A roadmap for national action on clinical decision support

Adverse drug reactions in hospital and ambulatory care settings identified using a large administrative database

BACKGROUND

Previous studies are limited in sample size and number of sites for the detection and characterization of adverse drug reactions (ADRs) in ambulatory care and hospital settings.

OBJECTIVE

To determine the prevalence and distribution of suspected ADRs according to demographic characteristics and drug classes for ambulatory care and hospitalized patients.

METHODS

A cross-sectional evaluation of administrative data from 2002-2005, containing a maximum of 20 million Medicare and commercially insured patients in a year, was completed. Individuals with one or more claims suggesting an ADR were identified, using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) criteria referred to as a "suspected ADR." Frequency of ICD-9-CM codes consistent with suspected ADRs for the 4 years was calculated for hospital and ambulatory care settings, based on age ranges, comorbidities, and drug classes.

RESULTS

Between 2002 and 2005, the average annual prevalence of suspected ADRs was 0.5%, with a total of 249,633 suspected ADRs during the 4 years. The mean age of hospitalized patients experiencing a suspected ADR was 12 years older than that of ambulatory care patients and 20 years older than that of the general database population. Diseases of the circulatory and endocrine/nutritional/metabolic systems rank among the top 5 comorbid conditions in hospitalized patients who had a suspected ADR. Injury and poisoning was the primary comorbidity in ambulatory patients. High-risk medications frequently associated with suspected ADRs in both settings were antineoplastic and anticoagulant agents. Other drug classes commonly associated with suspected ADRs in hospitalized patients were antihypertensives and diuretics. For the ambulatory care setting, drug classes frequently associated with suspected ADRs were antirheumatic and antiarteriosclerotic agents.

CONCLUSIONS

ADR detection, using administrative data, revealed differences in age, comorbidities, and drug classifications between ambulatory care and hospital settings. The results can be used to develop focused prevention strategies and targeted surveillance for individuals most at risk for developing ADRs.

Integrating genomic-based clinical decision support into electronic health records

There is a growing consensus that the first and most necessary step to improving the efficiency, cost–effectiveness and quality of healthcare systems can be achieved through the implementation of interoperable patient-centric electronic health record (EHR) systems across hospitals and clinics. Targeted therapeutics (including screening, prevention and disease management) through EHR-based clinical decision support delivery may drive both the acceptance and adoption of EHR systems by providing personalized information at the point-of-care. The realization of targeted therapeutics will depend on the resolution of current political, ethical, socioeconomical and technical challenges surrounding EHR implementation efforts. There is a growing need for broad-based consensus initiatives to foster an essential level of standardization for EHRs. The timeliness of these issues is underlined by the rapid emergence of private sector efforts in this potentially lucrative field, from direct-to-consumer testing to Google-, or Microsoft-owned personal health data. This review discusses the potential value for adopting healthcare technology, with a focus on personalized medicine, and highlights the challenges that remain to achieve this.

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support

OBJECTIVE

Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS).

MATERIALS AND METHODS

We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data.

RESULTS

The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions.

CONCLUSION

A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core components has been developed to facilitate the tasks of TCM knowledge discovery and CDS. We have conducted several OLAP and data mining tasks to explore the empirical knowledge from the TCM clinical data. The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.

Identifying best practices for clinical decision support and knowledge management in the field

To investigate best practices for implementing and managing clinical decision support (CDS) in community hospitals and ambulatory settings, we carried out a series of ethnographic studies to gather information from nine diverse organizations. Using the Rapid Assessment Process methodology, we conducted surveys, interviews, and observations over a period of two years in eight different geographic regions of the U.S.A. We first utilized a template organizing method for an expedited analysis of the data, followed by a deeper and more time consuming interpretive approach. We identified five major categories of best practices that require careful consideration while carrying out the planning, implementation, and knowledge management processes related to CDS. As more health care organizations implement clinical systems such as computerized provider order entry with CDS, descriptions of lessons learned by CDS pioneers can provide valuable guidance so that CDS can have optimal impact on health care quality.

The physician-patient relationship and a National Health Information network

The growing use of interoperable electronic health records is likely to have significant effects on the physician-patient relationship. This relationship involves two-way trust: of the physician in patients, and of the patients in their providers. Interoperable records opens up this relationship to further view, with consequences that may both enhance and undermine trust. On the one hand, physicians may learn (from additional records) that information from their patients is - or is not - to be trusted. On the other hand, patients may learn from the increased oversight made possible by electronic records that their trust in their physicians is - or is not - warranted. Release of information through new methods of surveillance may also undermine patient trust. The article concludes that because trust is fragile, attention to transparency and confidentiality in the use of interoperable electronic records is essential.

Electronic health records and the management of women at high risk of hereditary breast and ovarian cancer

Currently, management strategies exist that can decrease the morbidity and mortality associated with having a BRCA1 or BRCA2 mutation. Unfortunately, the task of identifying these patients at high risk is a daunting challenge. This problem is intensified because Electronic Health Records (EHRs) today lack the functionality needed to identify these women and to manage those women once they have been identified. Numerous niche software programs have been developed to fill this gap. Unfortunately, these extremely valuable niche programs are prevented from being interoperable with the EHRs, on the premise that each EHR vendor will build their own programs. Effectively, in our efforts to adopt EHRs, we have lost sight of the fact that they can only have a major impact on quality of care if they contain structured data and if they interact with robust Clinical Decision Support (CDS) tools. We are at a cross roads in the development of the health care Information Technology infrastructure. We can choose a path where each EHR vendor develops each CDS module independently. Alternatively, we can choose a path where experts in each field develop external niche software modules that are interoperable with any EHR vendor. We believe that the modular approach to development of niche software programs that are interoperable with current EHRs will markedly increase the speed at which useful and functional EHRs that improve quality of care become a reality. Thus, in order to realize the benefits of CDS, we suggest vendors develop means to become interoperable with external modular niche programs.

Genomic and personalized medicine: foundations and applications

The last decade has witnessed a steady embrace of genomic and personalized medicine by senior government officials, industry leadership, health care providers, and the public. Genomic medicine, which is the use of information from genomes and their derivatives (RNA, proteins, and metabolites) to guide medical decision making-is a key component of personalized medicine, which is a rapidly advancing field of health care that is informed by each person's unique clinical, genetic, genomic, and environmental information. As medicine begins to embrace genomic tools that enable more precise prediction and treatment disease, which include "whole genome" interrogation of sequence variation, transcription, proteins, and metabolites, the fundamentals of genomic and personalized medicine will require the development, standardization, and integration of several important tools into health systems and clinical workflows. These tools include health risk assessment, family health history, and clinical decision support for complex risk and predictive information. Together with genomic information, these tools will enable a paradigm shift to a comprehensive approach that will identify individual risks and guide clinical management and decision making, all of which form the basis for a more informed and effective approach to patient care. DNA-based risk assessment for common complex disease, molecular signatures for cancer diagnosis and prognosis, and genome-guided therapy and dose selection are just among the few important examples for which genome information has already enabled personalized health care along the continuum from health to disease. In addition, information from individual genomes, which is a fast-moving area of technological development, is spawning a social and information revolution among consumers that will undoubtedly affect health care decision making. Although these and other scientific findings are making their way from the genome to the clinic, the full application of genomic and personalized medicine in health care will require dramatic changes in regulatory and reimbursement policies as well as legislative protections for privacy for system-wide adoption. Thus, there are challenges from both a scientific and a policy perspective to personalized health care; however, they will be confronted and solved with the certainty that the science behind genomic medicine is sound and the practice of medicine that it informs is evidence based.

[Designing a tool to describe drug interactions and adverse events for learning and clinical routine]

Pharmacists play an important role in prescription analysis. They are involved in therapeutic drug monitoring, particularly for drugs with a narrow therapeutic index, prevention and management of drug interactions, and may be called in to identify side effects and adverse events related to drug therapy. For the polymedicated patient, the medical file, the list of prescribed drugs and the history of their administration may be insufficient to adequately assign the responsibility of a given adverse effect to one or more drugs. Graphical representations can sometimes be useful to describe and clarify a sequence of events. In addition, as part of their academic course, students have many occasions to hear about "side effects" and "drug interactions". However, in the academic setting, there are few opportunities to observe the evolution and the consequences of these events. In the course of their hospital training, these students are required to perform patient follow-up for pharmacotherapeutic or educational purposes and to comment case reports to physicians. The aim of this paper is to present a tool facilitating the graphic display of drug interaction consequences and side effects. This tool can be a useful aid for causality assessment. It structures the students' training course and helps them better understand the commentaries pharmacists provide for physicians. Further development of this tool should contribute to the prevention of adverse drug events.

Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives

Effective clinical decision support (CDS) is essential for addressing healthcare performance improvement imperatives, but care delivery organizations (CDO) typically struggle with CDS deployment. Ensuring safe and effective medication delivery to patients is a central focus of CDO performance improvement efforts, and this article provides an overview of best-practice strategies for applying CDS to these goals. The strategies discussed are drawn from a new guidebook, co-published and co-sponsored by more than a dozen leading organizations. Developed by scores of CDS implementers and experts, the guidebook outlines key steps and success factors for applying CDS to medication management. A central thesis is that improving outcomes with CDS interventions requires that the CDS five rights be addressed successfully. That is, the interventions must deliver the right information, to the right person, in the right format, through the right channel, at the right point in workflow. This paper provides further details about these CDS five rights, and highlights other important strategies for successful CDS programs.

Information management to enable personalized medicine: stakeholder roles in building clinical decision support

BACKGROUND

Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies.

DISCUSSION

Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine.

SUMMARY

This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.

What can natural language processing do for clinical decision support?

Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

Clinical decision support capabilities of commercially-available clinical information systems

BACKGROUND

The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems.

METHODS

The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features.

RESULTS

Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern.

CONCLUSIONS

These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

Parent-driven technology for decision support in pediatric emergency care

BACKGROUND

A quasi-experimental intervention study composed of control and intervention periods was conducted to determine if a parent-driven health information technology influenced completeness of documentation and adherence to evidence-based emergency care for children.

METHODS

Structured chart abstraction was used to assess documentation and correctness of clinical actions at test ordering, medication prescribed for disease, and medication ordered for pain in a tertiary care pediatric emergency department and a suburban general emergency department. During the intervention periods, parents of children who presented with complaints related to otitis media, urinary tract infection, head trauma, or asthma entered data into a health information technology (ParentLink), which produced treatment plans in the context of evidence-based guidelines.

RESULTS

Of 1,410 subjects analyzed, 1,072 (76%) were assigned to one of four disease categories: urinary tract infection (22%), otitis media (20%), asthma (11%) and head trauma (47%). During ParentLink use, documentation of pain significantly improved (28% incomplete [control] versus 15% [intervention], p = .003). Incorrect actions for pain treatment decreased, but not significantly (33% [control] versus 24% [intervention], p = .13). ParentLink did not influence actions for test ordering or prescribing for disease.

DISCUSSION

Parent-driven health information technology intended to translate parents' knowledge into clinical practice and to support evidence-based care suggested a trend toward modest impact on pain management but did not demonstrate broad effects across diseases or care processes. The emergence and proliferation of personally controlled health records (PCHRs) presents opportunities for patients and parents to control their medical profiles. Although ParentLink is not a comprehensive PCHR, it represents a step in incorporating parent-derived information into medical decision making.

Evaluation of causes and frequency of medication errors during information technology downtime

PURPOSE

The causes and frequency of medication errors occurring during information technology downtime were evaluated.

METHODS

Individuals from a convenience sample of 78 hospitals who were directly responsible for supporting and maintaining clinical information systems (CISs) and automated dispensing systems (ADSs) were surveyed using an online tool between February 2007 and May 2007 to determine if medication errors were reported during periods of system downtime. The errors were classified using the National Coordinating Council for Medication Error Reporting and Prevention severity scoring index. The percentage of respondents reporting downtime was estimated.

RESULTS

Of the 78 eligible hospitals, 32 respondents with CIS and ADS responsibilities completed the online survey for a response rate of 41%. For computerized prescriber order entry, patch installations and system upgrades caused an average downtime of 57% over a 12-month period. Lost interface and interface malfunction were reported for centralized and decentralized ADSs, with an average downtime response of 34% and 29%, respectively. The average downtime response was 31% for software malfunctions linked to clinical decision-support systems. Although patient harm did not result from 30 (54%) medication errors, the potential for harm was present for 9 (16%) of these errors.

CONCLUSION

Medication errors occurred during CIS and ADS downtime despite the availability of backup systems and standard protocols to handle periods of system downtime. Efforts should be directed to reduce the frequency and length of down-time in order to minimize medication errors during such downtime.

The role of health information technology in quality improvement in pediatrics

Health information technology (HIT) will play an important role in most efforts to improve the quality of pediatric medicine, as evident from the range of investigations and projects discussed in this volume. Clement McDonald identified the importance of using information technology as an integral component of quality initiatives early in the development of electronic medical records (EMR). The role of HIT in quality improvement is not limited to tools integrated into EMR, but that remains an important strategy. Today, much attention is focused on interoperability of clinical systems that integrate and share data from multiple sources. There are also additional freestanding quality-improvement tools that can be used without an EMR. This article explores the many roles of HIT in quality improvement from several perspectives.

Don E. Detmer and the American Medical Informatics Association: an appreciation

Don E. Detmer has served as President and Chief Executive Officer of the American Medical Informatics Association (AMIA) for the past five years, helping to set a course for the organization and demonstrating remarkable leadership as AMIA has evolved into a vibrant and influential professional association. On the occasion of Dr. Detmer's retirement, we fondly reflect on his professional life and his many contributions to biomedical informatics and, more generally, to health care in the U.S. and globally.

Computerized prompting and feedback of diabetes care: a review of the literature

BACKGROUND

The objective of this study was to assess published literature on computerized prompting and feedback of diabetes care as well as to identify opportunities to strengthen diabetes care processes.

METHODS

Medline (1970-2008), Cumulative Index to Nursing and Allied Health Literature (1982-2008), and Cochrane Central Register of Controlled Trials (4th quarter 2008) were searched, and reference lists from included articles were reviewed to identify additional studies. Patient sample, clinician sample, setting, duration of the trial, intervention description, control description, and results were abstracted from each study.

RESULTS

Fifteen trials were included in this review. The following elements were observed in the interventions: general prompt for a particular patient to be seen for diabetes-related follow-up (5 studies), specific prompt reminding clinicians of particular tests or procedures related to diabetes (13 studies), feedback to clinicians in addition to prompting (5 studies), and patient reminders in addition to clinician prompts (5 studies). Twelve of the 15 studies (80%) measured a significant process or outcome from the intervention.

CONCLUSIONS

The majority of trials identified at least one process or outcome that was significantly better in the intervention group than in the control group; however, the success of the information interventions varied greatly. Providing and receiving appropriate care is the first step toward better outcomes in chronic disease management.

Development of an electronic medical record-based clinical decision support tool to improve HIV symptom management

Common symptoms associated with HIV disease and its management are often underrecognized and undertreated. A clinical decision support tool for symptom management was developed within the Veterans Health Administration electronic medical record (EMR), aiming at increasing provider awareness of and response to common HIV symptoms. Its feasibility was studied in March to May 2007 by implementing it within a weekly HIV clinic, comparing a 4-week intervention period with a 4-week control period. Fifty-six patients and their providers participated in the study. Patients' perceptions of providers' awareness of their symptoms, proportion of progress notes mentioning any symptom(s) and proportion of care plans mentioning any symptom(s) were measured. The clinical decision support tool used portable electronic "tablets" to elicit symptom information at the time of check-in, filtered, and organized that information into a concise and clinically relevant EMR note available at the point of care, and facilitated clinical responses to that information. It appeared to be well accepted by patients and providers and did not substantially impact workflow. Although this pilot study was not powered to detect effectiveness, 25 (93%) patients in the intervention group reported that their providers were very aware of their symptoms versus 27 (75%) control patients (p = 0.07). The proportion of providers' notes listing symptoms was similar in both periods; however, there was a trend toward including a greater number of symptoms in intervention period progress notes. The symptom support tool seemed to be useful in clinical HIV care. The Veterans Health Administration EMR may be an effective "laboratory" for developing and testing decision supports.

A health examination system integrated with clinical decision support system

Health examinations play a key role in preventive medicine. We propose a health examination system named Health Examination Automatic Logic System (HEALS) to assist clinical workers in improving the total quality of health examinations. Quality of automated inference is confirmed by the zero inference error where during 6 months and 14,773 cases. Automated inference time is less than one second per case in contrast to 2 to 5 min for physicians. The most significant result of efficiency evaluation is that 3,494 of 4,356 (80.2%) cases take less than 3 min per case for producing a report summary. In the evaluation of effectiveness, novice physicians got 18% improvement in making decisions with the assistance of our system. We conclude that a health examination system with a clinical decision system can greatly reduce the mundane burden on clinical workers and markedly improve the quality and efficiency of health examination tasks.

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

BACKGROUND

In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.

DISCUSSION

Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.

SUMMARY

A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

Evaluation of a computer-based adverse-drug-event monitor

PURPOSE

The performance of a computer-based adverse-drug-event (ADE) monitor is evaluated, and the characteristics of ADEs detected and undetected by the monitor are compared.

METHODS

A retrospective analysis was conducted to identify ADEs using pre-defined ADE alerts that were recognized by a computer-based ADE monitor in a 1300-bed, tertiary care, teaching hospital in Seoul, Korea. A subsequent chart review was conducted by a pharmacist to confirm the ADEs and identify ADEs unrecognized by the monitor. The performance of the monitor was evaluated for its sensitivity and positive predictive value in detecting an ADE. The differences in characteristics of ADEs were compared between computer-recognized ADEs and computer-unrecognized ADEs for severity, causality, preventability, associated clinical manifestations, and types of ADEs.

RESULTS

During a one-month period, a total of 598 patients from two intensive care units and five general wards were monitored to identify ADEs. The computer-based ADE monitor identified 148 ADEs, and the chart review identified 39 computer-unrecognized ADEs. The sensitivity of the computer-based ADE monitor was 79% (148 of 187). The computer-recognized ADEs were more severe than computer-unrecognized ADEs, but there were no statistically significant differences in the causality, preventability, and types of ADEs. The positive predictive value of the computer monitor was 21% (148 of 718).

CONCLUSION

The computer-based ADE monitor successfully identified most of the ADEs and almost all of the severe ADEs that occurred in the hospitalized patients. However, the accuracy of the computer-based ADE monitor needs to be improved.

Recommendations for a clinical decision support for the management of individuals with chronic kidney disease

BACKGROUND AND OBJECTIVES

Care for advanced CKD patients is suboptimal. CKD practice guidelines aim to close gaps in care, but making providers aware of guidelines is an ineffective implementation strategy. The Institute of Medicine has endorsed the use of clinical decision support (CDS) for implementing guidelines. The authors' objective was to identify the requirements of an optimal CDS system for CKD management.

DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS

The aims of this study expanded on those of previous work that used the facilitated process improvement (FPI) methodology. In FPI, an expert workgroup develops a set of quality improvement tools that can subsequently be utilized by practicing physicians. The authors conducted a discussion with a group of multidisciplinary experts to identify requirements for an optimal CDS system.

RESULTS

The panel considered the process of patient identification and management, associated barriers, and elements by which CDS could address these barriers. The panel also discussed specific knowledge needs in the context of a typical scenario in which CDS would be used. Finally, the group developed a set of core requirements that will likely facilitate the implementation of a CDS system aimed at improving the management of any chronic medical condition.

CONCLUSIONS

Considering the growing burden of CKD and the potential healthcare and resource impact of guideline implementation through CDS, the relevance of this systematic process, consistent with Institute of Medicine recommendations, cannot be understated. The requirements described in this report could serve as a basis for the design of a CKD-specific CDS.

Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings

Background

Despite wide promotion, clinical practice guidelines have had limited effect in changing physician behavior. Effective implementation strategies to date have included: multifaceted interventions involving audit and feedback, local consensus processes, marketing; reminder systems, either manual or computerized; and interactive educational meetings. In addition, there is now growing evidence that contextual factors affecting implementation must be addressed such as organizational support (leadership procedures and resources) for the change and strategies to implement and maintain new systems.

Methods

To examine the feasibility and effectiveness of implementation of a computerized decision support system for depression (CDSS-D) in routine public mental health care in Texas, fifteen study clinicians (thirteen physicians and two advanced nurse practitioners) participated across five sites, accruing over 300 outpatient visits on 168 patients.

Results

Issues regarding computer literacy and hardware/software requirements were identified as initial barriers. Clinicians also reported concerns about negative impact on workflow and the potential need for duplication during the transition from paper to electronic systems of medical record keeping.

Conclusion

The following narrative report based on observations obtained during the initial testing and use of a CDSS-D in clinical settings further emphasizes the importance of taking into account organizational factors when planning implementation of evidence-based guidelines or decision support within a system.

Advancing patient-centered pediatric care through health information exchange: update from the American Health Information Community Personalized Health Care Workgroup

The Personalized Health Care Workgroup of the American Health Information Community was formed to foster a broad, community-based approach to facilitate the incorporation of interoperable, clinically useful, genetic/genomic information and analytical tools into electronic health records, to support clinical decision-making. The Personalized Health Care Workgroup has developed a series of use cases that outline the informational needs of multiple stakeholders (eg, patients, clinicians, organizations, and systems) and describe the information systems necessary to connect these stakeholders at multiple levels. These use case scenarios offer a guide for standardized data elements and architecture that enable interoperability (content sharing) among different formats of patient electronic health records.

Use of clinical decision support to increase influenza vaccination: multi-year evolution of the system

Despite recognition that clinical decision support (CDS) can improve patient care, there has been poor penetration of this technology into healthcare settings. We used CDS to increase inpatient influenza vaccination during implementation of an electronic medical record, in which pharmacy and nursing transactions increasingly became electronic. Over three influenza seasons we evaluated standing orders, provider reminders, and pre-selected physician orders. A pre-intervention cross-sectional survey showed that most patients (95%) met criteria for vaccination. During our intervention, physicians were increasingly likely to accept pre-selected vaccination orders, Year 1 (47%), Year 2 (77%), Year 3 (83%); however vaccine administration by nurses was suboptimal. As electronic medical record functionality improved, patient receipt of vaccine increased dramatically, Year 1 [0/36; 0%], Year 2 [8/66; 12%], Year 3 [286/805; 36%]. Successful use of clinical decision support to increase inpatient influenza vaccination only occurred after initiation of CPOE for all medications and integration of an electronic medication administration record. Also, since most patients met criteria for influenza vaccination, complicated logic to identify high-risk patients was unnecessary.

Community-wide implementation of health information technology: the Massachusetts eHealth Collaborative experience

The Massachusetts eHealth Collaborative (MAeHC) was formed to improve patient safety and quality of care by promoting the use of health information technology through community-based implementation of electronic health records (EHRs) and health information exchange. The Collaborative has recently implemented EHRs in a diverse set of competitively selected communities, encompassing nearly 500 physicians serving over 500,000 patients. Targeting both EHR implementation and health information exchange at the community level has identified numerous challenges and strategies for overcoming them. This article describes the formation and implementation phases of the Collaborative, focusing on barriers identified, lessons learned, and policy issues.

Advancing personalized health care through health information technology: an update from the American Health Information Community's Personalized Health Care Workgroup

The Personalized Health Care Workgroup of the American Health Information Community was formed to determine what is needed to promote standard reporting and incorporation of medical genetic/genomic tests and family health history data in electronic health records. The Workgroup has examined and clarified a range of issues related to this information, including interoperability standards and requirements for confidentiality, privacy, and security, in the course of developing recommendations to facilitate its capture, storage, transmission, and use in clinical decision support. The Workgroup is one of several appointed by the American Health Information Community to study high-priority issues related to the implementation of interoperable electronic health records in the United States. It is also a component of the U.S. Department of Health and Human Services' Personalized Health Care Initiative, which is designed to create a foundation upon which information technology that supports personalized, predictive, and pre-emptive health care can be built.

The informatics opportunities at the intersection of patient safety and clinical informatics

Health care providers have a basic responsibility to protect patients from accidental harm. At the institutional level, creating safe health care organizations necessitates a systematic approach. Effective use of informatics to enhance safety requires the establishment and use of standards for concept definitions and for data exchange, development of acceptable models for knowledge representation, incentives for adoption of electronic health records, support for adverse event detection and reporting, and greater investment in research at the intersection of informatics and patient safety. Leading organizations have demonstrated that health care informatics approaches can improve safety. Nevertheless, significant obstacles today limit optimal application of health informatics to safety within most provider environments. The authors offer a series of recommendations for addressing these challenges.

Reasons for declining computerized insulin protocol recommendations: application of a framework

Clinical decision support systems (CDS) can interpret detailed treatment protocols for ICU care providers. In open-loop systems, clinicians can decline protocol recommendations. We capture their reasons for declining as part of ongoing, iterative protocol validation and refinement processes. Even though our protocol was well-accepted by clinicians overall, noncompliance patterns revealed potential protocol improvement targets, and suggested ways to reduce barriers impeding software use. We applied Rita Kukafka and colleagues' (2003) IT implementation framework to identify and categorize reasons documented by ICU nurses when declining recommendations from an insulin-titration protocol. Two methods were used to operationalize the framework: reasons for declining recommendations from actual software use, and a nurse questionnaire. Applying the framework exposed limitations of our data sources, and suggested ways to address those limitations; and facilitated our analyses and interpretations.

Grand challenges in clinical decision support

There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: improve the human-computer interface; disseminate best practices in CDS design, development, and implementation; summarize patient-level information; prioritize and filter recommendations to the user; create an architecture for sharing executable CDS modules and services; combine recommendations for patients with co-morbidities; prioritize CDS content development and implementation; create internet-accessible clinical decision support repositories; use freetext information to drive clinical decision support; mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare.

Centers speak up: the clinical context for health information technology in the ambulatory care setting

BACKGROUND

Clinicians in ambulatory care settings are increasingly called upon to use health information technology (health IT) to improve practice efficiency and performance. Successful adoption of health IT requires an understanding of how clinical tasks and workflows will be affected; yet this has not been well described.

OBJECTIVE

To describe how health IT functions within a clinical context.

DESIGN

Qualitative study, using in-depth, semi-structured interviews.

PARTICIPANTS

Executives and staff at 4 community health centers, 3 health center networks, and 1 large primary care organization.

APPROACH

Transcribed audio-recorded interviews, analyzed using the constant comparative method.

RESULTS

Systematic characterization of clinical context identified 6 primary clinical domains. These included results management, intra-clinic communication, patient education and outreach, inter-clinic coordination, medication management, and provider education and feedback. We generated clinical process diagrams to characterize these domains. Participants suggested that underlying workflows for these domains must be fully operational to ensure successful deployment of health IT.

CONCLUSIONS

Understanding the clinical context is a necessary precursor to successful deployment of health IT. Process diagrams can serve as the basis for EHR certification, to identify challenges, to measure health IT adoption, or to develop curricular content regarding the role of health IT in clinical practice.

Electronic medical record-assisted design of a cluster-randomized trial to improve diabetes care and outcomes

BACKGROUND

Electronic medical records (EMRs) have the potential to facilitate the design of large cluster-randomized trials (CRTs).

OBJECTIVE

To describe the design of a CRT of clinical decision support to improve diabetes care and outcomes.

METHODS

In the Diabetes Improvement Group-Intervention Trial (DIG-IT), we identified and balanced preassignment characteristics of 12,675 diabetic patients cared for by 147 physicians in 24 practices of 2 systems using the same vendor's EMR. EMR-facilitated disease management was system A's experimental intervention; system B interventions involved patient empowerment, with or without disease management. For our sample, we: (1) identified characteristics associated with response to interventions or outcomes; (2) summarized feasible partitions of 10 system A practices (2 groups) and 14 system B practices (3 groups) using intra-cluster correlation coefficients (ICCs) and standardized differences; (3) selected (blinded) partitions to effectively balance the characteristics; and (4) randomly assigned groups of practices to interventions.

RESULTS

In System A, 4,306 patients, were assigned to 2 groups of practices; 8,369 patients in system B were assigned to 3 groups of practices. Nearly all baseline outcome variables and covariates were well-balanced, including several not included in the initial design. DIG-IT's balance was superior to alternative partitions based on volume, geography or demographics alone.

CONCLUSIONS

EMRs facilitated rigorous CRT design by identifying large numbers of patients with diabetes and enabling fair comparisons through preassignment balancing of practice sites. Our methods can be replicated in other settings and for other conditions, enhancing the power of other translational investigations.

A framework and model for evaluating clinical decision support architectures

In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN, and SAGE.

A four-phase model of the evolution of clinical decision support architectures

BACKGROUND

A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems.

PURPOSE

To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems.

METHODS

The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model.

RESULTS

The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems.

CONCLUSIONS

Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: (1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, (2) there are serious terminological issues, (3) patient data may be spread across several sources with no single source having a complete view of the patient, and (4) major difficulties exist in transferring successful interventions from one site to another.

Automation, decision support, and expert systems in nephrology

Increasing data suggest that errors in medicine occur frequently and result in substantial harm to the patient. The Institute of Medicine report described the magnitude of the problem, and public interest in this issue, which was already large, has grown. The traditional approach in medicine has been to identify the persons making the errors and recommend corrective strategies. However, it has become increasingly clear that it is more productive to focus on the systems and processes through which care is provided. If these systems are set up in ways that would both make errors less likely and identify those that do occur and, at the same time, improve efficiency, then safety and productivity would be substantially improved. Clinical decision support systems (CDSSs) are active knowledge systems that use 2 or more items of patient data to generate case specific recommendations. CDSSs are typically designed to integrate a medical knowledge base, patient data, and an inference engine to generate case specific advice. This article describes how automation, templating, and CDSS improve efficiency, patient care, and safety by reducing the frequency and consequences of medical errors in nephrology. We discuss practical applications of these in 3 settings: a computerized anemia-management program (CAMP, Henry Ford Health System, Detroit, MI), vascular access surveillance systems, and monthly capitation notes in the hemodialysis unit.

Preventing adverse drug events in hospital practice: an overview

Adverse drug events (ADEs) are a considerable cause of morbidity and mortality in hospital practice. The precise frequency is unknown, but studies give an incidence number ranging from 2 until 52 ADEs per 100 patients. There are many different methods for definition, causality assessment, severity classification and detection which make it difficult to compare the different studies. A substantial part (in some studies up to 70%) of ADEs can be prevented and it is important to, besides their detection, focus on the prevention of these ADEs. In this literature review we give an overview of methods for preventing ADEs. There are many different tools with different impact on a particular part of the distribution system which has the potential to prevent ADEs. A multifaceted approach is needed. Two interesting strategies of prevention, pharmacist participation on ward rounds and computerised physician order entry with clinical decision support systems (CDSS), are highlighted. Moreover, two promising CDSS are discussed in more detail, namely computer-based monitoring systems and information systems which link laboratory and pharmacy data.

Migrating toward a next-generation clinical decision support application: the BJC HealthCare experience

The next-generation model outlined in the AMIA Roadmap for National Action on Clinical Decision Support (CDS) is aimed to optimize the effectiveness of CDS interventions, and to achieve widespread adoption. BJC HealthCare re-engineered its existing CDS system in alignment with the AMIA roadmap and plans to use it for guidance on further enhancements. We present our experience and discuss an incremental approach to migrate towards the next generation of CDS applications from the viewpoint of a healthcare institution. Specifically, a CDS rule engine service with a standards-based rule representation format was built to simplify maintenance and deployment. Rules were separated from execution code and made customizable for multi-facility deployment. Those changes resulted in system improvement in the short term while aligning with long-term strategic objectives.

Recommendations for clinical decision support deployment: synthesis of a roundtable of medical directors of information systems

BACKGROUND

Ample evidence exists that clinical decision support (CDS) can improve clinician performance. Nevertheless, additional evidence demonstrates that clinicians still do not perform adequately in many instances. This suggests an ongoing need for implementation of CDS, in turn prompting development of a roadmap for national action regarding CDS.

OBJECTIVE

Develop practical advice to aid CDS implementation in order to improve clinician performance.

METHOD

Structured group interview during a roundtable discussion by medical directors of information systems (N = 30), with subsequent review by participants and synthesis.

RESULTS

Participant consensus was that CDS should be comprehensive and should involve techniques such as order sets and facilitated documentation as well as alerts; should be subject to ongoing feedback; and should flow from and be governed by an organization's clinical goals.

CONCLUSION

A structured roundtable discussion of clinicians experienced in health information technology can yield practical, consensus advice for implementation of CDS.

Some unintended consequences of clinical decision support systems

Clinical decision support systems (CDS) coupled with computerized physician/provider order entry (CPOE) can improve the quality of patient care and the efficiency of hospital operations. However, they can also produce unintended consequences. Using qualitative methods, a multidisciplinary team gathered and analyzed data about the unintended consequences of CPOE, identifying nine types, and found that CDS-generated unintended consequences appeared among all types. Further analysis of 47 CDS examples uncovered three themes related to CDS content: elimination or shifting of human roles; difficulty in keeping content current; and inappropriate content. Three additional themes related to CDS presentation were found: rigidity of the system; alert fatigue; and potential for errors. Management of CDS must include careful selection and maintenance of content and prudent decision making about human computer interaction opportunities.

A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting

OBJECTIVE

Despite demonstrated benefits, few healthcare organizations have implemented clinical event monitors to detect adverse drug events (ADEs). The objective of this study was to conduct a systematic review of pharmacy and laboratory signals used by clinical event monitors to detect ADEs in hospitalized adults.

DESIGN

We performed a comprehensive search of MEDLINE, CINHAL and EMBASE to identify studies published between 1985 through 2006. Studies were included if they: described a clinical event monitor to detect ADEs in an adult hospital setting; described laboratory or pharmacy ADE signals; and, provided positive predictive values (PPVs) or information to allow the calculation of PPVs for individual ADE signals.

MEASUREMENTS

We calculated overall estimates of PPVs and 95% confidence intervals (CIs) for signals reported in 2 or more studies and contained no evidence heterogeneity. Results were examined by signal category: medication levels, laboratory tests, or antidotes.

RESULTS

We identified 12 observational studies describing 36 unique ADE signals. Fifteen signals (3 antidotes, 4 medication levels, and 8 laboratory values) contained no evidence of heterogeneity. The pooled PPVs for these individual signals ranged from 0.03 [CI=0.03-0.03] for hypokalemia, to 0.50 [CI=0.39-0.61] for supratherapeutic quinidine level. In general, antidotes (range=0.09-0.11) had the lowest PPVs, followed by laboratory values (0.03-0.27), and medication levels (0.03-0.50).

CONCLUSION

Results from this study should help clinical information system and computerized decision support producers develop or improve existing clinical event monitors to detect ADEs in their own hospitals by prioritizing those signals with the highest PPVs [corrected]

Proposal for fulfilling strategic objectives of the U.S. Roadmap for national action on clinical decision support through a service-oriented architecture leveraging HL7 services

Despite their demonstrated effectiveness, clinical decision support (CDS) systems are not widely used within the U.S. The Roadmap for National Action on Clinical Decision Support, published in June 2006 by the American Medical Informatics Association, identifies six strategic objectives for achieving widespread adoption of effective CDS capabilities. In this manuscript, we propose a Service-Oriented Architecture (SOA) for CDS that facilitates achievement of these six objectives. Within the proposed framework, CDS capabilities are implemented through the orchestration of independent software services whose interfaces are being standardized by Health Level 7 and the Object Management Group through their joint Healthcare Services Specification Project (HSSP). Core services within this framework include the HSSP Decision Support Service, the HSSP Common Terminology Service, and the HSSP Retrieve, Locate, and Update Service. Our experiences, and those of others, indicate that the proposed SOA approach to CDS could enable the widespread adoption of effective CDS within the U.S. health care system.