewed and compiled by theme.

Results

After implementation, there was a 5-fold increase in the overall number of medication safety reports; by the third month the rate of reported medication errors had returned to baseline. The majority of reports were near misses. Three major safety themes arose: (1) enterprise logic in rounding of doses and dosing volumes; (2) ordering clinician seeing a concentration and product when ordering medications; and (3) the need for standardized dosing units through age contexts created issues with continuous infusions and pump library safeguards.

Conclusions

Future research and work need to be focused on standards and guidelines on implementing an EHR that encompasses all age contexts.

Effects of training physicians in electronic prescribing in the outpatient setting on clinical, learning and behavioural outcomes: a cluster randomized trial

AIMS

Electronic prescribing systems may improve medication safety, but only when used appropriately. The effects of task analysis-based training on clinical, learning and behavioural outcomes were evaluated in the outpatient setting, compared with the usual educational approach.

METHODS

This was a multicentre, cluster randomized trial [EDUCATional intervention for IT-mediated MEDication management (MEDUCATE trial)], with physicians as the unit of analysis. It took place in the outpatient clinics of two academic hospitals. Participants comprised specialists and residents (specialty trainees, in the UK) and their patients. Training took the form of a small-group session and an e-learning. The primary outcome was the proportion of medication discrepancies per physician, measured as discrepancies between medications registered by physicians in the electronic prescribing system and those reported by patients. Clinical consequences were estimated by the proportion of patients per physician with at least one missed drug-drug interaction with the potential for causing adverse drug events. A questionnaire assessed physicians' knowledge and skills.

RESULTS

Among 124 participating physicians, primary outcome data for 115 (93%) were available. A total of 1094 patients were included. A mean of 48% of registered medications per physician were discrepant with the medications that their patients reported in both groups (P = 0.14). Due to registration omissions, a mean of 4% of patients per physician had one or more missed drug-drug interactions with the potential to cause a clinically relevant adverse drug event in the intervention group, and 7% in controls (P = 0.11). The percentages of correct answers on the knowledge and skills test were higher in the intervention group (57%) compared with controls (51%; P = 0.01).

CONCLUSION

The training equipped outpatient physicians with the knowledge and skills for appropriate use of electronic prescribing systems, but had no effect on medication discrepancies.

Introduction of cost display reduces laboratory test utilization

OBJECTIVES

To study the effects on the number of laboratory tests ordered after introduction of cost display (showing the cost in the computerized test ordering system at test ordering and test result delivery) and cost charge (requiring all primary healthcare centers to pay full laboratory costs of the ordered tests).

STUDY DESIGN

The study included cost display for secondary healthcare centers (inpatient hospitals, emergency departments, and outpatient specialist providers) as well as publicly and privately operated primary healthcare centers (sites of nonemergency, nonspecialist healthcare). After 3 months, cost charge was introduced by management for all primary healthcare centers.

METHODS

Information on laboratory test cost was appended to the laboratory test name in the test ordering system, resulting in cost display both at the moment of test ordering and at the presentation of the test result. Numbers of laboratory tests were obtained from the laboratory information system and calculated as tests per physician visit. Cost charge was managed through the established laboratory invoicing system.

RESULTS

In the publicly operated primary healthcare centers, neither of the interventions had any effect on laboratory test volume, nor did cost display have an effect in the privately operated primary healthcare centers. However, introduction of cost charge significantly decreased laboratory test ordering in the privately operated primary healthcare centers. In contrast, secondary healthcare centers lowered test volumes when cost display was introduced.

CONCLUSIONS

The results support cost awareness and cost charge as means of reducing laboratory utilization. However, the outcome varies with the setting.

Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review

Objectives

To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.

Materials and Methods

Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well.

Results

Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P < .001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository.

Discussion and Conclusion

Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

Multidisciplinary strategy to reduce errors with the use of medical gases

OBJECTIVE

Lack of awareness of the risks associated with the use of medical  gases amongst health professionals and health organizations is concerning. The  objective of this study is to redefine the use process of medical gases in a  hospital setting.

METHOD

A sentinel event took place in a clinical unit, the incorrect administration of a medical gas to an inpatient. A multidisciplinary  causeroot analysis of the sentinel event was carried out. Different improvement points were identified for each error detected and so we defined a  good strategy to ensure the safe use of these drugs.

RESULTS

9 errors were identified and the following improvement actions were  defined: storage (gases of clinical use were separated from those of industrial  use and proper identification signs were placed), prescription (6 protocols were  included in the hospital´s Computerized Physician Order Entry software),  validation (pharmacist validation of the prescription to ensure appropriate use of  these), dispensation (a new protocol for medical gases dispensation and  transportation was designed and implemented) and administration (information  on the pressure gauges used for each type of gas was collected and reviewed).  72 Signs with recommendations for medical gases identification and  administration were placed in all the clinical units. Specific training on the safe  use of medical gases and general safety training was imparted.

CONCLUSIONS

The implementation of a process that integrates all phases of use  of medical gases and applies to all professionals involved is presented here as a  strategy to increase safety in the use of these medicines.

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit

Background

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows.

Methods

Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools).

Results

Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001).

Conclusions

The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

Thinking Together: Modeling Clinical Decision-Support as a Sociotechnical System

Computerized clinical decision-support systems are members of larger sociotechnical systems, composed of human and automated actors, who send, receive, and manipulate artifacts. Sociotechnical consideration is rare in the literature. This makes it difficult to comparatively evaluate the success of CDS implementations, and it may also indicate that sociotechnical context receives inadequate consideration in practice. To facilitate sociotechnical consideration, we developed the Thinking Together model, a flexible diagrammatical means of representing CDS systems as sociotechnical systems. To develop this model, we examined the literature with the lens of Distributed Cognition (DCog) theory. We then present two case studies of vastly different CDSSs, one almost fully automated and the other with minimal automation, to illustrate the flexibility of the Thinking Together model. We show that this model, informed by DCog and the CDS literature, are capable of supporting both research, by enabling comparative evaluation, and practice, by facilitating explicit sociotechnical planning and communication.

Physician decision making in anticoagulating atrial fibrillation: a prospective survey of a physician notification system for atrial fibrillation detected on cardiac implantable electronic devices of patients at increased risk of stroke

OBJECTIVES

The objectives of this study were to evaluate the effectiveness of a physician notification system for atrial fibrillation (AF) detected on cardiac devices, and to assess predictors of anticoagulation in patients with device-detected AF.

METHODS

In 2013, a physician notification system for AF detected on a patient's CIED [including pacemakers, implantable cardioverter defibrillators (ICD) or cardiac resynchronization therapy (CRT) devices] was implemented, with a recommendation to consider oral anticoagulation in high-risk patients. We prospectively investigated the effectiveness of this system, and evaluated both patient and physician predictors of anticoagulation, as well as factors influencing physician decision making in prescribing anticoagulation. Both uni- and multivariable analysis as well as descriptive statistics were used in the analysis.

RESULTS

We identified 177 patients with device-detected AF, 126 with a CHADS2 ⩾2. Only 41% were prescribed anticoagulation at any point within 12 months. On multivariable analysis, stroke risk as predicted by CHADS2 was not a predictor of anticoagulation. ASA use predicted a lower rate of anticoagulation (OR 0.39, 95% CI 0.16-0.97, p = 0.04); physicians in practice for <20 years were more likely to prescribe anticoagulation (OR 3.39, 95% CI 1.28-8.93, p = 0.01); and physicians who believed both cardiologist and family doctor should be involved in managing anticoagulation were more likely to prescribe anticoagulation (OR 3.28, 95% CI 1.02-10.5, p = 0.05).

CONCLUSIONS

Patients on aspirin were less likely to be anticoagulated. Physicians in practice for <20 years and who believed that both the general practitioner and cardiologist should be involved in managing anticoagulants were more likely to prescribe anticoagulation.

Healthcare IT Strategic Alignment: Challenges and Recommendations

Information technology (IT) has dramatically transformed business processes in many industries including healthcare, where electronic health records, electronic prescribing and computerized provider order entry systems have positively changed the practice of healthcare. Recently, King Faisal Specialist Hospital and Research Center, Saudi Arabia, implemented various IT systems in multiple clinical and administrative departments leading to major transformation in healthcare workflows and business processes. At the pharmacy department, many Healthcare-IT alignment challenges are still perceived. Information about challenges of strategic alignment were gathered using qualitative survey methods, through conducting semi-structures interviews, to collect opinions, experiences and suggestions. Findings were first validated, according to published literature and research work, then sorted into fourteen challenges categorized into four main areas and recommended solutions: 1) Improving organizational communication, 2) Enhancing organizational governance, 3) Specifying the alignment scope and building the architecture and 4) Developing organizational and human skills.

[Inappropriate prescribing in older adults: Critical review of the literature and safety alerts]

BACKGROUND

Prescribing errors are a risk factor for patients to present adverse events and a strategy that has been incorporated into medical care to reduce them is the use of computer tools. The objective was to obtain the scientific basis for the development of prescribing error alerts for four chronic diseases with a higher prevalence in population ≥ 65 years.

METHODS

We reviewed the literature from 2010 to 2015 to obtain information about adverse events and adverse drug reactions associated with the use of drugs for the treatment of diabetes mellitus type 2 (DM2), hypertension, osteoarticular diseases (OD) and depression; the review included these databases: PubMed, OVID, Cochrane Library, LILACS, MEDES, Portal Mayores and SIETES. A group of physicians reviewed and analyzed the papers that were identified and in a meeting they developed the alerts for the treatments used in the included diseases.

RESULTS

We obtained 76 papers, out of which 47 were analyzed by the group of physicians, who eliminated 18. With the remaining 29 were integrated 55 alerts: five for DM2, 16 for hypertension, 15 for OD and 19 for depression.

CONCLUSION

The safety alerts that were developed mainly were drug-drug interactions and adverse reactions.

Improving patient identification in an ophthalmology clinic using name alerts

OBJECTIVE

To develop a standardized process for reviewing daily patient lists and identifying potential risks of misidentification. Our goal was to develop a proactive approach to identify and eliminate risks of patient misidentification.

METHODS

Assessment of current patient identification practices took place over a period of 4 weeks. Using a process map, a patient survey was developed to determine the encounter points when patient identification was confirmed. This information was used to develop a standardized protocol for review of daily appointment lists.

RESULTS

Review of daily appointment lists was completed to identify potential similar/same name risks. A standardized manual process of chart review, flagging, and tracking was developed.

CONCLUSIONS

The name alert process resulted in a simple manual process for identifying which patients have a higher name risk and allowed care providers to take preventative action to decrease potential risk of incorrect diagnostic testing, procedure, or medication administration.

Electronic alert system for improving appropriate thromboprophylaxis in hospitalized medical patients: a randomized controlled trial

Essentials Venous thromboembolism (VTE) prophylaxis in hospitalized medical patients remains inconsistent. We implemented an electronic alert system featuring a validated risk assessment model for VTE. In this randomized controlled study, the e-alert system did not improve VTE prophylaxis. Many electronic alerts were ignored by ordering physicians.

SUMMARY

Background The use of thromboprophylaxis among acutely ill hospitalized medical patients remains inconsistent. Objective To improve thromboprophylaxis use by implementing a computer-based alert system combined with a Geneva Risk Score calculation tool in the electronic patient chart and order entry system. Patients/Methods Consecutive patients admitted to the general internal medicine wards of the University Hospital Bern, Switzerland were randomized to the alert group, in which an alert and the Geneva Risk Score calculation tool was issued in the electronic patient chart, or to the control group, in which no alert was issued. The primary endpoint was the rate of appropriate thromboprophylaxis during hospital stay. Results Overall, 1593 patients (alert group, 804; control group, 789) were eligible for analysis. The median age was 67 years (interquartile range, 53-79 years) and 47% were female. Appropriate thromboprophylaxis was administered to 536 (66.7%) patients from the alert group and to 526 (66.7%) patients from the control group. Among the 804 patients from the alert group, a total of 446 (55.5%) either had no score calculation by the physician in charge (n = 348) or had a calculated score result that was inconsistent with information from the patient chart (n = 98). Appropriate thromboprophylaxis was less often administered to patients with no score or an inconsistent score result than to 358 patients with a consistent score result (62.6% versus 71.8%). Conclusions The electronic alert (e-alert) system did not improve appropriate thromboprophylaxis, most likely because many e-alerts were ignored by ordering physicians. The use of appropriate thromboprophylaxis in the control group was higher than expected.

Redesign of computerized decision support to improve antimicrobial prescribing. A controlled before-and-after study

OBJECTIVE

To determine the impact of the introduction of new pre-written orders for antimicrobials in a computerized provider order entry (CPOE) system on 1) accuracy of documented indications for antimicrobials in the CPOE system, 2) appropriateness of antimicrobial prescribing, and 3) compliance with the hospital's antimicrobial policy. Prescriber opinions of the new decision support were also explored to determine why the redesign was effective or ineffective in altering prescribing practices.

METHODS

The study comprised two parts: a controlled pre-post study and qualitative interviews. The intervention involved the redesign of pre-written orders for half the antimicrobials so that approved indications were incorporated into pre-written orders. 555 antimicrobials prescribed before (September - October, 2013) and 534 antimicrobials prescribed after (March - April, 2015) the intervention on all general wards of a hospital were audited by study pharmacists. Eleven prescribers participated in semi-structured interviews.

RESULTS

Redesign of computerized decision support did not result in more appropriate or compliant antimicrobial prescribing, nor did it improve accuracy of indication documentation in the CPOE system (Intervention antimicrobials: appropriateness 49% vs. 50%; compliance 44% vs. 42%; accuracy 58% vs. 38%; all p>0.05). Via our interviews with prescribers we identified five main reasons for this, primarily that indications entered into the CPOE system were not monitored or followed-up, and that the antimicrobial approval process did not align well with prescriber workflow.

CONCLUSION

Redesign of pre-written orders to incorporate appropriate indications did not improve antimicrobial prescribing. Workarounds are likely when compliance with hospital policy creates additional work for prescribers or when system usability is poor. Implementation of IT, in the absence of support or follow-up, is unlikely to achieve all anticipated benefits.

Clinical decisions support malfunctions in a commercial electronic health record

OBJECTIVES

Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection.

METHODS

We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated.

RESULTS

Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44.

DISCUSSION

CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively.

CONCLUSION

CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients

BACKGROUND

Clinical decision support systems (CDSS) reduce prescription errors, but their effectiveness is reduced by high alert rates, "alert fatigue", and indiscriminate rejection.

OBJECTIVES

To compare acceptance rates of alerts generated by the SafeRx^® prescription CDSS among different alert types and departments in a tertiary care hospital, identify factors associated with alert acceptance, and determine whether alert overrides were justified.

METHODS

In a retrospective study, we compared acceptance rates of all prescription alerts generated in 2013 in 18 departments of Israel's largest tertiary care center. In a prospective study in 2 internal medicine departments, we collected data on factors potentially associated with alert override, and an expert panel evaluated the justification for each overridden alert. We used multivariate analyses to examine the association between patient and physician-related factors and alert acceptance.

RESULTS

In the retrospective study, of 390,841 prescriptions, 37.1% triggered at least one alert, 5.3% of which were accepted. Acceptance rates ranged from 7.9% for excessive dose alerts to 4.0% for duplicate drug and major drug-drug interactions alerts (p<0.001). In the prospective study, common reasons for alert overriding included "irrelevance to the specific condition" and "medication previously tolerated by the patient". Weekend shifts (incident rate ratio [IRR]=1.50 [95% CI, 1.01-2.22]) and a specific department (IRR=1.87 [1.23-2.87]) were associated with higher alert acceptance, while night shift (IRR=0.47 [0.26-0.85]) was associated with alert override. Most alert overrides (88.6%) were judged justified.

CONCLUSIONS

The vast majority of SafeRx^® alerts are overridden, and overriding is justified in most cases. Minimizing the number of alerts is essential to reduce the likelihood of developing "alert fatigue". Our findings may inform a rational, department-specific approach for alert silencing.

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.

Analysis of Daily Laboratory Orders at a Large Urban Academic Center: A Multifaceted Approach to Changing Test Ordering Patterns

OBJECTIVES

We sought to address concerns regarding recurring inpatient laboratory test order practices (daily laboratory tests) through a multifaceted approach to changing ordering patterns.

METHODS

We engaged in an interdepartmental collaboration to foster mindful test ordering through clinical policy creation, electronic clinical decision support, and continuous auditing and feedback.

RESULTS

Annualized daily order volumes decreased from approximately 25,000 to 10,000 during a 33-month postintervention review. This represented a significant change from preintervention order volumes (95% confidence interval, 0.61-0.64; P < 10-16). Total inpatient test volumes were not affected.

CONCLUSIONS

Durable changes to inpatient order practices can be achieved through a collaborative approach to utilization management that includes shared responsibility for establishing clinical guidelines and electronic decision support. Our experience suggests auditing and continued feedback are additional crucial components to changing ordering behavior. Curtailing daily orders alone may not be a sufficient strategy to reduce in-laboratory costs.

Contributions from the 2016 Literature on Clinical Decision Support

Objectives: To summarize recent research and select the best papers published in 2016 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and section editor evaluation. Results: Among the 1,145 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper describes machine learning models used to predict breast cancer multidisciplinary team decisions and compares them with two predictors based on guideline knowledge. The second paper introduces a linked-data approach for publication, discovery, and interoperability of CDSSs. The third paper assessed the variation in high-priority drug-drug interaction (DDI) alerts across 14 Electronic Health Record systems, operating in different institutions in the US. The fourth paper proposes a generic framework for modeling multiple concurrent guidelines and detecting their recommendation interactions using semantic web technologies. Conclusions: The process of identifying and selecting best papers in the domain of CDSSs demonstrated that the research in this field is very active concerning diverse dimensions, such as the types of CDSSs, e.g. guideline-based, machine-learning-based, knowledge-fusion-based, etc., and addresses challenging areas, such as the concurrent application of multiple guidelines for comorbid patients, the resolution of interoperability issues, and the evaluation of CDSSs. Nevertheless, this process also showed that CDSSs are not yet fully part of the digitalized healthcare ecosystem. Many challenges remain to be faced with regard to the evidence of their output, the dissemination of their technologies, as well as their adoption for better and safer healthcare delivery.