A roadmap for national action on clinical decision support

A program evaluation of the Patient CaringTouch System: A pre- and postimplementation assessment

BACKGROUND

The Patient CaringTouch System (PCTS) is an innovative, strategic and patient-centric framework developed by the Army Nurse Corps for nursing care delivery that is designed to reduce nursing care variation and improve patient and nurse outcomes.

PURPOSE

This manuscript describes a program evaluation of the PCTS.

METHODS

A pre and post design was used to describe changes in patient and nursing measures following PCTS implementation.

DISCUSSION

Overall there was a good uptake of the PCTS; however, concurrent with initiation of the PCTS, declines in staffing levels and increases in patient acuity were noted. Medication administration error rates declined, but fall with injury rates increased. Pain reassessment following pain medication administration improved, as did several aspects of the nursing practice environment. Nurses' job dissatisfaction and intent to leave increased; however, potentially preventable losses decreased.

CONCLUSIONS

The program evaluation results will be used to target areas for improvement so that the PCTS may be sustained.

The Relationship of Genetics, Nursing Practice, and Informatics Tools in 6-Mercaptopurine Dosing in Pediatric Oncology [Formula: see text]

An antileukemic agent prescribed for pediatric oncology patients during the maintenance phase of therapy for acute lymphoblastic leukemia, 6-mercaptopurine (6-MP), is highly influenced by genetic variations in the thiopurine S-methyltransferase enzyme. As such, 6-MP must be dosed so that patients with 1 or 2 inactive thiopurine S-methyltransferase alleles will not incur an increased risk for myelosuppression or other toxicities. Informatics tools such as clinical decision support systems are useful for the application of this and similar pharmacogenetics information to the realm of nursing and clinical practice for safe and effective patient care. This article will discuss pharmacogenetics and the associated use of 6-MP; present implications for nursing practice; identify informatics tools such as clinical decision support systems, which can greatly enhance the care of patients whose treatment is based on critical genetic information; and examine the relationship of genetics, nursing practice, and informatics for 6-MP dosing in pediatric oncology.

Reconciliation of SNOMED CT and domain clinical model for interoperable medical knowledge creation

Use of heterogeneous data models in hospital information systems (HIS), obstructs the integration of clinical decision support system (CDSS) with clinical workflows. The diverse concepts diminish the interoperability level among the CDSS knowledge bases and data models of HIS. Standard terminology utilization in knowledge acquisition and its reconciliation with HIS data models are the candidate solution to overcome the interoperability barrier. We propose a reconciliation model to map concepts of diverse domain clinical models (DCM) with the standard terminology. In the proposed model, the implicit and explicit semantics are complemented to the word set of the targeted DCM concepts. The inclusion of semantics, mapped the DCM concepts to the SNOMED CT concepts with high accuracy. The results showed that the system correctly mapped 95% of concepts of DCM with standard terminology SNOMED CT concepts.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Evaluating Terminologies to Enable Imaging-Related Decision Rule Sharing

Purpose: Clinical decision support tools provide recommendations based on decision rules. A fundamental challenge regarding decision rule-sharing involves inadequate expression using standard terminology. We aimed to evaluate the coverage of three standard terminologies for mapping imaging-related decision rules. Methods: 50 decision rules, randomly selected from an existing library, were mapped to Systemized Nomenclature of Medicine (SNOMED CT), Radiology Lexicon (RadLex) and International Classification of Disease (ICD-10-CM). Decision rule attributes and values were mapped to unique concepts, obtaining the best possible coverage with the fewest concepts. Manual and automated mapping using Clinical Text Analysis and Knowledge Extraction System (cTAKES) were performed. Results: Using manual mapping, SNOMED CT provided the greatest concept coverage (83%), compared to RadLex (36%) and ICD-10-CM (8%) (p<0.0001). Combined mapping had 86% concept coverage. Automated mapping achieved 85% mapping coverage vs. 94% with manual mapping (p<0.001). Conclusion: Although some gaps remain, standard terminologies provide ample coverage for mapping imaging- related evidence.

Person centered prediction of survival in population based screening program by an intelligent clinical decision support system

AIM

To survey person centered survival rate in population based screening program by an intelligent clinical decision support system.

BACKGROUND

Colorectal cancer is the most common malignancy and major cause of morbidity and mortality throughout the world. Colorectal cancer is the sixth leading cause of cancer death in Iran. In this survey, we used cosine similarity as data mining technique and intelligent system for estimating survival of at risk groups in the screening plan.

METHODS

In the first step, we determined minimum data set (MDS). MDS was approved by experts and reviewing literatures. In the second step, MDS were coded by python language and matched with cosine similarity formula. Finally, survival rate by percent was illustrated in the user interface of national intelligent system. The national intelligent system was designed in PyCharm environment.

RESULTS

Main data elements of intelligent system consist demographic information, age, referral type, risk group, recommendation and survival rate. Minimum data set related to survival comprise of clinical status, past medical history and socio-demographic information. Information of the covered population as a comprehensive database was connected to intelligent system and survival rate estimated for each patient. Mean range of survival of HNPCC patients and FAP patients were respectively 77.7% and 75.1%. Also, the mean range of the survival rate and other calculations have changed with the entry of new patients in the CRC registry by real-time.

CONCLUSION

National intelligent system monitors the entire of risk group and reports survival rates by electronic guidelines and data mining technique and also operates according to the clinical process. This web base software has a critical role in the estimation survival rate in order to health care planning.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation

Introduction:

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation.

Material and methods:

The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio.

Results:

The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment.

Conclusion:

The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges

BACKGROUND

EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls.

OBJECTIVES

This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them.

METHODS

Along standard data processing stages - extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework -, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies.

RESULTS

Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately.

CONCLUSIONS

This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.

Clinical decision support for drug related events: Moving towards better prevention

Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

A Multidisciplinary Sepsis Program Enabled by a Two-Stage Clinical Decision Support System: Factors That Influence Patient Outcomes

Sepsis is an inflammatory response triggered by infection, with risk of in-hospital mortality fueled by disease progression. Early recognition and intervention by multidisciplinary sepsis programs may reverse the inflammatory response among at-risk patient populations, potentially improving outcomes. This retrospective study of a sepsis program enabled by a 2-stage sepsis Clinical Decision Support (CDS) system sought to evaluate the program's impact, identify early indicators that may influence outcomes, and uncover opportunities for quality improvement. Data encompassed 16 527 adult hospitalizations from 2014 and 2015. Of 2108 non-intensive care unit patients screened-in by sepsis CDS, 97% patients were stratified by 177 providers. Risk of adverse outcome improved 30% from baseline to year end, with gains materializing and stabilizing at month 7 after sepsis program go-live. Early indicators likely to influence outcomes include patient age, recent hospitalization, electrolyte abnormalities, hypovolemic shock, hypoxemia, patient location when sepsis CDS activated, and specific alert patterns.

Clinician attitudes, skills, motivations and experience following the implementation of clinical decision support tools in a large dental practice

OBJECTIVE

This study assesses dental clinicians' pre- and post-implementation attitudes, skills, and experiences with three clinical decision support (CDS) tools built into the electronic health record (EHR) of a multi-specialty group dental practice.

METHODS

Electronic surveys designed to examine factors for acceptance of EHR-based CDS tools including caries management by risk assessment (CAMBRA), periodontal disease management by risk assessment (PEMBRA) and a risk assessment-based Proactive Dental Care Plan (PDCP) were distributed to all Willamette Dental Group employees at 2 time points; 3 months pre-implementation (Fall 2013) and 15 months after implementation (winter 2015). The surveys collected demographics, measures of job experience and satisfaction, and attitudes toward each CDS tool. The baseline survey response rate among clinicians was 83.1% (n = 567) and follow-up survey response rate was 63.2% (n = 508). Among the 344 clinicians who responded to both before and after surveys, 27% were general and specialist dentists, 32% were dental hygienists, and 41% were dental assistants.

RESULTS

Adherence to the CDS tools has been sustained at 98%+ since roll-out. Between baseline and follow-up, the change in mean attitude scores regarding CAMBRA reflect statistically significant improvement in formal training, knowing how to use the tools, belief in the science supporting the tools, and the usefulness of the tool to motivate patients. For PEMBRA, statistically significant improvement was found in formal training, knowing how to use the tools, belief in the science supporting the tools, with improvement also found in belief that the format and process worked well. Finally, for the PDCP, significant and positive changes were seen for every attitude and skill item scored. A strong and positive correlation with post-implementation attitudes was found with positive experiences in the work environment, whereas a negative correlation was found with workload and stress. Clinicians highly ranked a commitment to evidence-based care and sense that the tools were helping to improve patient care, health, and experience as motivations to use the tools. Peer pressure, fears about malpractice, and incentive pay were rated the lowest among the motivation factors.

CONCLUSION

This study shows that CDS tools built into the EHR can be successfully implemented in a dental practice and widely accepted by the entire clinical team. Achieving a high level of adherence to use of CDS can be done through adequate training, alignment with the mission and purpose of the organization, and is compatible with an improved work environment and clinician satisfaction.

Function-specific Design Principles for the Electronic Health Record

This practice-oriented paper provides a collection of design principles that are specific to certain functions within the electronic health record (EHR). Design principles for EHRs tend to be broad rules of thumb rather than specific and actionable because the relevant literature is organized by specific EHR functions. That is, a good amount of research has been conducted on specific functions, rather than EHRs as a whole. Based on the relevant literature, we provide design principles with underlying rationale for progress notes, problem list, consults, clinical reminders, clinical decision support, medication list, medication alerts, and medication reconciliation. This paper is meant to offer a collection of practical guidelines for designers, grounded in the academic literature, that are more actionable than broad usability heuristics. Future work should include refinement of these principles through systematic literature review and the inclusion of additional EHR functions.

Risk Stratification to Decrease Unnecessary Diagnostic Imaging for Acute Appendicitis

BACKGROUND

There has been an increase in the use of imaging modalities to diagnose appendicitis despite evidence that can help identify children at especially high or low risk of appendicitis who may not benefit. We hypothesized that the passive diffusion of a standardized care pathway (including diagnostic imaging recommendations) would improve the diagnostic workup of appendicitis by safely decreasing the use of unnecessary imaging when compared with historical controls and that an electronic, real-time decision support tool would decrease unnecessary imaging.

METHODS

We used an interrupted time series trial to compare proportions of patients who underwent diagnostic imaging (computed tomography [CT] and ultrasound) between 3 time periods: baseline historical controls, after passive diffusion of a diagnostic workup clinical pathway, and after introduction of an electronic medical record-embedded clinical decision support tool that provides point-of-care imaging recommendations (active intervention).

RESULTS

The moderate- and high-risk groups showed lower proportions of CT in the passive and active intervention time periods compared with the historical control group. Proportions of patients undergoing ultrasound in all 3 risk groups showed an increase from the historical baseline. Time series analysis confirmed that time trends within any individual time period were not significant; thus, incidental secular trends over time did not appear to explain the decreased use of CT.

CONCLUSIONS

Passive and active decision support tools minimized unnecessary CT imaging; long-term effects remain an important area of study.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Workflow-driven clinical decision support for personalized oncology

BACKGROUND

The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge.

RESULTS

To address these challenges, we propose a framework enabling efficient implementation of meaningful CDS that incorporates a large variety of clinical knowledge models to bring to the clinic comprehensive solutions leveraging the latest domain knowledge. We use both literature-based models and models built within the p-medicine project using the rich datasets from clinical trials and care provided by the clinical partners. The framework is open to the biomedical community, enabling reuse of deployed models by third-party CDS implementations and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and cope with the complexity of patient management in oncology, we also support and leverage the clinical processes adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow functionality. The clinical models are embedded in the workflow models and executed at the right time, when and where the recommendations are needed in the clinical process.

CONCLUSIONS

In this paper we present our CDS framework developed in p-medicine and the CDS implementation leveraging the framework. To support complex decisions, the framework relies on clinical models that encapsulate relevant clinical knowledge. Next to assisting the decisions, this solution supports by default (through modeling and implementation of workflows) the decision processes as well and exploits the knowledge embedded in those processes.

Asthma-related emergency department use: current perspectives

Asthma is one of the most common chronic pediatric diseases. Patients with asthma often present to the emergency department for treatment for acute exacerbations. These patients may not have a primary care physician or primary care home, and thus are seeking care in the emergency department. Asthma care in the emergency department is multifaceted to treat asthma patients appropriately and provide quality care. National and international guidelines exist to help drive clinical care. Electronic and paper-based tools exist for both physicians and patients to help improve emergency, home, and preventive care. Treatment of patients with asthma should include the acute exacerbation, long-term management of controller medications, and controlling triggers in the home environment. We will address the current state of asthma research in emergency medicine in the US, and discuss some of the resources being used to help provide a medical home and improve care for patients who suffer from acute asthma exacerbations.

A 'green button' for using aggregate patient data at the point of care

Randomized controlled trials have traditionally been the gold standard against which all other sources of clinical evidence are measured. However, the cost of conducting these trials can be prohibitive. In addition, evidence from the trials frequently rests on narrow patient-inclusion criteria and thus may not generalize well to real clinical situations. Given the increasing availability of comprehensive clinical data in electronic health records (EHRs), some health system leaders are now advocating for a shift away from traditional trials and toward large-scale retrospective studies, which can use practice-based evidence that is generated as a by-product of clinical processes. Other thought leaders in clinical research suggest that EHRs should be used to lower the cost of trials by integrating point-of-care randomization and data capture into clinical processes. We believe that a successful learning health care system will require both approaches, and we suggest a model that resolves this escalating tension: a "green button" function within EHRs to help clinicians leverage aggregate patient data for decision making at the point of care. Giving clinicians such a tool would support patient care decisions in the absence of gold-standard evidence and would help prioritize clinical questions for which EHR-enabled randomization should be carried out. The privacy rule in the Health Insurance Portability and Accountability Act (HIPAA) of 1996 may require revision to support this novel use of patient data.

Use of Simulation to Study Nurses' Acceptance and Nonacceptance of Clinical Decision Support Suggestions

Our long-term goal was to ensure nurse clinical decision support works as intended before full deployment in clinical practice. As part of a broader effort, this pilot project explored factors influencing acceptance/nonacceptance of eight clinical decision support suggestions displayed in an electronic health record-based nursing plan of care software prototype. A diverse sample of 21 nurses participated in this high-fidelity clinical simulation experience and completed a questionnaire to assess reasons for accepting/not accepting the clinical decision support suggestions. Of 168 total suggestions displayed during the experiment (eight for each of the 21 nurses), 123 (73.2%) were accepted, and 45 (26.8%) were not accepted. The mode number of acceptances by nurses was seven of eight, with only two of 21 nurses accepting all. The main reason for clinical decision support acceptance was the nurse's belief that the suggestions were good for the patient (100%), with other features providing secondary reinforcement. Reasons for nonacceptance were less clear, with fewer than half of the subjects indicating low confidence in the evidence. This study provides preliminary evidence that high-quality simulation and targeted questionnaires about specific clinical decision support selections offer a cost-effective means for testing before full deployment in clinical practice.

Development of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Electronic Health Record Data

OBJECTIVE

Acute kidney injury is independently associated with poor outcomes in critically ill children. However, the main biomarker of acute kidney injury, serum creatinine, is a late marker of injury and can cause a delay in diagnosis. Our goal was to develop and validate a data-driven multivariable clinical prediction model of acute kidney injury in a general PICU using electronic health record data.

DESIGN

Derivation and validation of a prediction model using retrospective data.

PATIENTS

All patients 1 month to 21 years old admitted between May 2003 and March 2015 without acute kidney injury at admission and alive and in the ICU for at least 24 hours.

SETTING

A multidisciplinary, tertiary PICU.

INTERVENTION

The primary outcome was early acute kidney injury, which was defined as new acute kidney injury developed in the ICU within 72 hours of admission. Multivariable logistic regression was performed to derive the Pediatric Early AKI Risk Score using electronic health record data from the first 12 hours of ICU stay.

MEASUREMENTS AND MAIN RESULTS

A total of 9,396 patients were included in the analysis, of whom 4% had early acute kidney injury, and these had significantly higher mortality than those without early acute kidney injury (26% vs 3.3%; p < 0.001). Thirty-three candidate variables were tested. The final model had seven predictors and had good discrimination (area under the curve 0.84) and appropriate calibration. The model was validated in two validation sets and maintained good discrimination (area under the curves, 0.81 and 0.86).

CONCLUSION

We developed and validated the Pediatric Early AKI Risk Score, a data-driven acute kidney injury clinical prediction model that has good discrimination and calibration in a general PICU population using only electronic health record data that is objective, available in real time during the first 12 hours of ICU care and generalizable across PICUs. This prediction model was designed to be implemented in the form of an automated clinical decision support system and could be used to guide preventive, therapeutic, and research strategies.

Automated alerts and reminders targeting patients: A review of the literature

OBJECTIVE

Information technology supporting patient self-management has the potential to foster shared accountability for healthcare outcomes by improving patient adherence. There is growing interest in providing alerts and reminders to patients to improve healthcare self-management. This paper describes a literature review of automated alerts and reminders directed to patients, the technology used, and their efficacy.

METHODS

An electronic literature search was conducted in PubMed to identify relevant studies. The search produced 2418 abstracts; 175 articles underwent full-text review, of which 124 were rejected. 51 publications were included in the final analysis and coding.

RESULTS

The articles are partitioned into alerts and reminders. A summary of the analysis for the 51 included articles is provided.

CONCLUSION

Reminders and alerts are advantageous in many ways; they can be used to reach patients outside of regular clinic settings, be personalized, and there is a minimal age barrier in the efficacy of automated reminders sent to patients. As technologies and patients' proficiencies evolve, the use and dissemination of patient reminders and alerts will also change.

PRACTICE IMPLICATIONS

Automated technology may reliably assist patients to adhere to their health regimen, increase attendance rates, supplement discharge instructions, decrease readmission rates, and potentially reduce clinic costs.

Opportunities and Challenges for Adolescent Health Under the Affordable Care Act

The purpose of this commentary is to highlight some of the key policy changes under the Patient Protection and Affordable Care Act (ACA) that have the potential to improve health care services for adolescents as well as to draw attention to challenges that have yet to be addressed. This commentary stems from our prior policy research, which examined the extent to which the health care needs of adolescents were being considered in the early implementation phases of the ACA. This study was informed by a literature review and interviews with health care administrators, health policy researchers, and adolescent medicine specialists. The ACA has significantly expanded health insurance access; however, inequities in coverage and access remain. Primarily, the structure and financing of adolescent health care needs to be improved to better support the delivery of patient-centered, comprehensive care for this special population. Additionally, improvements in youths' awareness of their benefits under the ACA as well as a greater appreciation of preventive visits are critical. Furthermore, an unanticipated consequence of the ACA is that it exacerbates the risk of confidentiality breaches through explanation of benefits and electronic health records, which can compromise adolescents' access and utilization of health care services. Greater attention to improving and sustaining health promoting behaviors within the context of the ACA is critical for it to truly have a positive impact on adolescent health.

Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System

OBJECTIVES

To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs.

MATERIALS AND METHODS

We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making.

RESULTS

A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse.

CONCLUSION

Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

Primary Care Physician Designation and Response to Clinical Decision Support Reminders

Clinical decision support (CDS) has been shown to improve process outcomes, but overalerting may not produce incremental benefits. We analyzed providers’ response to preventive care reminders to determine if reminder response rates varied when a primary care provider (PCP) saw their own patients as compared with a partner’s patients. Secondary objectives were to describe variation in PCP identification in the electronic health record (EHR) across sites, and to determine its accuracy.

We retrospectively analyzed response to preventive care reminders during visits to outpatient primary care sites over a three-month period where an EHR was used. Data on clinician requests for reminders, viewing of preventive care reminders, and response rates were stratified by whether the patient visited their own PCP, the PCP’s partner, or where no PCP was listed in the EHR. We calculated the proportion of PCP identification across sites and agreement of identified PCP with an external standard.

Of 84,937 visits, 58,482 (68.9%) were with the PCP, 10,259 (12.1%) were with the PCP’s partner, and 16,196 (19.1%) had no listed PCP. Compared with PCP partner visits, visits with the patient’s PCP were associated with more requested reminders (30.9% vs 22.9%), viewed reminders (29.7% vs 20.7%), and responses to reminders (28.7% vs 12.6%), all comparisons p<0.001. Visits with no listed PCP had the lowest rates of requests, views, and responses. There was good agreement between the EHR-listed PCP and the provider seen for a plurality of visits over the last year (D = 0.917).

A PCP relationship during a visit was associated with higher use of preventive care reminders and a lack of PCP was associated with lower use of CDS. Targeting reminders to the PCP may be desirable, but further studies are needed to determine which strategy achieves better patient care outcomes.

primary care physician (PCP), clinical decision support (CDS), electronic health record (EHR), National Provider Identifier (NPI)

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

Clinical management IT system for enhanced recovery

Surgical resection has a key role for the treatment of early stage lung cancer along with certain advanced cases, and minimally invasive techniques, representatively video-assisted thoracoscopic surgery (VATS), are becoming standard for lung cancer surgery. Implementation of integrated programs which could manage the whole process of patient treatment including preoperative, intraoperative and postoperative care is thought to be essential partner for successful application of minimally invasive thoracic surgery for lung cancer treatment. Enhanced recovery after surgery (ERAS), so called "fast-track" programs pursue the adequate and efficient delivery of health care services therefore to improve postoperative outcomes and reduce medical cost. Well-organized information technology systems would be helpful to achieve the goals of ERAS without increasing the burden of budget or working staffs. Furthermore, it could contribute to create knowledge and translate to the clinical process.

Genomics in CKD: Is This the Path Forward?

Recent advances in genomics and sequencing technology have led to a better understanding of genetic risk in CKD. Genetics could account in part for racial differences in treatment response for medications including antihypertensives and immunosuppressive medications due to its correlation with ancestry. However, there is still a substantial lag between generation of this knowledge and its adoption in routine clinical care. This review summarizes the recent advances in genomics and CKD, discusses potential reasons for its underutilization, and highlights potential avenues for application of genomic information to improve clinical care and outcomes in this particularly vulnerable population.

Determining the effects and challenges of incorporating genetic testing into primary care management of hypertensive patients with African ancestry

People of African ancestry (Blacks) have increased risk of kidney failure due to numerous socioeconomic, environmental, and clinical factors. Two variants in the APOL1 gene are now thought to account for much of the racial disparity associated with hypertensive kidney failure in Blacks. However, this knowledge has not been translated into clinical care to help improve patient outcomes and address disparities. GUARDD is a randomized trial to evaluate the effects and challenges of incorporating genetic risk information into primary care. Hypertensive, non-diabetic, adults with self-reported African ancestry, without kidney dysfunction, are recruited from diverse clinical settings and randomized to undergo APOL1 genetic testing at baseline (intervention) or at one year (waitlist control). Providers are educated about genomics and APOL1. Guided by a genetic counselor, trained staff return APOL1 results to patients and provide low-literacy educational materials. Real-time clinical decision support tools alert clinicians of their patients' APOL1 results and associated risk status at the point of care. Our academic-community-clinical partnership designed a study to generate information about the impact of genetic risk information on patient care (blood pressure and renal surveillance) and on patient and provider knowledge, attitudes, beliefs, and behaviors. GUARDD will help establish the effective implementation of APOL1 risk-informed management of hypertensive patients at high risk of CKD, and will provide a robust framework for future endeavors to implement genomic medicine in diverse clinical practices. It will also add to the important dialog about factors that contribute to and may help eliminate racial disparities in kidney disease.

Periodontal Management by Risk Assessment: A Pragmatic Approach

An evidence-based periodontal disease risk assessment and diagnosis system has been developed and combined with a clinical decision support and management program to improve treatment and measure patient outcomes.

BACKGROUND

There is little agreement on a universally accepted periodontal risk assessment, periodontal diagnosis, and treatment management tool and their incorporation into dental practice to improve patient care. This article highlights the development and use of a practical periodontal management and risk assessment program that can be implemented in dental settings.

METHODS

The approach taken by Willamette Dental Group to develop a periodontal disease risk assessment, periodontal diagnosis, and treatment management tool is described using evidence-based best practices. With goals of standardized treatment interventions while maintaining personalized care and improved communication, this process is described to facilitate its incorporation into other dental settings.

CONCLUSIONS

Current electronic health records can be leveraged to enhance patient-centered care through the use of risk assessments and standardized guidelines to more effectively assess, diagnose, and treat patients to improve outcomes. Dental hygienists, and other committed providers, with their emphasis on prevention of periodontal disease can be principal drivers in creation and implementation of periodontal risk assessments and personalized treatment planning. Willamette Dental Group believes that such evidence-based tools can advance dentistry to new diagnostic and treatment standards.

Drivers Advancing Oral Health in a Large Group Dental Practice Organization

Three change drivers are being implemented to high standards of patient centric and evidence-based oral health care within the context of a large multispecialty dental group practice organization based on the commitment of the dental hygienist chief operating officer and her team.

BACKGROUND AND PURPOSE

A recent environmental scan elucidated 6 change drivers that can impact the provision of oral health care. Practitioners who can embrace and maximize aspects of these change drivers will move dentistry forward and create future opportunities. This article explains how 3 of these change drivers are being applied in a privately held, accountable risk-bearing entity that provides individualized treatment programs for more than 417,000 members. To facilitate integration of the conceptual changes related to the drivers, a multi-institutional, multidisciplinary, highly functioning collaborative work group was formed.

METHODS AND APPROACH

The document Dental Hygiene at a Crossroads for Change(1) inspired the first author, a dental hygienist in a unique position as chief operating officer of a large group practice, to pursue evidence-based organizational change and to impact the quality of patient care. This was accomplished by implementing technological advances including dental diagnosis terminology in the electronic health record, clinical decision support, standardized treatment guidelines, quality metrics, and patient engagement to improve oral health outcomes at the patient and population levels. The systems and processes used to implement 3 change drivers into a large multi-practice dental setting is presented to inform and inspire others to implement change drivers with the potential for advancing oral health.

CONCLUSIONS

Technology implementing best practices and improving patient engagement are excellent drivers to advance oral health and are an effective use of oral health care dollars. Improved oral health can be leveraged through technological advances to improve clinical practice.

eRegistries: Electronic registries for maternal and child health

BACKGROUND

The Global Roadmap for Health Measurement and Accountability sees integrated systems for health information as key to obtaining seamless, sustainable, and secure information exchanges at all levels of health systems. The Global Strategy for Women's, Children's and Adolescent's Health aims to achieve a continuum of quality of care with effective coverage of interventions. The WHO and World Bank recommend that countries focus on intervention coverage to monitor programs and progress for universal health coverage. Electronic health registries - eRegistries - represent integrated systems that secure a triple return on investments: First, effective single data collection for health workers to seamlessly follow individuals along the continuum of care and across disconnected cadres of care providers. Second, real-time public health surveillance and monitoring of intervention coverage, and third, feedback of information to individuals, care providers and the public for transparent accountability. This series on eRegistries presents frameworks and tools to facilitate the development and secure operation of eRegistries for maternal and child health.

METHODS

In this first paper of the eRegistries Series we have used WHO frameworks and taxonomy to map how eRegistries can support commonly used electronic and mobile applications to alleviate health systems constraints in maternal and child health. A web-based survey of public health officials in 64 low- and middle-income countries, and a systematic search of literature from 2005-2015, aimed to assess country capacities by the current status, quality and use of data in reproductive health registries.

RESULTS

eRegistries can offer support for the 12 most commonly used electronic and mobile applications for health. Countries are implementing health registries in various forms, the majority in transition from paper-based data collection to electronic systems, but very few have eRegistries that can act as an integrating backbone for health information. More mature country capacity reflected by published health registry based research is emerging in settings reaching regional or national scale, increasingly with electronic solutions. 66 scientific publications were identified based on 32 registry systems in 23 countries over a period of 10 years; this reflects a challenging experience and capacity gap for delivering sustainable high quality registries.

CONCLUSIONS

Registries are being developed and used in many high burden countries, but their potential benefits are far from realized as few countries have fully transitioned from paper-based health information to integrated electronic backbone systems. Free tools and frameworks exist to facilitate progress in health information for women and children.

The genomic CDS sandbox: An assessment among domain experts

Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.

eHealth: past and future perspectives

eHealth is an umbrella term incorporating any area that combines healthcare and technology to improve efficiencies and reduce costs. The ultimate goal of eHealth is to rationalize treatment selection to improve patient safety and outcomes. Telemedicine, first used in the 1920s, is the oldest form of eHealth. The introduction of broadband Internet, followed by wireless technologies, has allowed an explosion of mHealth applications within this field. Wearable technologies, such as smartwatches, are now being used for diagnostics and patient monitoring. Challenges remain to develop reusable Clinical Decision Support systems that will streamline the flow of data from clinical laboratories to point of care. This review explores the history of eHealth, and describes some of the remaining integration and implementation challenges.

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Background

Clinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment.

Methods

We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants.

Results

The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners’ perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases.

Conclusion

The cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0221-z) contains supplementary material, which is available to authorized users.

Decision support at the point of prescribing to increase formulary adherence

PURPOSE

Study results demonstrating the effectiveness of order-entry clinical decision support (CDS) alerts as a tool for enforcing therapeutic interchange are presented.

METHODS

A retrospective observational study was conducted at an academic medical center to evaluate formulary nonadherence before and after implementation of a fully electronic medical record with computerized prescriber order-entry (CPOE) technology configured to display therapeutic interchange alerts immediately on entry of orders for nonformulary agents. Formulary nonadherence (defined as the proportion of pharmacist-verified nonformulary orders to total verified orders) within eight medication classes was assessed during a six-month baseline period and two consecutive six-month periods after implementation.

RESULTS

In the 12 months after implementation of the therapeutic interchange alerts, the overall rate of formulary nonadherence decreased by 65%, from 3.5% at baseline to 1.2% during the second 6-month postintervention period (p < 0.001). The total number of verified nonformulary orders decreased from 300 at baseline to 102 during the second postintervention period. The largest decreases in formulary nonadherence were observed in the intranasal steroid drug class (the rate of nonadherent orders declined by a total of 12 percentage points) and the nonbarbiturate sedatives and hypnotics class (a 5-point decline), with significant 6- and 12-month declines also documented in four of the remaining six drug classes.

CONCLUSION

The incorporation of hard-stop CDS alerts into the CPOE system improved the overall rate of prescriber adherence to institutional therapeutic interchange protocols.

Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement

When coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms. This process generated 3,046 concepts spanning 68 domains. Evaluation against representative CDS and eCQM resources revealed approximately 50-70% concept coverage, indicating the need for continued expansion of the terminology.

Longitudinal Analysis of Computerized Alerts for Laboratory Monitoring of Post-liver Transplant Immunosuppressive Care

Post-liver transplant patients require lifelong immunosuppressive care and monitoring. Computerized alerts can aid laboratory monitoring, but it is unknown how the distribution of alerts changes over time. We describe the changes over time of the distribution of computerized alerts for laboratory monitoring of post-liver transplant immunosuppressive care. Data were collected for post-liver transplant patients transplanted and managed at Intermountain Healthcare between 2005 and 2012. Alerts were analyzed based on year triggered, time since transplantation, hospitalization status, alert type, action taken (accepted or rejected), reason given for the action taken, and narrative comments. Alerts for overdue laboratory testing became more prevalent as time since transplantation increased. There is an increased need to support monitoring for overdue laboratory testing as the time since transplantation increases. Alerts should support providers as they monitor the evolving needs of post-transplant patients over time. We identify opportunities for improving laboratory monitoring of post-liver transplant patients.

Nutrition Informatics Applications in Clinical Practice: a Systematic Review

Nutrition care and metabolic control contribute to clinical patient outcomes. Biomedical informatics applications represent a way to potentially improve quality and efficiency of nutrition management. We performed a systematic literature review to identify clinical decision support and computerized provider order entry systems used to manage nutrition care. Online research databases were searched using a specific set of keywords. Additionally, bibliographies were referenced for supplemental citations. Four independent reviewers selected sixteen studies out of 364 for review. These papers described adult and neonatal nutrition support applications, blood glucose management applications, and other nutrition applications. Overall, results indicated that computerized interventions could contribute to improved patient outcomes and provider performance. Specifically, computer systems in the clinical setting improved nutrient delivery, rates of malnutrition, weight loss, blood glucose values, clinician efficiency, and error rates. In conclusion, further investigation of informatics applications on nutritional and performance outcomes utilizing rigorous study designs is recommended.

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study

OBJECTIVE

To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis.

DESIGN

Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside.

SETTING

Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually.

PARTICIPANTS

Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented.

MAIN OUTCOME MEASURE

'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance.

RESULTS

A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification.

CONCLUSION

A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.

Provider Attitudes Toward the Implementation of Clinical Decision Support Tools in Dental Practice

PURPOSE

The objective of this paper is to assess clinical dental providers' baseline knowledge and attitudes about the implementation of three clinical decision support (CDS) tools built into the electronic health record (EHR) of a multi-specialty group dental practice.

PROCEDURES

An electronic survey designed to examine predisposing factors for acceptance of EHR-based tools, caries and periodontal disease management by risk assessment and a risk assessment-based Proactive Dental Care Plan, was distributed to all Willamette Dental Group (WDG) employees. The survey collected demographic data, along with measures of job experience and satisfaction, comfort with dental information technology, and attitudes and knowledge of each CDS tool. WDG provided data on site-level patient and financing mix, patient satisfaction data, employee role (e.g. dentist) and tenure with company. The survey was conducted 3 months prior to the rollout of the CDS tools in November 2013. The survey was distributed electronically to all WDG employees (n = 1166), of whom 58.5% (n = 682) were clinicians, located in 53 sites in Oregon, Washington and Idaho. The overall response rate was 79.8% (n = 930), with a response rate of 83.1% (n = 567) from all clinicians. Of these, 24.3% were general and specialist dentists (n = 138); 26.6% were dental hygienists (n = 151), and 49% were dental assistants (n = 278).

PRINCIPAL FINDINGS

The clinicians surveyed reported being highly amenable to implementation of the three CDS tools. Clinicians' attitudes reflected higher expected improvement in patient care and quality than in business processes due to the implementation. The clinician characteristics most strongly correlated with a positive attitude toward the CDS tool implementation (as measured on Likert scale 1 = low to 5 = high) included satisfaction with the EHR (0.499, p < 0.001), job satisfaction (0.458, p < 0.001), finding change to be exciting (0.398, p < 0.001), degree of control perceived over work (0.352, p < 0.001), and a perception of having adequate tools to get work done (0.340, p < 0.001). Higher reported frequency (scale 1 = never, 7 = always) of feeling burned out (-0.297, p < 0.001), feeling emotionally drained (-0.265, p < 0.001), and feeling work is a strain (-0.205, p < 0.001) had the greatest correlation with negative attitudes.

CONCLUSION

This is the first study to examine dental provider attitudes toward the implementation of CDS tools incorporated within an electronic health record. Provider attitudes toward CDS tools can shape the entire implementation process for better or worse. This study contributes to the literature by providing an understanding of factors related to positive attitudes at the outset of a system change and can help guide organizational administrators to better prepare their workforce and organization for adoption of evidence-based dentistry tools such as a CDS system.

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

OBJECTIVE

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

METHODS

We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin.

RESULTS

Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules.

CONCLUSION

Significant improvements in the EHR certification and implementation procedures are necessary.

Potential drug-related problems detected by electronic expert support system: physicians' views on clinical relevance

BACKGROUND

Drug-related problems cause suffering for patients and substantial costs. Multi-dose drug dispensing is a service in which patients receive their medication packed in bags with one unit for each dose occasion. The electronic expert support system (EES) is a clinical decision support system that provides alerts if potential drug-related problems are detected among a patients' current prescriptions, including drug-drug interactions, therapy duplications, high doses, drug-disease interactions, drug gender warnings, and inappropriate drugs and doses for geriatric or pediatric patients.

OBJECTIVE

The aim of the study was to explore physicians' views on the clinical relevance of alerts provided by EES. Furthermore we investigated if physicians performed any changes in drug treatment following the alerts and if there were any differences in perceived relevance and performed changes between different types of alerts and drugs.

SETTING

Two geriatric clinics and three primary care units in Sweden.

METHOD

Prescribed medications for patients (n = 254) with multi-dose drug dispensing were analyzed for potential drug-related problems using EES. For each alert, a physician assessed clinical relevance and indicated any intended action. A total of 15 physicians took part in the study. Changes in drug treatment following the alerts were later measured. The relationship between variables was analyzed using Chi square test.

MAIN OUTCOME MEASURE

Physicians' perceived clinical relevance of each alert, and changes in drug treatment following the alerts.

RESULTS

Physicians perceived 68% (502/740) of EES alerts as clinically relevant and 11% of all alerts were followed by a change in drug treatment. Clinical relevance and likelihood to make changes in drug treatment was related to the alert category and substances involved in the alert.

CONCLUSION

In most patients with multi-dose drug dispensing, EES detected potential drug-related problems, with the majority of the alerts regarded as clinically relevant and some followed by measurable changes in drug treatment.

Cost-effectiveness of clinical decision support system in improving maternal health care in Ghana

Objective

This paper investigated the cost-effectiveness of a computer-assisted Clinical Decision Support System (CDSS) in the identification of maternal complications in Ghana.

Methods

A cost-effectiveness analysis was performed in a before- and after-intervention study. Analysis was conducted from the provider’s perspective. The intervention area was the Kassena- Nankana district where computer-assisted CDSS was used by midwives in maternal care in six selected health centres. Six selected health centers in the Builsa district served as the non-intervention group, where the normal Ghana Health Service activities were being carried out.

Results

Computer-assisted CDSS increased the detection of pregnancy complications during antenatal care (ANC) in the intervention health centres (before-intervention= 9 /1,000 ANC attendance; after-intervention= 12/1,000 ANC attendance; P-value=0.010). In the intervention health centres, there was a decrease in the number of complications during labour by 1.1%, though the difference was not statistically significant (before-intervention =107/1,000 labour clients; after-intervention= 96/1,000 labour clients; P-value=0.305). Also, at the intervention health centres, the average cost per pregnancy complication detected during ANC (cost –effectiveness ratio) decreased from US$17,017.58 (before-intervention) to US$15,207.5 (after-intervention). Incremental cost –effectiveness ratio (ICER) was estimated at US$1,142. Considering only additional costs (cost of computer-assisted CDSS), cost per pregnancy complication detected was US$285.

Conclusions

Computer –assisted CDSS has the potential to identify complications during pregnancy and marginal reduction in labour complications. Implementing computer-assisted CDSS is more costly but more effective in the detection of pregnancy complications compared to routine maternal care, hence making the decision to implement CDSS very complex. Policy makers should however be guided by whether the additional benefit is worth the additional cost.