Impact of an Atrial Fibrillation Decision Support Tool on thromboprophylaxis for atrial fibrillation.

BACKGROUND: Appropriate thromboprophylaxis for patients with atrial fibrillation (AF) remains a national challenge. METHODS: We hypothesized that provision of decision support in the form of an Atrial Fibrillation Decision Support Tool (AFDST) would improve thromboprophylaxis for AF patients. We conducted a cluster randomized trial involving 15 primary care practices and 1,493 adults with nonvalvular AF in an integrated health care system between April 2014 and February 2015. Physicians in the intervention group received patient-level treatment recommendations made by the AFDST. Our primary outcome was the proportion of patients with antithrombotic therapy that was discordant from AFDST recommendation. RESULTS: Treatment was discordant in 42% of 801 patients in the intervention group. Physicians reviewed reports for 240 patients. Among these patients, thromboprophylaxis was discordant in 63%, decreasing to 59% 1 year later (P = .02). In nonstratified analyses, changes in discordant care were not significantly different between the intervention group and control groups. In multivariate regression models, assignment to the intervention group resulted in a nonsignificant trend toward decreased discordance (P = .29), and being a patient of a resident physician (P = .02) and a higher HAS-BLED score predicted decreased discordance (P = .03), whereas female gender (P = .01) and a higher CHADSVASc score (P = .10) predicted increased discordance. CONCLUSIONS: Among patients whose physicians reviewed recommendations of the decision support tool discordant therapy decreased significantly over 1 year. However, in nonstratified analyses, the intervention did not result in significant improvements in discordant antithrombotic therapy.

[Business intelligence in radiology. Challenges and opportunities]. / Business Intelligence in der Radiologie. Herausforderungen und Möglichkeiten.

Due to economic pressures and need for higher transparency, a ubiquitous availability of administrative information is needed. Therefore radiology managers should consider implementing business intelligence (BI) solutions. BI is defined as a systemic approach to support decision-making in business administration. It is an important part of the overall strategy of an organization. Implementation and operation is initially associated with costs and for a successful launch important prerequisites must be fulfilled. First, a suitable product must be selected, followed by the technical and organizational implementation. After consideration of the type of data to be collected and a system of key performance indicators must be established. BI replaces classic retrospective business reporting with multidimensional and multifactorial analyses, real-time monitoring, and predictive analyses. The benefits of BI include the rapid availability of important information and the depth of possible data analysis. The simple and intuitive use of modern BI applications by the users themselves (!) combined with a continuous availability of information is the key to success. Professional BI will be an important part of management in radiology in the future.

[Prioritization and Consentation of Criteria for the Appraisal, Funding and Evaluation of Projects from the German Innovationsfonds: A multi-perspective Delphi study]. / Priorisierung und Konsentierung von Begutachtungs-, Förder- und Evaluationskriterien für Projekte aus dem Innovationsfonds: Eine multiperspektivische Delphi-Studie.

BACKGROUND: The German Innovationsfonds provides the chance for evidence-based developments of the German healthcare system. OBJECTIVE: Prioritization of recommendations for an effective, efficient, fair, transparent, and sustainable granting of funds through a transparent, evidence-driven consensus-process involving all relevant stakeholder groups. METHODS: Representatives from health and research policy, payers, patient representatives, healthcare providers, and scientists were invited to nominate participants for an electronic 3 round iterative Delphi-study to prioritize the thematic focus, requirements concerning study methods, the team of applicants, evaluation, utilization of study results, and for the selection of reviewers. Criteria considered as relevant by at least 60% of the panel (consensus definition) in the first 2 Delphi rounds were rated as facultative, preferable, or obligatory criteria for project funding. Data were analyzed descriptively. ( REGISTRATION: Datenbank Versorgungsforschung Deutschland VfD_15_003561). RESULTS: All invited stakeholder groups except payers participated. 34 (85%) of 40 nominated representatives participated in the Delphi-study. A total of 64 criteria were consented as relevant for project review and funding concerning the thematic focus (n=28), methodological requirements (n=13), requirements for applicants (n=4), for the evaluation (n=4), utilization (n=6), and selection of peer reviewers (n=9). DISCUSSION: It is the collective responsibility of all stakeholders to spend the designated funds as efficient and sustainable as possible. The consented recommendations shall serve decision makers as a resource for the granting of funds and the evaluation of the Innovationsfonds.

A conceptual framework for organizational readiness to implement nutrition and physical activity programs in early childhood education settings.

Across multiple sectors, organizational readiness predicts the success of program implementation. However, the factors influencing readiness of early childhood education (ECE) organizations for implementation of new nutrition and physical activity programs is poorly understood. This study presents a new conceptual framework to measure organizational readiness to implement nutrition and physical activity programs in ECE centers serving children aged 0 to 5 years. The framework was validated for consensus on relevance and generalizability by conducting focus groups; the participants were managers (16 directors and 2 assistant directors) of ECE centers. The framework theorizes that it is necessary to have "collective readiness," which takes into account such factors as resources, organizational operations, work culture, and the collective attitudes, motivation, beliefs, and intentions of ECE staff. Results of the focus groups demonstrated consensus on the relevance of proposed constructs across ECE settings. Including readiness measures during program planning and evaluation could inform implementation of ECE programs targeting nutrition and physical activity behaviors.

Predefined three tier business intelligence architecture in healthcare enterprise.

Business Intelligence (BI) has caused extensive concerns and widespread use in gathering, processing and analyzing data and providing enterprise users the methodology to make decisions. Different from traditional BI architecture, this paper proposes a new BI architecture, Top-Down Scalable BI architecture with defining mechanism for enterprise decision making solutions and aims at establishing a rapid, consistent, and scalable multiple applications on multiple platforms of BI mechanism. The two opposite information flows in our BI architecture offer the merits of having the high level of organizational prospects and making full use of the existing resources. We also introduced the avg-bed-waiting-time factor to evaluate hospital care capacity.

Improving decision quality in healthcare with an Error Prevention Model (EPM).

Human factors involved in decision quality are critical issues in healthcare. In this paper, issues related to the impact of human factors on decision quality in healthcare are considered. Specifically, the focus is on the issue of reducing human error as well as improving decision quality. An Error Prevention Model (EPM) is presented for considering tools and techniques that can be used to analyze complex errors that may be considered latent.

A conceptual framework for automating the operational and strategic decision-making process in the health care delivery system.

Making reliable and justified operational and strategic decisions is a really challenging task in the health care domain. So far, the decisions have been made based on the experience of managers and staff, or they are evaluated with traditional methods, using inadequate data. As a result of this kind of decision-making process, attempts to improve operations usually have failed or led to only local improvements. Health care organizations have a lot of operational data, in addition to clinical data, which is the key element for making reliable and justified decisions. However, it is progressively problematic to access it and make usage of it. In this paper we discuss about the possibilities how to exploit operational data in the most efficient way in the decision-making process. We'll share our future visions and propose a conceptual framework for automating the decision-making process.

The evolution of eProtocols that enable reproducible clinical research and care methods.

Unnecessary variation in clinical care and clinical research reduces our ability to determine what healthcare interventions are effective. Reducing this unnecessary variation could lead to further healthcare quality improvement and more effective clinical research. We have developed and used electronic decision support tools (eProtocols) to reduce unnecessary variation. Our eProtocols have progressed from a locally developed mainframe computer application in one clinical site (LDS Hospital) to web-based applications available in multiple languages and used internationally. We use eProtocol-insulin as an example to illustrate this evolution. We initially developed eProtocol-insulin as a local quality improvement effort to manage stress hyperglycemia in the adult intensive care unit (ICU). We extended eProtocol-insulin use to translate our quality improvement results into usual clinical care at Intermountain Healthcare ICUs. We exported eProtocol-insulin to support research in other US and international institutions, and extended our work to the pediatric ICU. We iteratively refined eProtocol-insulin throughout these transitions, and incorporated new knowledge about managing stress hyperglycemia in the ICU. Based on our experience in the development and clinical use of eProtocols, we outline remaining challenges to eProtocol development, widespread distribution and use, and suggest a process for eProtocol development. Technical and regulatory issues, as well as standardization of protocol development, validation and maintenance, need to be addressed. Resolution of these issues should facilitate general use of eProtocols to improve patient care.

Evaluation of the Performance of Routine Information System Management (PRISM) framework: evidence from Uganda.

BACKGROUND: Sound policy, resource allocation and day-to-day management decisions in the health sector require timely information from routine health information systems (RHIS). In most low- and middle-income countries, the RHIS is viewed as being inadequate in providing quality data and continuous information that can be used to help improve health system performance. In addition, there is limited evidence on the effectiveness of RHIS strengthening interventions in improving data quality and use. The purpose of this study is to evaluate the usefulness of the newly developed Performance of Routine Information System Management (PRISM) framework, which consists of a conceptual framework and associated data collection and analysis tools to assess, design, strengthen and evaluate RHIS. The specific objectives of the study are: a) to assess the reliability and validity of the PRISM instruments and b) to assess the validity of the PRISM conceptual framework. METHODS: Facility- and worker-level data were collected from 110 health care facilities in twelve districts in Uganda in 2004 and 2007 using records reviews, structured interviews and self-administered questionnaires. The analysis procedures include Cronbach's alpha to assess internal consistency of selected instruments, test-retest analysis to assess the reliability and sensitivity of the instruments, and bivariate and multivariate statistical techniques to assess validity of the PRISM instruments and conceptual framework. RESULTS: Cronbach's alpha analysis suggests high reliability (0.7 or greater) for the indices measuring a promotion of a culture of information, RHIS tasks self-efficacy and motivation. The study results also suggest that a promotion of a culture of information influences RHIS tasks self-efficacy, RHIS tasks competence and motivation, and that self-efficacy and the presence of RHIS staff have a direct influence on the use of RHIS information, a key aspect of RHIS performance. CONCLUSIONS: The study results provide some empirical support for the reliability and validity of the PRISM instruments and the validity of the PRISM conceptual framework, suggesting that the PRISM approach can be effectively used by RHIS policy makers and practitioners to assess the RHIS and evaluate RHIS strengthening interventions. However, additional studies with larger sample sizes are needed to further investigate the value of the PRISM instruments in exploring the linkages between RHIS data quality and use, and health systems performance.

Emergency planning and long-term care: least paid, least powerful, most responsible.

As disasters can occur anywhere, planning to avoid emergencies is an international concern. Our research specifically addresses planning for the needs and safety of a vulnerable population, long-term care residents. Our initial purposes in this evaluation research were to assess the utility of a template to gather emergency management information for individual long-term care communities, to report on how prepared they are to cope with emergencies that have occurred elsewhere in areas like ours, and to assess the effectiveness of employing gerontology students in the planning process. As we began analyzing our data, we realized that it is imperative to consider whether it is possible for long-term care communities to respond effectively to disasters. In our findings we focus on the impact of gender in the planning process, the importance of size with regard to template utility, the positive and negative consequences of student aid, and the fact that gathering plans for individual long-term care communities may have detracted from collaborative community planning.

Implications of event entry latency on anesthesia information management decision support systems.

BACKGROUND: Decision support systems (DSSs) are being developed to use events entered in anesthesia information management systems (AIMS) for quality of care, compliance, billing, documentation, and management purposes. DSS performance is impacted by latency from the actual time an event occurs to when it is written to the database, as well as how often the database is queried. Such latencies may result in poor DSS recommendations. METHODS: We analyzed approximately 48,000 cases at Hospital A for latency of two DSS prototype events, Surgery Begin and Surgery End. Each latency was measured from 1) the time that the event was recorded in the AIMS database as having taken place to 2) the time when the first DSS query would have been executed after the documentation of that event by the provider. The effects on latency of 1, 5, and 10 min query intervals were determined. Latencies for Surgery Begin and Surgery End were compared with those of Hospital B, where a different AIMS was used. RESULTS: Network delays and the event processing time of the AIMS contributed <1 s and 30 s, respectively, to latency. Average latencies for the two studied events were approximately half of the query interval, the expected value if the events occurred randomly within each interval. However, the longest 5% of latencies exceeded the query interval. This was not due to providers editing the times of the Begin or End Surgery events, as each occurred in only 0.7% of cases. Although the median latencies for the two events were longer at Hospital B than Hospital A by a few minutes, the 90th and 95th percentiles of the latencies were much longer at Hospital B (8-30 min, depending on the query interval and the percentile). CONCLUSIONS: DSS performance is influenced by the timeliness of documentation, the incidence of missing documentation and the query interval. Facilities using a DSS, including electronic whiteboards showing patient status, should assess the latencies of the measured events and critique the influence of the latencies on clinical and managerial decisions.

Data mining in pharma sector: benefits.

PURPOSE: The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. DESIGN/METHODOLOGY/APPROACH: This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. FINDINGS: The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. RESEARCH LIMITATIONS/IMPLICATIONS: Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. ORIGINALITY/VALUE: Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Targeted business intelligence pays off.

Application business intelligence can accomplish much of what large-scale, enterprisewide efforts can accomplish: Focus on a variety of data that are interrelated in a meaningful way, Support decision making at multiple levels within a given organization, Leverage data that are already captured but not fully used, Provide actionable information and support quick response via a dashboard or control panel.

Description of a method to support public health information management: organizational network analysis.

In this case study, we describe a method that has potential to provide systematic support for public health information management. Public health agencies depend on specialized information that travels throughout an organization via communication networks among employees. Interactions that occur within these networks are poorly understood and are generally unmanaged. We applied organizational network analysis, a method for studying communication networks, to assess the method's utility to support decision making for public health managers, and to determine what links existed between information use and agency processes. Data on communication links among a health department's staff was obtained via survey with a 93% response rate, and analyzed using Organizational Risk Analyzer (ORA) software. The findings described the structure of information flow in the department's communication networks. The analysis succeeded in providing insights into organizational processes which informed public health managers' strategies to address problems and to take advantage of network strengths.

The effects of electronic documentation in the ambulatory surgery setting.

Electronic documentation can improve organizational processes in health care settings and may be of particular benefit to ambulatory surgery centers. A decision support system (DSS) can be integrated with an electronic documentation system. A DSS can identify potential errors and deviations from best practices and provide electronic alerts for health care clinicians to support patient screening and care. Barriers to implementation of a DSS include practitioner noncompliance with alerts and limitations in system design. Nurses can be instrumental in overcoming the barriers that prevent some clinicians from adopting these useful information systems.

Automatic generation of spoken dialogue from medical plans and ontologies.

This paper presents some research undertaken as part of the EU-funded HOMEY project, into the application of intelligent dialogue systems to healthcare systems. The work presented here concentrates on the ways in which knowledge of underlying task structure (e.g., a medical guideline) can be combined with ontological knowledge (e.g., medical semantic dictionaries) to provide a basis for the automatic generation of flexible and re-configurable dialogue. This approach is next evaluated via a specific application that provides decision support to general practitioners to help determine whether or not a patient should be referred to a cancer specialist. The competence of the resulting dialogue application, its speech recognition performance, and dialogue performance are all evaluated to determine the applicability of this approach.

Developing an evidence-based decision support system for rational insecticide choice in the control of African malaria vectors.

The emergence of Anopheles species resistant to insecticides widely used in vector control has the potential to impact directly on the control of malaria. This may have a particularly dramatic effect in Africa, where pyrethroids impregnated onto bed-nets are the dominant insecticides used for vector control. Because the same insecticides are used for crop pests, the extensive use and misuse of insecticides for agriculture has contributed to the resistance problem in some vectors. The potential for resistance to develop in African vectors has been apparent since the 1950s, but the scale of the problem has been poorly documented. A geographical information system-based decision support system for malaria control has recently been established in Africa and used operationally in Mozambique. The system incorporates climate data and disease transmission rates, but to date it has not incorporated spatial or temporal data on vector abundance or insecticide resistance. As a first step in incorporating this information, available published data on insecticide resistance in Africa has now been collated and incorporated into this decision support system. Data also are incorporated onto the openly available Mapping Malaria Risk in Africa (MARA) Web site (http://www.mara.org.za). New data, from a range of vector population-monitoring initiatives, can now be incorporated into this open access database to allow a spatial understanding of resistance distribution and its potential impact on disease transmission to benefit vector control programs.

Decision-Making to Support Smooth Daily Unit Operation in Perioperative Settings.

Decision-making in daily unit operation in perioperative settings needs to be smooth. Decision support systems are mainly used as help in this situation. These systems reduce the possibility of risks caused by poor communication. But the decisions and dimensions of the decisions made by nurse manager are still unsolved. The aim of our study was to describe the timeframe of the decisions made by nurse managers in the daily unit operation in perioperative settings. The results indicated that nurse managers made operational and tactical decisions. These operational and tactical decisions happened coincide during the nurse managers shift. The nurse managers were repeatly interrupted in decision-making.

U.S. Department of Defense Multiple-Parameter Biodosimetry Network.

The U.S. Department of Defense (USDOD) service members are at risk of exposure to ionizing radiation due to radiation accidents, terrorist attacks and national defense activities. The use of biodosimetry is a standard of care for the triage and treatment of radiation injuries. Resources and procedures need to be established to implement a multiple-parameter biodosimetry system coupled with expert medial guidance to provide an integrated radiation diagnostic system to meet USDOD requirements. Current USDOD biodosimetry capabilities were identified and recommendations to fill the identified gaps are provided. A USDOD Multi-parametric Biodosimetry Network, based on the expertise that resides at the Armed Forces Radiobiology Research Institute and the Naval Dosimetry Center, was designed. This network based on the use of multiple biodosimetry modalities would provide diagnostic and triage capabilities needed to meet USDOD requirements. These are not available with sufficient capacity elsewhere but could be needed urgently after a major radiological/nuclear event.

Decision Support System of Nursing Human Resources Allocation in General Wards Based on Hospital Information System.

AIM: To construct a Decision support system of nursing human resources allocation in general wards based on Hospital information system (HIS). METHOD: Time series prediction model and Information technical method were used based on data of HIS in West China Hospital, Sichuan University (Chengdu, P.R. China). RESULTS: This study completed the function design and system implementation of the nursing human resources allocation decision support system. DISCUSSION: The system would help nursing managers choose the optimal scheme and make scientific decisions in combination with "the actual" situation but more empirical studies are needed.