A roadmap for national action on clinical decision support

Evaluation of a novel electronic genetic screening and clinical decision support tool in prenatal clinical settings

"The Pregnancy and Health Profile" (PHP) is a free prenatal genetic screening and clinical decision support (CDS) software tool for prenatal providers. PHP collects family health history (FHH) during intake and provides point-of-care risk assessment for providers and education for patients. This pilot study evaluated patient and provider responses to PHP and effects of using PHP in practice. PHP was implemented in four clinics. Surveys assessed provider confidence and knowledge and patient and provider satisfaction with PHP. Data on the implementation process were obtained through semi-structured interviews with administrators. Quantitative survey data were analyzed using Chi square test, Fisher's exact test, paired t tests, and multivariate logistic regression. Open-ended survey questions and interviews were analyzed using qualitative thematic analysis. Of the 83% (513/618) of patients that provided feedback, 97% felt PHP was easy to use and 98% easy to understand. Thirty percent (21/71) of participating physicians completed both pre- and post-implementation feedback surveys [13 obstetricians (OBs) and 8 family medicine physicians (FPs)]. Confidence in managing genetic risks significantly improved for OBs on 2/6 measures (p values ≤0.001) but not for FPs. Physician knowledge did not significantly change. Providers reported value in added patient engagement and reported mixed feedback about the CDS report. We identified key steps, resources, and staff support required to implement PHP in a clinical setting. To our knowledge, this study is the first to report on the integration of patient-completed, electronically captured and CDS-enabled FHH software into primary prenatal practice. PHP is acceptable to patients and providers. Key to successful implementation in the future will be customization options and interoperability with electronic health records.

The need for clinical decision support integrated with the electronic health record for the clinical application of whole genome sequencing information

Whole genome sequencing (WGS) is rapidly approaching widespread clinical application. Technology advancements over the past decade, since the first human genome was decoded, have made it feasible to use WGS for clinical care. Future advancements will likely drive down the price to the point wherein WGS is routinely available for care. However, were this to happen today, most of the genetic information available to guide clinical care would go unused due to the complexity of genetics, limited physician proficiency in genetics, and lack of genetics professionals in the clinical workforce. Furthermore, these limitations are unlikely to change in the future. As such, the use of clinical decision support (CDS) to guide genome-guided clinical decision-making is imperative. In this manuscript, we describe the barriers to widespread clinical application of WGS information, describe how CDS can be an important tool for overcoming these barriers, and provide clinical examples of how genome-enabled CDS can be used in the clinical setting.

A proposed clinical decision support architecture capable of supporting whole genome sequence information

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

Adoption of clinical decision support in multimorbidity: a systematic review

Background

Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks.

Objective

The aim of this review was to identify to what extent CDS is adopted in multimorbidity.

Methods

This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness.

Results

A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients’ clinical records (n=19), clinical practice guidelines (n=12), and clinicians’ knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed, there were no studies referring to the active involvement of the patient in the decision-making process or to patient self-management. None of the articles reviewed adopted mobile technologies. There were no rigorous evaluations of usability or effectiveness of the CDS systems reported.

Conclusions

This review shows that multimorbidity is underinvestigated in the informatics of supporting clinical decisions. CDS interventions that systematize clinical practice guidelines without considering the interactions of different conditions and care processes may lead to unhelpful or harmful clinical actions. To improve patient safety in multimorbidity, there is a need for more evidence about how both conditions and care processes interact. The data needed to build this evidence base exist in many electronic health record systems and are underused.

Use of a Computer Decision Support System and Antimicrobial Therapy Appropriateness

Objective.

To determine whether antimicrobial (AM) courses ordered with an antimicrobial computer decision support system (CDSS) were more likely to be appropriate than courses ordered without the CDSS.

Design.

Retrospective cohort study. Blinded expert reviewers judged whether AM courses were appropriate, considering drug selection, route, dose, and duration.

Setting.

A 279-bed university-affiliated Department of Veterans Affairs (VA) hospital.

Patients.

A 500-patient random sample of inpatients who received a therapeutic AM course between October 2007 and September 2008.

Intervention.

An optional CDSS, available at the point of order entry in the VA computerized patient record system.

Results.

CDSS courses were significantly more likely to be appropriate (111/254, 44%) compared with non-CDSS courses (81/246, 33%, P = .013). Courses were more likely to be appropriate when the initial provider diagnosis of the condition being treated was correct (168/273, 62%) than when it was incorrect, uncertain, or a sign or symptom rather than a disease (24/227, 11%, P< .001). In multivariable analysis, CDSS-ordered courses were more likely to be appropriate than non-CDSS-ordered courses (odds ratio [OR], 1.83; 95% confidence interval [CI], 1.13–2.98). Courses were also more likely to be judged appropriate when the initial provider diagnosis of the condition being treated was correct than when it was incorrect, uncertain, or a sign or symptom rather than a disease (OR, 3.56; 95% CI, 1.4-9.0).

Conclusions.

Use of the CDSS was associated with more appropriate AM use. To achieve greater improvements, strategies are needed to improve provider diagnoses of syndromes that are infectious or possibly infectious.

Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers

BACKGROUND

Following liver transplantation, patients require lifelong immunosuppressive care and monitoring. Computerized clinical decision support (CDS) has been shown to improve post-transplant immunosuppressive care processes and outcomes. The readiness of transplant information systems to implement computerized CDS to support post-transplant care is unknown.

OBJECTIVES

a) Describe the current clinical information system functionality and manual and automated processes for laboratory monitoring of immunosuppressive care, b) describe the use of guidelines that may be used to produce computable logic and the use of computerized alerts to support guideline adherence, and c) explore barriers to implementation of CDS in U.S. liver transplant centers.

METHODS

We developed a web-based survey using cognitive interviewing techniques. We surveyed 119 U.S. transplant programs that performed at least five liver transplantations per year during 2010-2012. Responses were summarized using descriptive analyses; barriers were identified using qualitative methods.

RESULTS

Respondents from 80 programs (67% response rate) completed the survey. While 98% of programs reported having an electronic health record (EHR), all programs used paper-based manual processes to receive or track immunosuppressive laboratory results. Most programs (85%) reported that 30% or more of their patients used external laboratories for routine testing. Few programs (19%) received most external laboratory results as discrete data via electronic interfaces while most (80%) manually entered laboratory results into the EHR; less than half (42%) could integrate internal and external laboratory results. Nearly all programs had guidelines regarding pre-specified target ranges (92%) or testing schedules (97%) for managing immunosuppressive care. Few programs used computerized alerting to notify transplant coordinators of out-of-range (27%) or overdue laboratory results (20%).

CONCLUSIONS

Use of EHRs is common, yet all liver transplant programs were largely dependent on manual paper-based processes to monitor immunosuppression for post-liver transplant patients. Similar immunosuppression guidelines provide opportunities for sharing CDS once integrated laboratory data are available.

Service oriented architecture for clinical decision support: a systematic review and future directions

The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

The Number Needed to Remind: a Measure for Assessing CDS Effectiveness

BACKGROUND

Clinical decision support (CDS) is associated with improvement in quality and efficiency in healthcare delivery. The appropriate way to evaluate its effectiveness remains uncertain.

METHODS

We analyzed data from our electronic health record (EHR) measuring the display frequency of eight reminders for Coronary Artery disease and Type 2 Diabetes and their associated performance according to a predefined methodology. We propose two key performance indicators to measure their impact on a target population: the reminder performance (RP), and the number needed to remind (NNR), to evaluate the impact that Clinical decision support reminders have on the adherence to guideline derived CDS interventions on the entire patient population, and individual providers receiving the interventions.

RESULTS

Data were available for 116,027 patients and a total of 1,982,735 reminders were displayed to a subset of 65,516 patients during the study period from January 1 to December 31, 2010. The evaluation framework assessed provider acknowledgement of the CDS intervention, and the presence of the expected performance event while accounting for patients' exposure to the CDS reminders. The total RP was 2.7% while the average NNR was 3.1 for all the reminders under study.

CONCLUSIONS

The proposed framework to asses of CDS performance provides a novel approach to improve the design and evaluation of CDS interventions. The application of this methodology represents an indicator to understand the impact of CDS interventions and subsequent patient outcomes. Further research is required to evaluate the impact of these systems on the quality of care.

Clinical Decision Support for Early Recognition of Sepsis

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman's Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability.

Costs associated with implementation of computer-assisted clinical decision support system for antenatal and delivery care: case study of Kassena-Nankana district of northern Ghana

OBJECTIVE

This study analyzed cost of implementing computer-assisted Clinical Decision Support System (CDSS) in selected health care centres in Ghana.

METHODS

A descriptive cross sectional study was conducted in the Kassena-Nankana district (KND). CDSS was deployed in selected health centres in KND as an intervention to manage patients attending antenatal clinics and the labour ward. The CDSS users were mainly nurses who were trained. Activities and associated costs involved in the implementation of CDSS (pre-intervention and intervention) were collected for the period between 2009-2013 from the provider perspective. The ingredients approach was used for the cost analysis. Costs were grouped into personnel, trainings, overheads (recurrent costs) and equipment costs (capital cost). We calculated cost without annualizing capital cost to represent financial cost and cost with annualizing capital costs to represent economic cost.

RESULTS

Twenty-two trained CDSS users (at least 2 users per health centre) participated in the study. Between April 2012 and March 2013, users managed 5,595 antenatal clients and 872 labour clients using the CDSS. We observed a decrease in the proportion of complications during delivery (pre-intervention 10.74% versus post-intervention 9.64%) and a reduction in the number of maternal deaths (pre-intervention 4 deaths versus post-intervention 1 death). The overall financial cost of CDSS implementation was US$23,316, approximately US$1,060 per CDSS user trained. Of the total cost of implementation, 48% (US$11,272) was pre-intervention cost and intervention cost was 52% (US$12,044). Equipment costs accounted for the largest proportion of financial cost: 34% (US$7,917). When economic cost was considered, total cost of implementation was US$17,128-lower than the financial cost by 26.5%.

CONCLUSIONS

The study provides useful information in the implementation of CDSS at health facilities to enhance health workers' adherence to practice guidelines and taking accurate decisions to improve maternal health care.

Reducing risk with clinical decision support: a study of closed malpractice claims

OBJECTIVE

Identify clinical opportunities to intervene to prevent a malpractice event and determine the proportion of malpractice claims potentially preventable by clinical decision support (CDS).

MATERIALS AND METHODS

Cross-sectional review of closed malpractice claims over seven years from one malpractice insurance company and seven hospitals in the Boston area. For each event, clinical opportunities to intervene to avert the malpractice event and the presence or absence of CDS that might have a role in preventing the event, were assigned by a panel of expert raters. Compensation paid out to resolve a claim (indemnity), was associated with each CDS type.

RESULTS

Of the 477 closed malpractice cases, 359 (75.3%) were categorized as substantiated and 195 (54%) had at least one opportunity to intervene. Common opportunities to intervene related to performance of procedure, diagnosis, and fall prevention. We identified at least one CDS type for 63% of substantiated claims. The 41 CDS types identified included clinically significant test result alerting, diagnostic decision support and electronic tracking of instruments. Cases with at least one associated intervention accounted for $40.3 million (58.9%) of indemnity.

DISCUSSION

CDS systems and other forms of health information technology (HIT) are expected to improve quality of care, but their potential to mitigate risk had not previously been quantified. Our results suggest that, in addition to their known benefits for quality and safety, CDS systems within HIT have a potential role in decreasing malpractice payments.

CONCLUSION

More than half of malpractice events and over $40 million of indemnity were potentially preventable with CDS.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Literature review on clinical decision support system reducing medical error

OBJECTIVES

Quite a number of studies on clinical decision support systems (CDSS) have been published in recent years to assess the characteristics and architecture of CDSS and evaluate the effects of CDSS on clinical work. However, until now there have been no relevant studies to investigate the quantity of these, and their contribution to present day thinking. The aim of this study was to explore the areas of theme, and the study design of research on CDSS in literature published in English and Chinese-language journals.

METHODS

We searched the major database including MEDLINE, EMbase, Cochrane Library and four Chinese databases including Chinese Biomedical Literature Database (CBM), Wanfang Data, Chinese Scientific Journal Database (VIP), and Chinese Journals Full-text Database (CNKI) and to analyze the publication years, research themes, authors' affiliations and methodologies of studies. Quality and statistical method were only appraised by classification of study designs.

RESULTS

A total of 616 studies published from 1990 to 2013 were included in our research. In the year of 2011 the number of studies reached its peak with 96 studies accounting for 15.58% of the years' publication. We grouped the included studies into six major topic areas of which computerized clinical decision support systems dominated the included studies accounting for 51.46% of all studies. Commentary reviews and cross-sectional studies which took up approximately 46.10% of the included studies, with 30.52% (188 studies) and 15.58% (96 studies) respectively. Most included studies on CDSS were conducted in the following four institutions: universities, hospitals, research institutions and companies.

CONCLUSIONS

There is a growing change trend in the number of studies on CDSS research in recent two decades, most of which are non-comparative studies (46.10%) . Only 21 systematic reviews and 22 randomized controlled trails were published with the percentage of 3.41% and 3.57% of the included studies. More methodologically rigorous designs are needed to improve the research quality on CDSS.

The future of genomic testing in primary care: the changing face of personalized medicine

Primary care is recognized worldwide as a key component for improving health outcomes in the population. At the same time, healthcare systems are rapidly changing with increasing expectations from technological advances. Genomics is a major driver in changing how medicine is being practiced; however, the importance for primary care has been under-appreciated. Strategically implementing genomics in a way that accounts for the unique characteristics of the primary care context is essential. In this perspective, we present important areas that we believe are critical in consideration of both the future of genomic medicine and primary healthcare delivery.

Clinical decision support for colon and rectal surgery: an overview

Clinical decision support (CDS) has been shown to improve clinical processes, promote patient safety, and reduce costs in healthcare settings, and it is now a requirement for clinicians as part of the Meaningful Use Regulation. However, most evidence for CDS has been evaluated primarily in internal medicine care settings, and colon and rectal surgery (CRS) has unique needs with CDS that are not frequently described in the literature. The authors reviewed published literature in informatics and medical journals, combined with expert opinion to define CDS, describe the evidence for CDS, outline the implementation process for CDS, and present applications of CDS in CRS.CDS functionalities such as order sets, documentation templates, and order facilitation aids are most often described in the literature and most likely to be beneficial in CRS. Further research is necessary to identify and better evaluate additional CDS systems in the setting of CRS.

Validation of a guideline-based decision support system for the diagnosis of primary headache disorders based on ICHD-3 beta

Background

China may have the largest population of headache sufferers and therefore the most serious burden of disease worldwide. However, the rate of diagnosis for headache disorders is extremely low, possibly due to the relative complexity of headache subtypes and diagnostic criteria. The use of computerized clinical decision support systems (CDSS) seems to be a better choice to solve this problem.

Methods

We developed a headache CDSS based on ICHD-3 beta and validated it in a prospective study that included 543 headache patients from the International Headache Center at the Chinese PLA General hospital, Beijing, China.

Results

We found that the CDSS correctly recognized 159/160 (99.4%) of migraine without aura, 36/36 (100%) of migraine with aura, 20/21 (95.2%) of chronic migraine, and 37/59 (62.7%) of probable migraine. This system also correctly identified 157/180 (87.2%) of patients with tension-type headache (TTH), of which infrequent episodic TTH was diagnosed in 12/13 (92.3%), frequent episodic TTH was diagnosed in 99/101 (98.0%), chronic TTH in 18/20 (90.0%), and probable TTH in 28/46 (60.9%). The correct diagnostic rates of cluster headache and new daily persistent headache (NDPH) were 90.0% and 100%, respectively. In addition, the system recognized 32/32 (100%) of patients with medication overuse headache.

Conclusions

With high diagnostic accuracy for most of the primary and some types of secondary headaches, this system can be expected to help general practitioners at primary hospitals improve diagnostic accuracy and thereby reduce the burden of headache in China.

Technical desiderata for the integration of genomic data with clinical decision support

The ease with which whole genome sequence (WGS) information can be obtained is rapidly approaching the point where it can become useful for routine clinical care. However, significant barriers will inhibit widespread adoption unless clinicians are able to effectively integrate this information into patient care and decision-making. Electronic health records (EHR) and clinical decision support (CDS) systems may play a critical role in this integration. A previously published technical desiderata focused primarily on the integration of genomic data into the EHR. This manuscript extends the previous desiderata by specifically addressing needs related to the integration of genomic information with CDS. The objective of this study is to develop and validate a guiding set of technical desiderata for supporting the clinical use of WGS through CDS. A panel of domain experts in genomics and CDS developed a proposed set of seven additional requirements. These desiderata were reviewed by 63 experts in genomics and CDS through an online survey and refined based on the experts' comments. These additional desiderata provide important guiding principles for the technical development of CDS capabilities for the clinical use of WGS information.

Clinical decision support: effectiveness in improving quality processes and clinical outcomes and factors that may influence success

The use of electronic health records has skyrocketed following the 2009 HITECH Act, which provides financial incentives to health care providers for the "meaningful use" of electronic medical record systems. An important component of the "Meaningful Use" legislation is the integration of Clinical Decision Support Systems (CDSS) into the computerized record, providing up-to-date medical knowledge and evidence-based guidance to the physician at the point of care. As reimbursement is increasingly tied to process and clinical outcomes, CDSS will be integral to future medical practice. Studies of CDSS indicate improvement in preventive services, appropriate care, and clinical and cost outcomes with strong evidence for CDSS effectiveness in process measures. Increasing provider adherence to CDSS recommendations is essential in improving CDSS effectiveness, and factors that influence adherence are currently under study.

The long road to semantic interoperability in support of public health: experiences from two states

Proliferation of health information technologies creates opportunities to improve clinical and public health, including high quality, safer care and lower costs. To maximize such potential benefits, health information technologies must readily and reliably exchange information with other systems. However, evidence from public health surveillance programs in two states suggests that operational clinical information systems often fail to use available standards, a barrier to semantic interoperability. Furthermore, analysis of existing policies incentivizing semantic interoperability suggests they have limited impact and are fragmented. In this essay, we discuss three approaches for increasing semantic interoperability to support national goals for using health information technologies. A clear, comprehensive strategy requiring collaborative efforts by clinical and public health stakeholders is suggested as a guide for the long road towards better population health data and outcomes.

A combined paging alert and web-based instrument alters clinician behavior and shortens hospital length of stay in acute pancreatitis

OBJECTIVES

There are many published clinical guidelines for acute pancreatitis (AP). Implementation of these recommendations is variable. We hypothesized that a clinical decision support (CDS) tool would change clinician behavior and shorten hospital length of stay (LOS).

DESIGN/SETTING

Observational study, entitled, The AP Early Response (TAPER) Project. Tertiary center emergency department (ED) and hospital.

PARTICIPANTS

Two consecutive samplings of patients having ICD-9 code (577.0) for AP were generated from the emergency department (ED) or hospital admissions. Diagnosis of AP was based on conventional Atlanta criteria. The Pre-TAPER-CDS-Tool group (5/30/06-6/22/07) had 110 patients presenting to the ED with AP per 976 ICD-9 (577.0) codes and the Post-TAPER-CDS-Tool group (5/30/06-6/22/07) had 113 per 907 ICD-9 codes (7/14/10-5/5/11).

INTERVENTION

The TAPER-CDS-Tool, developed 12/2008-7/14/2010, is a combined early, automated paging-alert system, which text pages ED clinicians about a patient with AP and an intuitive web-based point-of-care instrument, consisting of seven early management recommendations.

RESULTS

The pre- vs. post-TAPER-CDS-Tool groups had similar baseline characteristics. The post-TAPER-CDS-Tool group met two management goals more frequently than the pre-TAPER-CDS-Tool group: risk stratification (P<0.0001) and intravenous fluids >6L/1st 0-24 h (P=0.0003). Mean (s.d.) hospital LOS was significantly shorter in the post-TAPER-CDS-Tool group (4.6 (3.1) vs. 6.7 (7.0) days, P=0.0126). Multivariate analysis identified four independent variables for hospital LOS: the TAPER-CDS-Tool associated with shorter LOS (P=0.0049) and three variables associated with longer LOS: Japanese severity score (P=0.0361), persistent organ failure (P=0.0088), and local pancreatic complications (<0.0001).

CONCLUSIONS

The TAPER-CDS-Tool is associated with changed clinician behavior and shortened hospital LOS, which has significant financial implications.

Systematic review of clinical decision support interventions with potential for inpatient cost reduction

Background

Healthcare costs are increasing rapidly and at an unsustainable rate in many countries, and inpatient hospitalizations are a significant driver of these costs. Clinical decision support (CDS) represents a promising approach to not only improve care but to reduce costs in the inpatient setting. The purpose of this study was to systematically review trials of CDS interventions with the potential to reduce inpatient costs, so as to identify promising interventions for more widespread implementation and to inform future research in this area.

Methods

To identify relevant studies, MEDLINE was searched up to July 2013. CDS intervention studies with the potential to reduce inpatient healthcare costs were identified through titles and abstracts, and full text articles were reviewed to make a final determination on inclusion. Relevant characteristics of the studies were extracted and summarized.

Results

Following a screening of 7,663 articles, 78 manuscripts were included. 78.2% of studies were controlled before-after studies, and 15.4% were randomized controlled trials. 53.8% of the studies were focused on pharmacotherapy. The majority of manuscripts were published during or after 2008. 70.5% of the studies resulted in statistically and clinically significant improvements in an explicit financial measure or a proxy financial measure. Only 12.8% of the studies directly measured the financial impact of an intervention, whereas the financial impact was inferred in the remainder of studies. Data on cost effectiveness was available for only one study.

Conclusions

Significantly more research is required on the impact of clinical decision support on inpatient costs. In particular, there is a remarkable gap in the availability of cost effectiveness studies required by policy makers and decision makers in healthcare systems.

Integrating pharmacogenetic information and clinical decision support into the electronic health record

Pharmacogenetics (PG) examines gene variations for drug disposition, response, or toxicity. At the National Institutes of Health Clinical Center (NIH CC), a multidepartment Pharmacogenetics Testing Implementation Committee (PGTIC) was established to develop clinical decision support (CDS) algorithms for abacavir, carbamazepine, and allopurinol, medications for which human leukocyte antigen (HLA) variants predict severe hypersensitivity reactions. Providing PG CDS in the electronic health record (EHR) during order entry could prevent adverse drug events. Medical Logic Module (MLM) programming was used to implement PG CDS in our EHR. The MLM checks to see if an HLA sequence-based gene test is ordered. A message regarding test status (result present, absent, pending, or test not ordered) is displayed on the order form, and the MLM determines if the prescriber can place the order, place it but require an over-ride reason, or be blocked from placing the order. Since implementation, more than 725 medication orders have been placed for over 230 patients by 154 different prescribers for the three drugs included in our PG program. Prescribers commonly used an over-ride reason when placing the order mainly because patients had been receiving the drug without reaction before implementation of the CDS program. Successful incorporation of PG CDS into the NIH CC EHR required a coordinated, interdisciplinary effort to ensure smooth activation and a positive effect on patient care. Prescribers have adapted to using the CDS and have ordered PG testing as a direct result of the implementation.

Enabling cross-platform clinical decision support through Web-based decision support in commercial electronic health record systems: proposal and evaluation of initial prototype implementations

Enabling clinical decision support (CDS) across multiple electronic health record (EHR) systems has been a desired but largely unattained aim of clinical informatics, especially in commercial EHR systems. A potential opportunity for enabling such scalable CDS is to leverage vendor-supported, Web-based CDS development platforms along with vendor-supported application programming interfaces (APIs). Here, we propose a potential staged approach for enabling such scalable CDS, starting with the use of custom EHR APIs and moving towards standardized EHR APIs to facilitate interoperability. We analyzed three commercial EHR systems for their capabilities to support the proposed approach, and we implemented prototypes in all three systems. Based on these analyses and prototype implementations, we conclude that the approach proposed is feasible, already supported by several major commercial EHR vendors, and potentially capable of enabling cross-platform CDS at scale.

Stakeholder engagement: a key component of integrating genomic information into electronic health records

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.

Development and use of active clinical decision support for preemptive pharmacogenomics

Background

Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care.

Objective

We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively.

Materials and methods

Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge.

Results

Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued.

Conclusions

Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.

Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis

As studies have pointed out, severity scores are imperfect at predicting individual clinical chance of survival. The clinical condition and pathophysiological status of these patients in the Intensive Care Unit might differ from or be more complicated than most predictive models account for. In addition, as the pathophysiological status changes over time, the likelihood of survival day by day will vary. Actually, it would decrease over time and a single prediction value cannot address this truth. Clearly, alternative models and refinements are warranted. In this study, we used discrete-time-event models with the changes of clinical variables, including blood cell counts, to predict daily probability of mortality in individual patients from day 3 to day 28 post Intensive Care Unit admission. Both models we built exhibited good discrimination in the training (overall area under ROC curve: 0.80 and 0.79, respectively) and validation cohorts (overall area under ROC curve: 0.78 and 0.76, respectively) to predict daily ICU mortality. The paper describes the methodology, the development process and the content of the models, and discusses the possibility of them to serve as the foundation of a new bedside advisory or alarm system.

Communicating risk of hereditary breast and ovarian cancer with an interactive decision support tool

OBJECTIVE

Women with hereditary breast and ovarian cancer syndrome (HBOC) face a higher risk of earlier, more aggressive cancer. Because of HBOC's rarity, screening is recommended only for women with strong cancer family histories. However, most patients do not have accurate history available and struggle to understand genetic concepts.

METHODS

Cancer in the Family, an online clinical decision support tool, calculated women's HBOC risk and promoted shared patient-provider decisions about screening. A pilot evaluation (n=9 providers, n=48 patients) assessed the tool's impact on knowledge, attitudes, and screening decisions. Patients used the tool before wellness exams and completed three surveys. Providers accessed the tool during exams, completed exam checklists, and completed four surveys.

RESULTS

Patients entered complete family histories (67%), calculated personal risk (96%), and shared risk printouts with providers (65%). HBOC knowledge increased dramatically for patients and providers, and many patients (75%) perceived tool results as valid. The tool prompted patient-provider discussions about HBOC risk and cancer family history (88%).

CONCLUSIONS

The tool was effective in increasing knowledge, collecting family history, and sparking patient-provider discussions about HBOC screening.

PRACTICE IMPLICATIONS

Interactive tools can effectively communicate personalized risk and promote shared decisions, but they are not a substitute for patient-provider discussions.

The wave has finally broken: now what?

In 2005, the authors published a paper, 'Will the wave finally break? A brief view of the adoption of electronic medical records in the United States', which predicted that rapid adoption of electronic health records (EHR) would occur in the next 5 years given appropriate incentives. The wave has finally broken with the stimulus of the health information technology for economic and clinical health legislation in 2009, and there have been both positive and negative developments in the ensuing years. The positive developments, among others described, are increased adoption of EHR, the emergence of a national network infrastructure and the recognition of clinical informatics as a medical specialty. Problems that still exist include, among others described, continued user interface problems, distrust of EHR-generated notes and an increased potential for fraud and abuse. It is anticipated that in the next 5 years there will be near universal EHR adoption, greater emphasis on standards and interoperability, greater involvement of Congress in health information technology (IT), breakthroughs in user interfaces, compelling online medical and IT education, both increased use of data analytics for personalized healthcare and a realization of the difficulties of this approach, a blurring of the distinction between EHR and telemedicine, a resurgence of computer-assisted diagnosis and the emergence of a 'continuously learning' healthcare system.

Towards public health decision support: a systematic review of bidirectional communication approaches

OBJECTIVE

To summarize the literature describing computer-based interventions aimed at improving bidirectional communication between clinical and public health.

MATERIALS AND METHODS

A systematic review of English articles using MEDLINE and Google Scholar. Search terms included public health, epidemiology, electronic health records, decision support, expert systems, and decision-making. Only articles that described the communication of information regarding emerging health threats from public health agencies to clinicians or provider organizations were included. Each article was independently reviewed by two authors.

RESULTS

Ten peer-reviewed articles highlight a nascent but promising area of research and practice related to alerting clinicians about emerging threats. Current literature suggests that additional research and development in bidirectional communication infrastructure should focus on defining a coherent architecture, improving interoperability, establishing clear governance, and creating usable systems that will effectively deliver targeted, specific information to clinicians in support of patient and population decision-making.

CONCLUSIONS

Increasingly available clinical information systems make it possible to deliver timely, relevant knowledge to frontline clinicians in support of population health. Future work should focus on developing a flexible, interoperable infrastructure for bidirectional communications capable of integrating public health knowledge into clinical systems and workflows.

Clinical decision support system and incidence of delirium in cognitively impaired older adults transferred to intensive care

BACKGROUND

Elderly patients with cognitive impairment are at increased risk of developing delirium, especially in the intensive care unit.

OBJECTIVE

To evaluate the efficacy of a computer-based clinical decision support system that recommends consulting a geriatrician and discontinuing use of urinary catheters, physical restraints, and unnecessary anticholinergic drugs in reducing the incidence of delirium.

METHODS

Data for a subgroup of patients enrolled in a large clinical trial who were transferred to the intensive care units of a tertiary-care, urban public hospital in Indianapolis were analyzed. Data were collected on frequency of orders for consultation with a geriatrician; discontinuation of urinary catheterization, physical restraints, or anticholinergic drugs; and the incidence of delirium.

RESULTS

The sample consisted of 60 adults with cognitive impairment. Mean age was 74.6 years; 45% were African American, and 52% were women. No differences were detected between the intervention and the control groups in orders for consultation with a geriatrician (33% vs 40%; P = .79) or for discontinuation of urinary catheters (72% vs 76%; P = .99), physical restraints (12% vs 0%; P=.47), or anticholinergic drugs (67% vs 36%; P=.37). The 2 groups did not differ in the incidence of delirium (27% vs 29%; P = .85).

CONCLUSION

Use of a computer-based clinical decision support system may not be effective in changing prescribing patterns or in decreasing the incidence of delirium.

The next-generation electronic health record: perspectives of key leaders from the US Department of Veterans Affairs

The rapid change in healthcare has focused attention on the necessary development of a next-generation electronic health record (EHR) to support system transformation and more effective patient-centered care. The Department of Veterans Affairs (VA) is developing plans for the next-generation EHR to support improved care delivery for veterans. To understand the needs for a next-generation EHR, we interviewed 14 VA operational, clinical and informatics leaders for their vision about system needs. Leaders consistently identified priorities for development in the areas of cognitive support, information synthesis, teamwork and communication, interoperability, data availability, usability, customization, and information management. The need to reconcile different EHR initiatives currently underway in the VA, as well as opportunities for data sharing, will be critical for continued progress. These findings may support the VA's effort for evolutionary change to its information system and draw attention to necessary research and development for a next-generation information system and EHR nationally.

Medical Informatics and Bioinformatics

In the last 50 years, computational applications have been developed to aid clinicians and researchers alike in the broad field of Biomedicine. Adopted early in the evolution of the field, the term medical informatics has been applied to the various sub-disciplines of computer applications and methods of organizing and using information principles and techniques in both clinical care and biomedical research. This chapter provides a broad survey of the complex discipline of Biomedical Informatics with special emphasis on the key emerging sub-disciplines such as translational informatics, clinical research informatics, consumer health informatics, and the informatics of the “omics” sciences, systems biology, and nanotechnology.

A pilot study of distributed knowledge management and clinical decision support in the cloud

OBJECTIVE

Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users.

MATERIALS AND METHODS

The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons.

RESULTS

During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability.

DISCUSSION

Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines.

CONCLUSION

Decision support in the cloud is feasible and may be a reasonable path toward achieving better support of clinical decision-making across the widest range of health care providers.

Provider use of and attitudes towards an active clinical alert: a case study in decision support

BACKGROUND

In a previous study, we reported on a successful clinical decision support (CDS) intervention designed to improve electronic problem list accuracy, but did not study variability of provider response to the intervention or provider attitudes towards it. The alert system accurately predicted missing problem list items based on health data captured in a patient's electronic medical record.

OBJECTIVE

To assess provider attitudes towards a rule-based CDS alert system as well as heterogeneity of acceptance rates across providers.

METHODS

We conducted a by-provider analysis of alert logs from the previous study. In addition, we assessed provider opinions of the intervention via an email survey of providers who received the alerts (n = 140).

RESULTS

Although the alert acceptance rate was 38.1%, individual provider acceptance rates varied widely, with an interquartile range (IQR) of 14.8%-54.4%, and many outliers accepting none or nearly all of the alerts they received. No demographic variables, including degree, gender, age, assigned clinic, medical school or graduation year predicted acceptance rates. Providers' self-reported acceptance rate and perceived alert frequency were only moderately correlated with actual acceptance rates and alert frequency.

CONCLUSIONS

Acceptance of this CDS intervention among providers was highly variable but this heterogeneity is not explained by measured demographic factors, suggesting that alert acceptance is a complex and individual phenomenon. Furthermore, providers' self-reports of their use of the CDS alerting system correlated only modestly with logged usage.

A Patient-Based Needs Assessment for Living Well with Parkinson Disease: Implementation via Nominal Group Technique

Background. Parkinson's disease (PD) is a neurodegenerative condition with complex subtleties, making it challenging for physicians to fully inform their patients. Given that approximately 50% of Americans access the Internet for health information, the development of a multimedia, web-based application emphasizing targeted needs of people with Parkinson's disease (PwP) has the potential to change patient's lives. Objectives. To determine what information PwP perceive could enhance their quality of life. Methods. Group sessions utilizing nominal group technique (NGT) were conducted. Participants were asked “what information do you want to know about that would help you live well with PD?” Silent generation of ideas preceded discussion followed by anonymous ranking of items. A “summary score” (sum of rank × frequency) was calculated. Results. 36 individual items were collapsed into 9 categories. Coping with emotions, changing relationships, and social implications of PD were ranked as most important. Financial supports and skills for self-advocacy were also highly ranked. Conclusions. Qualitative research methodology was utilized to determine the unmet needs of PwP. Results of this survey will inform the development of a patient-oriented, online resource, the goal will be to provide information and strategies to improve symptom management, reduce disability and address all relevant concerns important to those affected by PD.

Evaluation of user interface and workflow design of a bedside nursing clinical decision support system

Background

Clinical decision support systems (CDSS) are important tools to improve health care outcomes and reduce preventable medical adverse events. However, the effectiveness and success of CDSS depend on their implementation context and usability in complex health care settings. As a result, usability design and validation, especially in real world clinical settings, are crucial aspects of successful CDSS implementations.

Objective

Our objective was to develop a novel CDSS to help frontline nurses better manage critical symptom changes in hospitalized patients, hence reducing preventable failure to rescue cases. A robust user interface and implementation strategy that fit into existing workflows was key for the success of the CDSS.

Methods

Guided by a formal usability evaluation framework, UFuRT (user, function, representation, and task analysis), we developed a high-level specification of the product that captures key usability requirements and is flexible to implement. We interviewed users of the proposed CDSS to identify requirements, listed functions, and operations the system must perform. We then designed visual and workflow representations of the product to perform the operations. The user interface and workflow design were evaluated via heuristic and end user performance evaluation. The heuristic evaluation was done after the first prototype, and its results were incorporated into the product before the end user evaluation was conducted. First, we recruited 4 evaluators with strong domain expertise to study the initial prototype. Heuristic violations were coded and rated for severity. Second, after development of the system, we assembled a panel of nurses, consisting of 3 licensed vocational nurses and 7 registered nurses, to evaluate the user interface and workflow via simulated use cases. We recorded whether each session was successfully completed and its completion time. Each nurse was asked to use the National Aeronautics and Space Administration (NASA) Task Load Index to self-evaluate the amount of cognitive and physical burden associated with using the device.

Results

A total of 83 heuristic violations were identified in the studies. The distribution of the heuristic violations and their average severity are reported. The nurse evaluators successfully completed all 30 sessions of the performance evaluations. All nurses were able to use the device after a single training session. On average, the nurses took 111 seconds (SD 30 seconds) to complete the simulated task. The NASA Task Load Index results indicated that the work overhead on the nurses was low. In fact, most of the burden measures were consistent with zero. The only potentially significant burden was temporal demand, which was consistent with the primary use case of the tool.

Conclusions

The evaluation has shown that our design was functional and met the requirements demanded by the nurses’ tight schedules and heavy workloads. The user interface embedded in the tool provided compelling utility to the nurse with minimal distraction.

Development of information systems and clinical decision support systems for emergency departments: a long road ahead for Japan

Emergency care services face common challenges worldwide, including the failure to identify emergency illnesses, deviations from standard treatments, deterioration in the quality of medical care, increased costs from unnecessary testing, and insufficient education and training of emergency personnel. These issues are currently being addressed by implementing emergency department information systems (EDIS) and clinical decision support systems (CDSS). Such systems have been shown to increase the efficiency and safety of emergency medical care. In Japan, however, their development is hindered by a shortage of emergency physicians and insufficient funding. In addition, language barriers make it difficult to introduce EDIS and CDSS in Japan that have been created for an English-speaking market. This perspective addresses the key events that motivated a campaign to prioritise these services in Japan and the need to customise EDIS and CDSS for its population.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach

OBJECTIVE

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor".

METHODS

This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record.

RESULTS

The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs.

CONCLUSION

Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.

Contribuições dos registros eletrônicos para a segurança do paciente em terapia intensiva: uma revisão integrativa

Revisão integrativa que analisou nas publicações as contribuições dos registros eletrônicos em saúde para a segurança do paciente em unidades de terapia intensiva. A pesquisa foi realizada nas bases de dados CINAHL, MEDLINE e SciELO, utilizando os descritores: registros eletrônicos de saúde, sistemas de informação, informática em enfermagem, informática médica, unidades de terapia intensiva, segurança do paciente e gerenciamento de segurança. Foram incluídos 64 artigos, sendo analisados em três categorias: "sistemas de informação e informática em saúde: o registro eletrônico para a continuidade do cuidado de Enfermagem", "sistemas de apoio à decisão: contribuições para a segurança do paciente" e "indicadores de qualidade do cuidado e de segurança do paciente partir dos registros eletrônicos". Os estudos apontaram como contribuições a continuidade do cuidado, a tomada de decisão baseada nos sistemas de apoio à decisão e a criação de indicadores de qualidade e segurança do paciente a partir dos registros eletrônicos.

Using nursing clinical decision support systems to achieve meaningful use

The Health Information Technology and Clinical Health Act (one component of the American Recovery and Reinvestment Act) is responsible for providing incentive payments to hospitals and eligible providers in an effort to support the adoption of electronic health records. Future penalties are planned for electronic health record noncompliance. In order to receive incentives and avoid penalties, hospitals and eligible providers must demonstrate "meaningful use" of their electronic health records. One of the meaningful-use objectives established by the Centers for Medicare & Medicaid Services involves the use of a clinical decision support rule that addresses a hospital-defined, high-priority condition. This article describes the Plan-Do-Study-Act process for creating and implementing a nursing clinical decision support system designed to improve guideline adherence for hypoglycemia management. This project identifies hypoglycemia management as the high-priority area. However, other facilities with different high-priority conditions may find the process presented in this article useful for implementing additional clinical decision support rules geared toward improving outcomes and meeting federal mandates.

Clinical information system services and capabilities desired for scalable, standards-based, service-oriented decision support: consensus assessment of the Health Level 7 clinical decision support Work Group

A standards-based, service-oriented architecture for clinical decision support (CDS) has the potential to significantly enhance CDS scalability and robustness. To enable such a CDS architecture, the Health Level 7 CDS Work Group reviewed the literature, hosted multi-stakeholder discussions, and consulted domain experts to identify and prioritize the services and capabilities required from clinical information systems (CISs) to enable service-oriented CDS. In addition, relevant available standards were identified. Through this process, ten CIS services and eight CIS capabilities were identified as being important for enabling scalable, service-oriented CDS. In particular, through a survey of 46 domain experts, five services and capabilities were identified as being especially critical: 1) the use of standard information models and terminologies; 2) the ability to leverage a Decision Support Service (DSS); 3) support for a clinical data query service; 4) support for an event subscription and notification service; and 5) support for a user communication service.

The implementation and acceptability of an HPV vaccination decision support system directed at both clinicians and families

We developed an electronic medical record (EMR)-based HPV vaccine decision support intervention targeting clinicians, (immunization alerts, education, and feedback) and families (phone reminders and referral to an educational website). Through telephone surveys completed by 162 parents of adolescent girls, we assessed the acceptability of the family-focused intervention and its effect on information-seeking behavior, communication, and HPV vaccine decision-making. The intervention was acceptable to parents and 46% remembered receiving the reminder call. Parents reported that the call prompted them to seek out information regarding the HPV vaccine, discuss the vaccine with friends and family, and reach a decision. Parents whose adolescent girls attended practices receiving the clinician-focused intervention were more likely to report that their clinician discussed the HPV vaccine at preventive visits. The results of this study demonstrate the acceptability and potential impact on clinical care of a comprehensive decision support system directed at both clinicians and families.