Association of decision support for hospital discharge disposition with outcomes

OBJECTIVES

To assess the association of a clinical decision support (CDS) algorithm for hospital discharge disposition with spending, readmissions, and postdischarge emergency department (ED) use.

STUDY DESIGN

A retrospective study in a cohort of fee-for-service Medicare patients 65 years or older linked to a database of patients receiving CDS.

METHODS

We evaluated (1) patients whose discharge disposition was concordant with the CDS recommendation versus those whose disposition was not and (2) patients receiving CDS for discharge disposition versus those not receiving CDS, regardless of concordance. Outcomes were spending over a 90-day episode, 90-day readmissions, and postdischarge ED utilization not associated with a readmission.

RESULTS

Analysis of concordant versus discordant cases showed decreased spending for concordant cases ($860 savings; 95% CI, $162-$1558; P = .016), a decrease in readmissions (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; P = .038), and no change in rate of postdischarge ED use (adjusted OR, 0.990; 95% CI, 0.882-1.110; P = .858). Analysis of patients receiving CDS versus not receiving CDS showed no significant difference in spending ($221 savings; 95% CI, -$115 to $557; P = .198), ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; P = .128), or readmission rate (adjusted OR, 1.004; 95% CI, 0.966-1.043; P = .840).

CONCLUSIONS

Following the recommendation of a CDS algorithm for hospital discharge disposition was associated with lower spending, fewer readmissions, and no change in ED use over a 90-day episode of care.

The Safer Prescription of Opioids Tool (SPOT): A Novel Clinical Decision Support Digital Health Platform for Opioid Conversion in Palliative and End of Life Care-A Single-Centre Pilot Study

Opioid errors are a leading cause of patient harm. Active failures in opioid dose conversion can contribute to error. Conversion is complex and is currently performed manually using tables of approximate equivalence. Apps that offer opioid dose double-checking are available but there are concerns about their accuracy and clinical validation. This study evaluated a novel opioid dose conversion app, The Safer Prescription of Opioids Tool (SPOT), a CE-marked Class I medical device, as a clinician decision support (CDS) platform. This single-centre prospective clinical utility pilot study followed a mixed methods design. Prescribers completed an initial survey exploring their current opioid prescribing practice. Thereafter prescribers used SPOT for opioid dosage conversions in parallel to their usual clinical practice, then evaluated SPOT through a survey and focus group. SPOT matched the Gold Standard result in 258 of 268 (96.3%) calculations. The 10 instances (3.7%) when SPOT did not match were due to a rounding error. Users had a statistically significant increase in confidence in prescribing opioids after using SPOT. Focus group feedback highlighted benefits in Quality Improvement and Safety when using SPOT. SPOT is a safe, reliable and validated CDS that has potential to reduce harms from opioid dosing errors.

Link-me: Protocol for a randomised controlled trial of a systematic approach to stepped mental health care in primary care

Primary care in Australia is undergoing significant reform, with a particular focus on cost-effective tailoring of mental health care to individual needs. Link-me is testing whether a patient-completed Decision Support Tool (DST), which predicts future severity of depression and anxiety symptoms and triages individuals into care accordingly, is clinically effective and cost-effective relative to usual care. The trial is set in general practices, with English-speaking patients invited to complete eligibility screening in their general practitioner's waiting room. Eligible and consenting patients will then complete the DST assessment and are randomised and stratified according to predicted symptom severity. Participants allocated to the intervention arm will receive feedback on DST responses, select treatment priorities, assess motivation to change, and receive a severity-matched treatment recommendation (information about and links to low intensity services for those with mild symptoms, or assistance from a specially trained health professional (care navigator) for those with severe symptoms). All patients allocated to the comparison arm will receive usual GP care plus attention control. Primary (psychological distress) and secondary (depression, anxiety, quality of life, days out of role) outcomes will be assessed at 6 and 12 months. Differences in outcome means between trial arms both across and within symptom severity group will be examined using intention-to-treat analyses. Within trial and modelled economic evaluations will be conducted to determine the value for money of credentials of Link-me. Findings will be reported to the Federal Government to inform how mental health services across Australia are funded and delivered in the future.

The critical need for clinical decision support systems for identification and management of teratogenic medications

OBJECTIVES

To describe the critical need for clinical decision support systems to identify and manage use of potentially teratogenic medications in women of reproductive potential in the United States.

DATA SOURCES

Medline, CINAHL Plus, Academic Search Complete, International Pharmaceutical Abstracts, and the Cochrane Library databases were searched on November 1, 2018, with the key words (teratogen* OR birth defect OR Category D OR Category X OR (pregnancy or pregnant)) AND (clinical decision support OR decision support OR electronic record) to identify primary literature published in peer-reviewed journals describing clinical decision support systems implemented in outpatient settings in the United States to promote safe prescribing and clinician counseling for teratogenic medications. A hand search of the reference lists of relevant articles, including review articles, found through this search strategy was also performed.

SUMMARY

Despite the great potential for clinical decision support to assist clinicians in minimizing inadvertent fetal exposure to potentially teratogenic medications, there were only seven primary articles meeting the criteria. The results of these studies have shown some evidence of effectiveness yet had several notable limitations. No published clinical decision system showed great success. An eighth article, published in 2017, details the design of an intervention that had been implemented but not yet evaluated.

CONCLUSION

There is a relative paucity of data regarding clinical decision support systems focused on teratogenic medications in the outpatient setting in the United States. Additional clinical decision support systems in this area need to be developed.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application

BACKGROUND

The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients.

METHOD

We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR).

RESULTS

A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria.

DISCUSSION

Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS.

CONCLUSIONS

We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

BDI personal medical assistant agents: The case of trauma tracking and alerting

Personal assistant agents can have an important role in healthcare as a smart technology to support physicians in their daily work, helping to tackle the increasing complexity of their task environment. In this paper we present and discuss a personal medical assistant agent technology for trauma documentation and management, based on the Belief-Desire-Intention (BDI) architecture. The purpose of the personal assistant agent is twofold: to assist the Trauma Team in doing precision tracking during a trauma resuscitation, so as to (automatically) produce an accurate documentation of the trauma, and to generate alerts at real-time, to be eventually displayed either on smart-glasses or room-display.

Acceptability of a decision-support electronic health record system and its impact on diabetes care goals in South Asia: a mixed-methods evaluation of the CARRS trial

AIMS

To describe physicians' acceptance of decision-support electronic health record system and its impact on diabetes care goals among people with Type 2 diabetes.

METHODS

We analysed data from participants in the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) trial, who received the study intervention (care coordinators and use of a decision-support electronic health record system; n=575) using generalized estimating equations to estimate the association between acceptance/rejection of decision-support system prompts and outcomes (mean changes in HbA_1c , blood pressure and LDL cholesterol) considering repeated measures across all time points available. We conducted in-depth interviews with physicians to understand the benefits, challenges and value of the decision-support electronic health record system and analysed physicians' interviews using Rogers' diffusion of innovation theory.

RESULTS

At end-of-trial, participants with diabetes for whom glycaemic, systolic blood pressure, diastolic blood pressure and LDL cholesterol decision-support electronic health record prompts were accepted vs rejected, experienced no reduction in HbA_1c [mean difference: -0.05 mmol/mol (95% CI -0.22, 0.13); P=0.599], but statistically significant improvements were observed for systolic blood pressure [mean difference: -11.6 mmHg (95% CI -13.9, -9.3); P ≤ 0.001], diastolic blood pressure [mean difference: -5.2 mmHg (95% CI -6.5, -3.8); P ≤ 0.001] and LDL cholesterol [mean difference: -0.7 mmol/l (95% CI -0.6, -0.8); P ≤0.001], respectively. The relative advantages and compatibility of the decision-support electronic health record system with existing clinic set-ups influenced physicians' acceptance of it. Software complexities and data entry challenges could be overcome by task-sharing.

CONCLUSION

Wider adherence to decision-support electronic health record prompts could potentially improve diabetes goal achievement, particularly when accompanied by assistance from a non-physician health worker.

Nudging physicians and patients with autopend clinical decision support to improve diabetes management

OBJECTIVES

To determine the impact on routine glycated hemoglobin (A1C) laboratory test completion of incorporating an autopend laboratory order functionality into clinical decision support, which (1) routed provider alerts to a separate electronic folder, (2) automatically populated preauthorization forms, and (3) linked the timing and content of electronic patient health maintenance topic (HMT) reminders to the provider authorization.

STUDY DESIGN

Observational pre-post study from November 2011 (1 year before autopend) through June 2014 (1.5 years after).

METHODS

The study included HMT reminders concerning an A1C test for patients with type 1 or type 2 diabetes (N = 15,630 HMT reminders; 8792 patients) in a large multispecialty ambulatory healthcare system. A Cox proportional hazard model, adjusted for patient and provider demographics, estimated the likelihood of laboratory test completion based on 3 HMT reminder characteristics: preautopend versus postautopend period, read versus unread, and the patient's time to reading.

RESULTS

In the postautopend period, the median time for patients to read reminders decreased (1 vs 3 days; P <.001) and the median time to complete laboratory tests decreased (40 vs 48 days; P <.001). Comparing preautopend HMT reminders with a similar time to reading, the likelihood of A1C laboratory test completion increased after autopend by between 21.1% (hazard ratio [HR], 1.211; P = .050), when time to reading was 57 days, and 33.9% (HR, 1.339; P = .003), when time to reading was 0 days. This result included 68% of the reminders. There was no statistical difference in A1C laboratory test completion for unread reminders in the preautopend versus postautopend period.

CONCLUSIONS

Automated patient-centered decision support can improve guideline-concordant monitoring of A1C among patients with diabetes, particularly among patients who read reminders in a timely fashion.

Role of Technology in Antimicrobial Stewardship

Because of the increasing plague of antimicrobial resistance and antibiotic misuse, antimicrobial stewardship programs (ASPs) are now a mandatory entity in all US hospitals. ASPs can use technological advances, such as the electronic medical record and clinical decision support systems, to impact a larger patient population with more efficiency. Additionally, through the use of mobile applications and social media, ASPs can highlight and propagate educational information regarding antimicrobial utilization to patients and providers in a widespread and timely manner. In this article, the authors describe how technology can play an important role in antimicrobial stewardship.

Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data

BACKGROUND

The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department.

METHODS

The CURB-65 severity score and HEART score were randomly selected from a list of the top emergency medicine CDRs. Emergency department (ED) visits with ICD-9 codes applicable to our CDRs were eligible. The charts were reviewed to determine the categorization components of the CDRs as structured and/or unstructured, median times of documentation, portion of charts with all data components documented as structured data, portion of charts with all structured CDR components documented before ED departure. A kappa score was calculated for interrater reliability.

RESULTS

The components of the CDRs were mainly documented as structured data for the CURB-65 severity score and HEART score. In the CURB-65 group, 26.8% of charts had all components documented as structured data, and 67.8% in the HEART score. Documentation of some CDR components often occurred late for both CDRs. Only 21 and 11% of patients had all CDR components documented as structured data prior to ED departure for the CURB-65 and HEART score groups, respectively. The interrater reliability for the CURB-65 score review was 0.75 and 0.65 for the HEART score.

CONCLUSION

Our study found that EHRs may be unable to automatically calculate popular CDRs-such as the CURB-65 severity score and HEART score-due to missing components and late data entry.

The Use of Clinical Decision Support in Reducing Diagnosis of and Treatment of Asymptomatic Bacteriuria

Clinical decision support (CDS) embedded within the electronic health record (EHR) is a potential antibiotic stewardship strategy for hospitalized patients. Reduction in urine testing and treating asymptomatic bacteriuria (ASB) is an important strategy to promote antibiotic stewardship. We created an intervention focused on reducing urine testing for asymptomatic patients at a large tertiary care center. The objective of this study was to design an intervention to reduce unnecessary urinalysis and urine culture (UC) orders as well as the treatment of ASB. We performed a quasiexperimental study among adult inpatients at a single academic institution. We implemented a bundled intervention, including information broadcast in newsletters, hospitalwide screensavers, and passive CDS messages in the EHR. We investigated the impact of this strategy on urinalysis, UC orders, and on the treatment of ASB by using an interrupted time series analysis. Our intervention led to reduced UC order as well as reduced antibiotic orders in response to urinalysis orders and UC results. This easily implementable bundle may play an important role as an antibiotic stewardship strategy.

The Feasibility of Sophisticated Multicriteria Support for Clinical Decisions

BACKGROUND

Multicriteria decision-making (MCDM) methods are well-suited to serve as the foundation for clinical decision support systems. To do so, however, they need to be appropriate for use in busy clinical settings. We compared decision-making processes and outcomes of patient-level analyses done with a range of multicriteria methods that vary in ease of use and intensity of decision support, 2 factors that could affect their ease of implementation into practice.

METHODS

We conducted a series of Internet surveys to compare the effects of 5 multicriteria methods that differ in user interface and required user input format on decisions regarding selection of a preferred method for lowering the risk of cardiovascular disease. The study sample consisted of members of an online Internet panel maintained by Fluidsurveys, an Internet survey company. Study outcomes were changes in preferred option, decision confidence, preparation for decision making, the Values Clarification and Decisional Uncertainty subscales of the Decisional Conflict Scale, and method ease of use.

RESULTS

The frequency of changes in the preferred option ranged from 9% to 38%, P < 0.001, and rose progressively as the level of decision support provided by the MCDM method increased. The proportion of respondents who rated the method as easy ranged from 57% to 79% and differed significantly among MCDM methods, P = 0.003, but was not consistently related to intensity of decision support or ease of use.

CONCLUSION

Decision support based on MCDM methods is not necessarily limited by decreases in ease of use. This result suggests that it is possible to develop decision support tools using sophisticated multicriteria techniques suitable for use in routine clinical care settings.

A kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney

We developed a kidney offer acceptance decision tool to predict the probability of graft survival and patient survival for first-time kidney-alone candidates after an offer is accepted or declined, and we characterized the effect of restricting the donor pool with a maximum acceptable kidney donor profile index (KDPI). For accepted offers, Cox proportional hazards models estimated these probabilities using transplanted kidneys. For declined offers, these probabilities were estimated by considering the experience of similar candidates who declined offers and the probability that declining would lead to these outcomes. We randomly selected 5000 declined offers and estimated these probabilities 3 years post-offer had the offers been accepted or declined. Predicted outcomes for declined offers were well calibrated (<3% error) with good predictive accuracy (area under the curve: graft survival, 0.69; patient survival, 0.69). Had the offers been accepted, the probabilities of graft survival and patient survival were typically higher. However, these advantages attenuated or disappeared with higher KDPI, candidate priority, and local donor supply. Donor pool restrictions were associated with worse 3-year outcomes, especially for candidates with high allocation priority. The kidney offer acceptance decision tool could inform offer acceptance by characterizing the potential risk-benefit trade-off associated with accepting or declining an offer.

"I Never Would Have Caught That Before": Pharmacist Perceptions of Using Clinical Decision Support for Antimicrobial Stewardship in the United States

To systematically improve the appropriateness of antibiotic prescribing, antimicrobial stewardship programs have been developed. There is a paucity of literature examining how pharmacists perform antimicrobial stewardship using a clinical decision support system in a hospital setting. The purpose of this qualitative study was to develop a model exploring how pharmacists perform antimicrobial stewardship to identify areas for programmatic improvement. Semistructured interviews were conducted across a health care system until saturation of themes was reached. Pharmacists identified that self-efficacy and time were vital for antimicrobial stewardship to be performed, while culture of the hospital and attitude facilitated the process of stewardship. Antimicrobial stewardship programs using clinical decision support tools should ensure pharmacists have adequate time to address rules, provide easy-to-use resources and training to support self-efficacy, and engage influential physicians to support a culture of collaboration.

The Effect of a Clinical Decision Support System on Improving Adherence to Guideline in the Treatment of Atrial Fibrillation: An Interrupted Time Series Study

To evaluate the effect of a computerized Decision Support System (CDSS) on improving adherence to an anticoagulation guideline for the treatment of atrial fibrillation (AF). This study had an interrupted time series design. The adherence to the guideline was assessed at fortnightly (two weeks) intervals from January 2016 to January 2017, 6 months before and 6 months after intervention. Newly diagnosed patients with AF were included in the offices of ten cardiologists. Stroke and major bleeding risks were calculated by the CDSS which was implemented via a mobile application. Treatment recommendations based on the guideline were shown to cardiologists. The segmented regression model was used to evaluate the effect of CDSS on level and trend of guideline adherence for the treatment of AF. In our analysis, 373 patients were included. The trend of adherence to the anticoagulation guideline for the treatment of AF was stable in the pre-intervention phase. After the CDSS intervention, mean of the adherence to the guideline significantly increased from 48% to 65.5% (P-value < 0.0001). The trend of adherence to the guideline was stable in the post-intervention phase. Our results showed that the CDSS can improve adherence to the anticoagulation guideline for the treatment of AF. Registration ID: IRCT2016052528070N1.

Economic and organizational impact of a clinical decision support system on laboratory test ordering

BACKGROUND

We studied the impact of a clinical decision support system (CDSS) implemented in a few wards of two Italian health care organizations on the ordering of redundant laboratory tests under different perspectives: (1) analysis of the volume of tests, (2) cost analysis, (3) end-user satisfaction before and after the installation of the CDSS.

METHODS

(1) and (2) were performed by comparing the ordering of laboratory tests between an intervention group of wards where a CDSS was in use and a second (control) group where a CDSS was not in use; data were compared during a 3-month period before (2014) and a 3-month period after (2015) CDSS installation. To measure end-user satisfaction (3), a questionnaire based on POESUS was administered to the medical staff.

RESULTS

After the introduction of the CDSS, the number of laboratory tests requested decreased by 16.44% and costs decreased by 16.53% in the intervention group, versus an increase in the number of tests (+3.75%) and of costs (+1.78%) in the control group. Feedback from practice showed that the medical staff was generally satisfied with the CDSS and perceived its benefits, but they were less satisfied with its technical performance in terms of slow response time.

CONCLUSIONS

The implementation of CDSSs can have a positive impact on both the efficiency of care provision and health care costs. The experience of using a CDSS can also result in good practice to be implemented by other health care organizations, considering the positive result from the first attempt to gather the point of view of end-users in Italy.

The hospital tech laboratory: quality innovation in a new era of value-conscious care

For decades, the healthcare industry has been incentivized to develop new diagnostic technologies, but this limitless progress fueled rapidly growing expenditures. With an emphasis on value, the future will favor information synthesis and processing over pure data generation, and hospitals will play a critical role in developing these systems. A Michigan Medicine, IBM, and AirStrip partnership created a robust streaming analytics platform tasked with creating predictive algorithms for critical care with the potential to support clinical decisions and deliver significant value.

Healthcare provider education to support integration of pharmacogenomics in practice: the eMERGE Network experience

Ten organizations within the Electronic Medical Records and Genomics Network developed programs to implement pharmacogenomic sequencing and clinical decision support into clinical settings. Recognizing the importance of informed prescribers, a variety of strategies were used to incorporate provider education to support implementation. Education experiences with pharmacogenomics are described within the context of each organization's prior involvement, including the scope and scale of implementation specific to their Electronic Medical Records and Genomics projects. We describe common and distinct education strategies, provide exemplars and share challenges. Lessons learned inform future perspectives. Future pharmacogenomics clinical implementation initiatives need to include funding toward implementing provider education and evaluating outcomes.

Studying Readiness for Clinical Decision Support for Worker Health Using the Rapid Assessment Process and Mixed Methods Interviews

To determine how the Rapid Assessment Process (RAP) can be adapted to evaluate the readiness of primary care clinics for acceptance and use of computerized clinical decision support (CDS) related to clinical management of working patients, we used a unique blend of ethnographic methods for gathering data. First, knowledge resources, which were prototypes of CDS content areas (diabetes, lower back pain, and asthma) containing evidence-based information, decision logic, scenarios and examples of use, were developed by subject matter experts. A team of RAP researchers then visited five clinic settings to identify barriers and facilitators to implementing CDS about the health of workers in general and the knowledge resources specifically. Methods included observations, semi-structured qualitative interviews and graphic elicitation interviews about the knowledge resources. We used both template and grounded hermeneutic approaches to data analysis. Preliminary results indicate that the methods succeeded in generating specific actionable recommendations for CDS design.

Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems

Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems.

Representation of People's Decisions in Health Information Systems.* A Complementary Approach for Understanding Health Care Systems and Population Health

OBJECTIVES

In this study, we aimed: 1) to conceptualize the theoretical challenges facing health information systems (HIS) to represent patients' decisions about health and medical treatments in everyday life; 2) to suggest approaches for modeling these processes.

METHODS

The conceptualization of the theoretical and methodological challenges was discussed in 2015 during a series of interdisciplinary meetings attended by health informatics staff, epidemiologists and health professionals working in quality management and primary and secondary prevention of chronic diseases of the Hospital Italiano de Buenos Aires, together with sociologists, anthropologists and e-health stakeholders.

RESULTS

HIS are facing the need and challenge to represent social human processes based on constructivist and complexity theories, which are the current frameworks of human sciences for understanding human learning and socio-cultural changes. Computer systems based on these theories can model processes of social construction of concrete and subjective entities and the interrelationships between them. These theories could be implemented, among other ways, through the mapping of health assets, analysis of social impact through community trials and modeling of complexity with system simulation tools.

CONCLUSIONS

This analysis suggested the need to complement the traditional linear causal explanations of disease onset (and treatments) that are the bases for models of analysis of HIS with constructivist and complexity frameworks. Both may enlighten the complex interrelationships among patients, health services and the health system. The aim of this strategy is to clarify people's decision making processes to improve the efficiency, quality and equity of the health services and the health system.

Discussion of "Representation of People's Decisions in Health Information Systems: A Complementary Approach for Understanding Health Care Systems and Population Health"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Representation of People's Decisions in Health Information Systems: A Complementary Approach for Understanding Health Care Systems and Population Health" written by Fernan Gonzalez Bernaldo de Quiros, Adriana Ruth Dawidowski, and Silvana Figar. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of de Quiros, Dawidowski, and Figar. In subsequent issues the discussion can continue through letters to the editor.

The Use of Automated SNOMED CT Clinical Coding in Clinical Decision Support Systems for Preventive Care

OBJECTIVE

The objective of this study is to discuss and analyze the use of automated SNOMED CT clinical coding in clinical decision support systems (CDSSs) for preventive care. The central question that this study seeks to answer is whether the utilization of SNOMED CT in CDSSs can improve preventive care.

METHOD

PubMed, Google Scholar, and Cochrane Library were searched for articles published in English between 2001 and 2012 on SNOMED CT, CDSS, and preventive care.

OUTCOME MEASURES

Outcome measures were the sensitivity or specificity of SNOMED CT coded data and the positive predictive value or negative predictive value of SNOMED CT coded data. Additionally, we documented the publication year, research question, study design, results, and conclusions of these studies.

RESULTS

The reviewed studies suggested that SNOMED CT successfully represents clinical terms and negated clinical terms.

CONCLUSION

The use of SNOMED CT in CDSS can be considered to provide an answer to the problem of medical errors as well as for preventive care in general. Enhancement of the modifiers and synonyms found in SNOMED CT will be necessary to improve the expected outcome of the integration of SNOMED CT with CDSS. Moreover, the application of the tree-augmented naïve (TAN) Bayesian network method can be considered the best technique to search SNOMED CT data and, consequently, to help improve preventive health services.

Physician Perceptions of Consultant Pharmacist Services Associated with an Intervention for Adverse Drug Events in the Nursing Facility

OBJECTIVE

To assess the importance and performance of consultant pharmacist services delivered before and after an intervention to detect and manage adverse drug events among nursing facility residents.

DESIGN

Before and after intervention survey of physicians participating in a randomized, controlled trial.

SETTING

Four nonprofit, academically affiliated nursing facilities.

PARTICIPANTS

Attending physicians providing nursing facility care who were randomized to intervention or control groups.

INTERVENTIONS

Within the intervention arm, consultant pharmacists provided academic detailing in which trained health care professionals visit practicing physicians in their offices and present the most up-to-date clinical information. Physicians responded to alerts from a medication monitoring system, adjudicated system alerts for adverse drug events (ADEs), and provided structured recommendations about ADE management.

MAIN OUTCOME MEASURES

We compared physicians' assessments of the importance and performance of consultant pharmacist services before and after the trial intervention in the intervention and control groups.

RESULTS

In the intervention group, ratings of importance increased for all 24 survey questions, and 5 of the changes were statistically significant (P < 0.05). In the control group, ratings of importance increased for 16 questions, and none of the changes were statistically significant. In the intervention group, ratings of performance increased for all 24 questions, and 20 of the changes were statistically significant. In the control group, ratings of performance increased for 16 questions, and none of the changes was statistically significant.

CONCLUSION

A multifaceted, consultant pharmacist-led intervention comprising academic detailing, computerized decision support, and structured communication framework can improve physicians' assessment of importance and performance of consultant pharmacist services.

ABBREVIATIONS

ADE = Adverse drug event, M = Statistically significant mean, RCT = Randomized controlled trial, SBAR = Situation, Background, Discussion, Recommendation, SD = Standard deviation.