Implementation Of Clinical Decision Support System Research Articles

PP145 VALIDATE Methodology For A Medication-Related Clinical Decision Support System: Innovating Or Going Back To Basics?

IntroductionIn the past decade, health technology assessment (HTA) has narrowed its scope to the analysis of mainly clinical and economic benefits. However, twenty-first century technology challenges require the need for more holistic assessments to obtain accurate recommendations for decision-making, as it was in HTA's foundations. VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology approaches complex technologies holistically to provide a deeper understanding of the problem through analysis of the heterogeneity of stakeholders’ views, allowing for more comprehensive HTAs. This study aimed to assess a pharmaceutical clinical decision support system (CDSS) using VALIDATE.MethodsA systematic review of the empirical evidence on CDSS was conducted according to PRISMA guidelines. PubMed, the Cochrane Library, and Web of Science databases were searched for literature published between 2000 and 2020. Additionally, a review of grey literature and semi-structured interviews with different hospital stakeholders (pharmacists, physicians, computer engineers, etc.) were conducted. Content analysis was used for data integration.ResultsPreliminary literature results indicated consensus regarding the effectiveness of CDSS. Nevertheless, when including multistakeholder views, CDSS appeared to not be fully accepted in clinical practice. The main reasons for this appeared to be alert fatigue and disruption of workflow. Preliminary results based on information from the literature were contrasted with stakeholder interview responses.ConclusionsIncorporation of facts and stakeholder values into the problem definition and scoping for a health technology is essential to properly conduct HTAs. The lack of an inclusive multistakeholder scoping can lead to inaccurate information, and in this particular case to suboptimal CDSS implementation concerning decision-making for the technology being evaluated.

Status of AI-Enabled Clinical Decision Support Systems Implementations in China

AI-enabled Clinical Decision Support Systems (AI + CDSSs) were heralded to contribute greatly to the advancement of health care services. There is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, understanding the actual system implementation status in clinical practice is imperative. The aim of the study is to understand (1) the current situation of AI + CDSSs clinical implementations in Chinese hospitals and (2) concerns regarding AI + CDSSs current and future implementations. We investigated 160 tertiary hospitals from six provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney U-test were utilized for analysis. Thirty-eight of the surveyed hospitals (23.75%) had implemented AI + CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI + CDSSs, p <0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as "just neutral" to "satisfied." The three most common concerns were system functions improvement and integration into the clinical process, data quality and availability, and methodological bias. While AI + CDSSs were not yet widespread in Chinese clinical settings, professionals recognize the potential benefits and challenges regarding in-hospital AI + CDSSs.

Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Intelligence amplification exploits the opportunities of artificial intelligence, which includes data analytic techniques and codified knowledge for increasing the intelligence of human decision makers. Intelligence amplification does not replace human decision makers but may help especially professionals in making complex decisions by well-designed human-AI system learning interactions (i.e., triple loop learning). To understand the adoption challenges of intelligence amplification systems, we analyse the adoption of clinical decision support systems (CDSS) as an organizational learning process by the case of a CDSS implementation for deciding on administering antibiotics to prematurely born babies. We identify user-oriented single and double loop learning processes, triple loop learning, and institutional deutero learning processes as organizational learning processes that must be realized for effective intelligence amplification adoption. We summarize these insights in a system dynamic model—containing knowledge stocks and their transformation processes—by which we analytically structure insights from the diverse studies of CDSS and intelligence amplification adoption and by which intelligence amplification projects are given an analytic theory for their design and management. From our case study, we find multiple challenges of deutero learning that influence the effectiveness of IA implementation learning as transforming tacit knowledge into explicit knowledge and explicit knowledge back to tacit knowledge. In a discussion of implications, we generate further research directions and discuss the generalization of our case findings to different organizations.

Clinical decision support system for lipid metabolism disorders: relevance and potential

Current guidelines for the management of patients with dyslipidemia are well known and easily accessible. Despite this, according to research data based on actual clinical practice, selection of optimal tactics for managing patients with dyslipidemia often causes difficulties and leads to a failure to achieve the target levels. Tools such as clinical decision support system (CDSS) can help clinicians follow current clinical guidelines, taking into account the diversity of phenotypic profiles and side effects. This review highlights the effectiveness of CDSS implementation in medical practice as a means for making decisions in managing patients with dyslipidemia, as well as presents the algorithm for CDSS for lipid metabolism disorders created by specialists of the Almazov National Medical Research Center and the University of Milan.

A Clinical Decision Support System Design Framework for Nursing Practice

AbstractBackground As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity.Objectives The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing.Methods We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case.Results We present the conceptualized CDSS with a framework comprising six interrelated components and demonstrate how each component is implemented via a hypothetical clinical case.Conclusion The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development. Next research steps include (1) working with clinical nurses to identify their knowledge resources for a particular disease to better articulate the knowledge base needed by a CDSS, (2) develop and deploy a CDSS in practice using the framework, and (3) evaluate the CDSS in the context of nursing care.

Acceptance of clinical decision support systems in Saudi healthcare organisations

Applications of clinical decision support systems (CDSS) have become essential for physicians seeking to make appropriate decisions. The implementation of CDSS, however, is complicated if the factors affecting physician’s acceptance are not recognised. This study aims to explore the various factors that may influence the acceptance of CDSS in Saudi Arabia. A qualitative method was used to collect data from interviews with 54 GPs, with interviews conducted in three stages. The study then integrated the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to communicate the findings. It is suggested that all factors of both UTAUT and TTF influence acceptance of CDSS by GPs, with the sole exception of the social influence factor. Some additional factors were also discovered by means of in-depth interviews, including accessibility, patient satisfaction, informativeness (increased knowledge), connectedness (informing patients), communication and shared knowledge, privacy and security, and perceived risk (functional performance risk and time risk). The study thus offers a new insight of the factors influencing GPs’ acceptance of CDSS.

Effectiveness of an Electronic Clinical Decision Support System in Improving the Management of Childhood Illness in Primary Care in Rural Nigeria: A Prospective Observational Study

Background and Rationale: Digital interventions can support health systems strengthening in resource-constrained settings. Clinical decision support systems (CDSS) provide evidence-based recommendations to health workers, tailored to individual patients, using clinical algorithms. Recent studies suggest that CDSS hold promise for the management of childhood illness in primary care, but evidence on effectiveness at scale is limited. We evaluated the impact of ‘ALMANACH’, a CDSS based on the Integrated Management of Childhood Illness (IMCI), on health and quality of care outcomes for children attending primary care facilities in north-eastern Nigeria. Methods In this prospective observational study, we compared caregiver-reported recovery of children (age 2-59 months) with acute illness in 45 facilities implementing ALMANACH with 44 facilities using paper-based IMCI. We collected sociodemographic and clinical information from caregivers and clinical records on Day 0, and recovery data from Day 7 phone follow-up. We calculated risk ratios and odds ratios for primary and secondary outcomes, and derived adjusted effect estimates using mixed-effects regressions. Findings We recruited 1,929 children of which 1,021 (53%) attended facilities implementing ALMANACH, in March and between July-September 2020. Caregiver-reported recovery was significantly higher among children attending ALMANACH facilities (adjusted OR=2·63, 95% CI: 1·60-4·32). We observed an increase in parenteral antimicrobial prescriptions (adjusted OR=2·42 (1·00-5·85)), and a decrease in oral antimicrobials (adjusted OR=0·40 (0·22-0·73)) in ALMANACH facilities, as well as marked increases in referral, communication of diagnosis, and follow-up advice. Interpretation Implementation of digital CDSS in primary care can improve quality of care and recovery of sick children in resource-constrained settings. The effect is likely mediated by better guideline adherence by health workers supported step-by-step through evidence-based recommendations on clinical assessment, diagnosis, treatment and referral. These findings support the use of CDSS for health systems strengthening to progress towards universal health coverage. Funding International Committee of the Red Cross. Declaration of Interest: All authors declare no competing interest. Ethical Approval: The study obtained ethical approval from the Health Research Ethics Committee of Adamawa, Nigeria (ADHREC 8/02/2020/003) and the Ethics Committee Northwest and Central Switzerland (Req-2020-00082).

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Leveraging the Hyperledger Fabric for Enhancing the Efficacy of Clinical Decision Support Systems.

Adopting and implementing the Clinical Decision Support System (CDSS) technology is a critical element in an effort to improve national quality initiatives and evidence-based practice at the point of care. CDSS is envisioned to be a potential solution to many current challenges in the healthcare sphere, which includes information overload, practice improvement, eliminating treatment errors, and reducing medical consultation costs. However, the CDSS did not manage to achieve these goals to the desired levels and provide context-appropriate alerts, although integrated with the electronic health records (EHRs) (1). Clinical decision support alerts can save lives, but frequent ones can cause increased cognitive burden to clinicians, worsen alert fatigue, and increase the duplication of tests. This ultimately increases health care costs without refining patient outcomes. Studies show that 49-96% of clinical alerts are ignored, raising questions about the effectiveness of CDSS (1). Blockchain, a decentralized, distributed digital ledger that contains a plethora of continuously updated, time-stamped, and highly encrypted virtual record, can be a key to addressing these challenges (2). The blockchain technology if integrated with the CDSS can serve as a potential solution to eliminating current drawbacks with CDSS (3). This article addresses the most significant and chronic problems facing the successful implementation of CDSS and how leveraging the Hyperledger Fabric can alleviate the clinical alert fatigue and reduce physician's burnout using patient-specific information. The proposed architecture framework for this study is designed to equip the CDSS with overall patient information at the point of care. This then empowers the physicians with the blockchain-integrated CDSS, which holds the potential to reduce clinician's cognitive burden, medical errors, and costs and ultimately enhance patient outcomes. The research study broadly discusses how the blockchain technology can be a potential solution, reasons for selecting the Hyperledger Fabric, and elaborates on how the Hyperledger Fabric can be leveraged to enhance the efficacy of CDSS.

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management.

The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.

Use of quick sequential organ failure assessment score-based sepsis clinical decision support system may be helpful to predict sepsis development

Objectives: A sepsis clinical decision support system (CDSS) can facilitate quicker sepsis detection and treatment and consequently improve outcomes. We developed a qSOFA-based sepsis CDSS and evaluated its impact on compliance with a 3-hour resuscitation bundle for patients with sepsis. Methods: This before-and-after study included consecutive adult patients with suspected infection and qSOFA scores of ≥ 2 at their emergency department (ED) presentation of a tertiary care hospital. Sepsis was defined according to the Sepsis-3 criteria. We evaluated the 3-hour resuscitation bundle compliance rate for control patients from July through August 2016, for patients using the qSOFA-based sepsis CDSS from September through December 2016, and the impact of the system using multivariable logistic regression analysis. Results: Of 306 patients with suspected infection and positive qSOFA scores at presentation, 265 patients (86.6%) developed sepsis (including 71 patients with septic shock). The 3-hour resuscitation bundle compliance rate did not differ significantly between the patients before and after the routine implementation of the qSOFA-based sepsis CDSS (63.7% vs. 52.6%; P = 0.071). Multivariate analysis showed that age (AOR [adjusted odds ratio], 1.033; P = 0.002) and body temperature (AOR, 1.677; P &lt; 0.001) were associated with bundle compliance. Conclusions: Among patients with a positive qSOFA score at presentation, sepsis developed in 86.6%, which means the qSOFA-based sepsis CDSS may be helpful; however, it was not associated with improved bundle compliance. Future quality improvement studies with multifactorial, hospital-wide approaches using sepsis CDSS tools are warranted.

Clinical Decision Support System with Renal Dose Adjustment Did Not Improve Subsequent Renal and Hepatic Function among Inpatients: The Japan Adverse Drug Event Study.

Background Medication dose adjustment is crucial for patients with renal dysfunction (RD). The assessment of renal function is generally mandatory; however, the renal function may change during the hospital stay and the manual assessment is sometimes challenging. Objective We developed the clinical decision support system (CDSS) that provided a recommended dose based on automated calculated renal function. Methods We conducted a prospective cohort study in a single teaching hospital in Japan. All hospitalized patients were included except for obstetrics/gynecology and pediatric wards between September 2013 and February 2015. The CDSS was implemented on December 2013. Renal and hepatic dysfunction (HD) were defined as changes in the estimated glomerular filtration rate (eGFR) and alanine aminotransferase or alkaline phosphatase levels based on these measurements during hospital stay. These measurements were obtained before (phase I), after (phase II), and 1 year after (phase III) the CDSS implementation. Results We included 6,767 patients (phase I: 2,205; phase II: 2,279; phase III: 2,283). The patients' characteristics were similar among phases. Changes in eGFR were similar among phases, but the incidence of RD increased in phase III (phase I: 228 [10.3%]; phase II: 260 [11.4%]; phase III: 296 [13.0%], p = 0.02). However, the differences in incidences of RD were not statistically significant after adjusting for eGFR at baseline and age. The incidences of HD were also similar among phases (phase I: 175 [13.2%]; phase II: 171 [12.9%]; phase III: 167 [12.2%], p = 0.72). Conclusion The CDSS implementation did not affect the incidence of renal and HD and changes in renal and hepatic function among hospitalized patients. The effectiveness of the CDSS with renal-guided doses should be investigated with respect to other endpoints.

Implementation of clinical decision support system for anticoagulant prescribing for patients with deep vein thrombosis

Background: Physicians adherence to recommendations for appropriate antithrombotic therapy of venous thromboembolism (VTE) can reduce the risk of recurrent VTE, pulmonary hypertension, bleeding and other adverse events. Clinical decision support systems (CDSS) are shown to increase physicians adherence to clinical guidelines.&#x0D; Aims: To assess effectivenes and safety of CDSS for anticoagulant prescribing for inhospital patients with VTE.&#x0D; Methods: A prospective cohort study was conducted in a Moscow general hospital from 06.30.2017 to 06.23.2018 to compare physicians compliance with clinical guidelines for DVT anticoagulant therapy, the rate of drug errors and direct costs of anticoagulant therapy before and after CDSS implementation (55 patients in control group and 49 in experimental group).&#x0D; Results: The rate of anticoagulant prescribing for patients with DVT did not alter significantly after CDSS implementation (96% compared with 91% before CDSS), but physicians compliance with recommendations on anticoagulant dosage increased from 32.7% to 73.5% (p = 0.0003) with corresponding decrease in the rate of anticoagulant prescribing errors from 1.35 to 0.65 per 1 patient (p = 0.0005). The length of stay and hemorrhagic complication rate did not differ between control and experimental groups. LMWH replacement with new oral anticoagulants has reduced the cost of anticoagulant therapy for 1 patient from 11.800 rubles (IQR = 7000) to 5.430 rubles (IQR = 5700) (p 0.005).&#x0D; Conclusions: СDSS can increase physicians adherence to recommended anticoagulant therapy for patients with DVT: to prevent unreasonable under-/overdosing or prolongation of anticoagulant therapy. CDSS for DVT drug therapy can be economically feasible.

Reducing unnecessary testing in the emergency department: The case for INR and aPTT.

Routine coagulation testing is rarely indicated in the emergency department. Our goal is to determine the combined effects of uncoupling routine coagulation testing (i.e., international normalized ratio [INR]; activated partial thromboplastin time [aPTT]), disseminating an educational module, and implementing a clinical decision support system (CDSS) on coagulation testing rates in two academic emergency departments. A prospective pre-post study of INR-aPTT uncoupling, educational module distribution, and CDSS implementation in two academic emergency departments. All patients ages 18 years and older undergoing evaluation and treatment during the period of August 1, 2015, to November 30, 2017, were included. Primary outcome was coagulation testing utilization during the emergency department encounter. Secondary outcomes included associated costs, frequency of downstream testing, and frequency of blood transfusions. Uncoupling INR-aPTT testing combined with educational module distribution and CDSS implementation resulted in significantly decreased coupled INR-aPTT testing, with significantly increased selective INR and aPTT testing. Overall, the aggregate rate of coagulation testing declined for both INR and aPTT testing (48 tests/100 patients/day to 26 tests/100 patients/day). There was a significant decrease in associated daily costs (median cost per day: $1048.32 v. $601.68), realizing estimated annual savings of $163,023 Canadian dollars (CAD). There was no signal of increased downstream testing or patient blood product requirements. Compared to baseline practice patterns, our multimodal initiative significantly decreased coagulation testing, with meaningful cost savings and without evidence of patient harm. Clinicians and administrators now have a growing toolkit to target the plethora of low-value tests and treatments in emergency medicine.

Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses.

To assess the effectiveness of clinical decision support systems (CDSSs) at reducing unnecessary and suboptimal antibiotic prescribing within different healthcare settings. A systematic review of published studies was undertaken with seven databases from database inception to November 2018. A protocol was developed using the PRISMA-P checklist and study selection criteria were determined prior to performing the search. Critical appraisal of studies was undertaken using relevant tools. Meta-analyses were performed using a random-effects model to determine whether CDSS use affected optimal antibiotic management. Fifty-seven studies were identified that reported on CDSS effectiveness. Most were non-randomized studies with low methodological quality. However, randomized controlled trials of moderate methodological quality were available and assessed separately. The meta-analyses indicated that appropriate antibiotic therapy was twice as likely to occur following the implementation of CDSSs (OR 2.28, 95% CI 1.82-2.86, k = 20). The use of CDSSs was also associated with a relative decrease (18%) in mortality (OR 0.82, 95% CI 0.73-0.91, k = 18). CDSS implementation also decreased the overall volume of antibiotic use, length of hospital stay, duration and cost of therapy. The magnitude of the effect did vary by study design, but the direction of the effect was consistent in favouring CDSSs. Decision support tools can be effective to improve antibiotic prescribing, although there is limited evidence available on use in primary care. Our findings suggest that a focus on system requirements and implementation processes would improve CDSS uptake and provide more definitive benefits for antibiotic stewardship.

Reducing Unnecessary Phlebotomy Testing Using a Clinical Decision Support System.

Reducing unnecessary tests reduces costs without compromising quality. We report here the effectiveness of a clinical decision support system (CDSS) on reducing unnecessary type and screen tests and describe, estimated costs, and unnecessary provider ordering. We used a pretest posttest design to examine unnecessary type and screen tests 3 months before and after CDSS implementation in a large academic medical center. The clinical decision support system appears when the test order is initiated and indicates when the last test was ordered and expires. Cost savings was estimated using time-driven activity-based costing. Provider ordering before and after the CDSS was described. There were 26,206 preintervention and 25,053 postintervention specimens. Significantly fewer unnecessary type and screen tests were ordered after the intervention (12.3%, n = 3,073) than before (14.1%, n = 3,691; p < .001) representing a 12.8% overall reduction and producing an estimated yearly savings of $142,612. Physicians had the largest weighted percentage of unnecessary orders (31.5%) followed by physician assistants (28.5%) and advanced practice nurses (11.9%). The CDSS reduced unnecessary type and screen tests and annual costs. Additional interventions directed at providers are recommended. The clinical decision support system can be used to guide all providers to make judicious decisions at the time of care.

Trends and Future Direction of the Clinical Decision Support System in Traditional Korean Medicine.

ObjectivesThe Clinical Decision Support System (CDSS), which analyzes and uses electronic health records (EHR) for medical care, pursues patient-centered medical care. It is necessary to establish the CDSS in Korean medical services for objectification and standardization. For this purpose, analyses were performed on the points to be followed for CDSS implementation with a focus on herbal medicine prescription.MethodsTo establish the CDSS in the prescription of Traditional Korean Medicine, the current prescription practices of Traditional Korean Medicine doctors were analyzed. We also analyzed whether the prescription support function of the electronic chart was implemented. A questionnaire survey was conducted querying Traditional Korean Medicine doctors working at Traditional Korean Medicine clinics and hospitals, to investigate their desired CDSS functions, and their perceived effects on herbal medicine prescription. The implementation of the CDSS among the audit software developers used by the Korean medical doctors was examined.ResultsOn average, 41.2% of Traditional Korean Medicine doctors working in Traditional Korean Medicine clinics manipulated 1 to 4 herbs, and 31.2% adjusted 4 to 7 herbs. On average, 52.5% of Traditional Korean Medicine doctors working in Traditional Korean Medicine hospitals adjusted 1 to 4 herbs, and 35.5% adjusted 4 to 7 herbs. Questioning the desired prescription support function in the electronic medical record system, the Traditional Korean Medicine doctors working at Korean medicine clinics desired information on ‘medicine name, meridian entry, flavor of medicinals, nature of medicinals, efficacy,’ ‘herb combination information’ and ‘search engine by efficacy of prescription.’ The doctors also desired compounding contraindications (eighteen antagonisms, nineteen incompatibilities) and other contraindicatory prescriptions, ‘medicine information’ and ‘prescription analysis information through basic constitution analyses.’ The implementation of prescription support function varied by clinics and hospitals.ConclusionIn order to implement and utilize the CDSS in a medical service, clinical information must be generated and managed in a standardized form. For this purpose, standardization of terminology, coding of prescriptions using a combination of herbal medicines, and unification such as the preparation method and the weights and measures should be integrated.

Design Process and Utilization of a Novel Clinical Decision Support System for Neuropathic Pain in Primary Care: Mixed Methods Observational Study.

BackgroundComputerized clinical decision support systems (CDSSs) have emerged as an approach to improve compliance of clinicians with clinical practice guidelines (CPGs). Research utilizing CDSS has primarily been conducted in clinical contexts with clear diagnostic criteria such as diabetes and cardiovascular diseases. In contrast, research on CDSS for pain management and more specifically neuropathic pain has been limited. A CDSS for neuropathic pain has the potential to enhance patient care as the challenge of diagnosing and treating neuropathic pain often leads to tension in clinician-patient relationships.ObjectiveThe aim of this study was to design and evaluate a CDSS aimed at improving the adherence of interprofessional primary care clinicians to CPG for managing neuropathic pain.MethodsRecommendations from the Canadian CPGs informed the decision pathways. The development of the CDSS format and function involved participation of multiple stakeholders and end users in needs assessment and usability testing. Clinicians, including family medicine physicians, residents, and nurse practitioners, in three academic teaching clinics were trained in the use of the CDSS. Evaluation over one year included the measurement of utilization of the CDSS; change in reported awareness, agreement, and adoption of CPG recommendations; and change in the observed adherence to CPG recommendations.ResultsThe usability testing of the CDSS was highly successful in the prototype environment. Deployment in the clinical setting was partially complete by the time of the study, with some limitations in the planned functionality. The study population had a high level of awareness, agreement, and adoption of guideline recommendations before implementation of CDSS. Nevertheless, there was a small and statistically significant improvement in the mean awareness and adoption scores over the year of observation (P=.01 for mean awareness scores at 6 and 12 months compared with baseline, for mean adoption scores at 6 months compared with baseline, and for mean adoption scores at 12 months). Documenting significant findings related to diagnosis of neuropathic pain increased significantly. Clinicians accessed CPG information more frequently than they utilized data entry functions. Nurse practitioners and first year family medicine trainees had higher utilization than physicians.ConclusionsWe observed a small increase in the adherence to CPG recommendations for managing neuropathic pain. Clinicians utilized the CDSS more as a source of knowledge and as a training tool than as an ongoing dynamic decision support.

The clinical decision support system for sepsis as an important part of the medical and economic component of a hospital

Aim: To evaluate an impact of a clinical decision support system (CDSS) in the clinical practice of a hospital on the quality and costs of treatment of patients with severe sepsis and septic shock.Materials and methods: We performed a retrospective analysis of the database in the medical information system qMS (SP.ARM, Russia, St. Petersburg) from 2015 to 2017 on 37,997 patients. In the first study period from January 2015 to June 2016, we analyzed the results of the conventional treatment regimen. From May to June 2016, the CDSS module was implemented into the qMS and the personnel was trained correspondingly. The data collected during the second study period mirrored the results of sepsis treatment with the use of CDSS. We assessed the average number of in-hospital days, duration of stay in the intensive care unit, number of septic shock cases, mortality, and treatment costs.Results: The diagnosis of sepsis was confirmed in 67 patients: in 1.4‰ (27/18,792) before the CDSS was implemented versus 2.1‰ (40/19,205) after the CDSS implementation (p &lt; 0.01). It was found that the use of CDSS integrated into the hospital medical information system reduced the number of cases of septic shock development (p &lt; 0.05). Lethality decreased by 10%. The sepsis-associated mortality showed a non-significant trend to decrease by 10% (p &lt; 0.1). The implementation of the CDSS incorporated into the qMS helped to reduce the number of septic shock cases from 26% (7/27) to 7.5% (3/40) (p &lt; 0.05). There was also a trend towards reduced duration of stay in intensive care unit, as well as towards decreased costs of sepsis treatment by 13% and efferent (extra-corporeal) treatments by 29%; however, the differences were not significant.Conclusion: The CDSS implementation for electronic monitoring of the patient's condition and changes in his/hers parameters allowed for an earlier diagnosis of sepsis. We identified some prerequisites for more rational utilization of medical resources, mainly due to early, targeted treatment of patients with severe sepsis and septic shock; however, additional studies are necessary.

Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review.

The purpose of this article is to perform a systematic review over the past 5years on the role and effectiveness of clinical decision support systems (CDSSs) on antibiotic stewardship. CDDS interventions found a significant impact on multiple outcomes relevant to antibiotic stewardship. There are various types of CDSS implementations, both active and passive (provider initiated). Passive interventions were associated with more significant outcomes; however, both interventions appeared effective. In the reviewed literature, CDSSs were consistently associated with decreasing antibiotic consumption and narrowing the spectrum of antibiotic usage. Generally, guideline adherence was improved with CDSS, although this was not universal. The effect on other outcomes, such as mortality, Clostridiodes difficile infections, length of stay, and cost, inconsistently showed a significant difference. Overall, CDDS implementation has effectively decreased antibiotic consumption and improved guideline adherence across the various types of CDSS. Other positive outcomes were noted in certain settings, but were not universal. When creating a new intervention, it is important to identify the optimal structure and deployment of a CDSS for a specific setting.