Patient contact managers augment your system.
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Appointments and Schedules↗
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Results for “Reminder Systems”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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The use of medical logic modules at LDS hospital.
The development of medical knowledge bases for use in a clinical information system (HELP) has been an ongoing goal at LDS Hospital in Salt Lake City, Utah, for the past 25 years. In building our medical knowledge base we felt the need to implement a decision support syntax which could capture the logic of our experts in a way that was not only executable, but also easily read and shared by others. During these 25 years we defined several simple syntaxes to express this medical logic. Our current approach is to cooperate with international standards groups (ASTM) and use the Arden Syntax for medical logic modules. We are working with the 3M Corporation in the joint development of an Arden Compiler for HELP. We plan to use the Arden Syntax initially to support our alert/reminder system and computerized management protocols.
Artificial Intelligence↗
Prescribers' responses to alerts during medication ordering in the long term care setting.
OBJECTIVE: Computerized physician order entry with clinical decision support has been shown to improve medication safety in adult inpatients, but few data are available regarding its usefulness in the long-term care setting. The objective of this study was to examine opportunities for improving medication safety in that clinical setting by determining the proportion of medication orders that would generate a warning message to the prescriber via a computerized clinical decision support system and assessing the extent to which these alerts would affect prescribers' actions. DESIGN: The study was set within a randomized controlled trial of computerized clinical decision support conducted in the long-stay units of a large, academically-affiliated long-term care facility. In March 2002, a computer-based clinical decision support system (CDSS) was added to an existing computerized physician order entry (CPOE) system. Over a subsequent one-year study period, prescribers ordering drugs for residents on three resident-care units of the facility were presented with alerts; these alerts were not displayed to prescribers in the four control units. MEASUREMENTS: We assessed the frequency of drug orders associated with various categories of alerts across all participating units of the facility. To assess the impact of actually receiving an alert on prescriber behavior during drug ordering, we calculated separately for the intervention and control units the proportion of the alerts, within each category, that were followed by an appropriate action and estimated the relative risk of an appropriate action in the intervention units compared to the control units. RESULTS: During the 12 months of the study, there were 445 residents on the participating units of the facility, contributing 3,726 resident-months of observation time. During this period, 47,997 medication orders were entered through the CPOE system-approximately 9 medication orders per resident per month. 9,414 alerts were triggered (2.5 alerts per resident-month). The alert categories most often triggered were related to risks of central nervous system side-effects such as over-sedation (20%). Alerts for risk of drug-associated constipation (13%) or renal insufficiency/electrolyte imbalance (12%) were also common. Twelve percent of the alerts were related to orders for warfarin. Overall, prescribers who received alerts were only slightly more likely to take an appropriate action (relative risk 1.11, 95% confidence interval 1.00, 1.22). Alerts related to orders for warfarin or central nervous system side effects were most likely to engender an appropriate action, such as ordering a recommended laboratory test or canceling an ordered drug. CONCLUSION: Long-term care facilities must implement new system-level approaches with the potential to improve medication safety for their residents. The number of medication orders that triggered a warning message in this study suggests that CPOE with a clinical decision support system may represent one such tool. However, the relatively low rate of response to these alerts suggests that further refinements to such systems are required, and that their impact on medication errors and adverse drug events must be carefully assessed.
Decision Support Systems, Clinical↗
Developing high-specificity anti-hypertensive alerts by therapeutic state analysis of electronic prescribing records.
OBJECTIVE: This paper presents a model for analysis of chronic disease prescribing action over time in terms of transitions in status of therapy as indicated in electronic prescribing records. The quality of alerts derived from these therapeutic state transitions is assessed in the context of antihypertensive prescribing. DESIGN: A set of alert criteria is developed based on analysis of state-transition in past antihypertensive prescribing of a rural Australian General Practice. Thirty active patients coded as hypertensive with alerts on six months of previously un-reviewed prescribing, and 30 hypertensive patients without alerts, are randomly sampled and independently reviewed by the practice's two main general practice physicians (GPs), each GP reviewing 20 alert and 20 non-alert cases (providing 10 alert and 10 non-alert cases for agreement assessment). MEASUREMENTS: GPs provide blind assessment of quality of hypertension management and retrospective assessment of alert relevance. RESULTS: Alerts were found on 66 of 611 cases with coded hypertension with 37 alerts on the 30 sampled alert cases. GPs assessed alerting sensitivity as 74% (CI 52% - 89%) and specificity as 61% (CI 45% - 74%) for the sample, which is estimated as 26% sensitivity and 93% specificity for the antihypertensive population. Agreement between the GPs on assessment of alert relevance was fair (kappa = 0.37). CONCLUSIONS: Data-driven development of alerts from electronic prescribing records using analysis of therapeutic state transition shows promise for derivation of high-specificity alerts to improve the quality of chronic disease management activities.
Antihypertensive Agents↗
A framework for mobile healthcare answers to chronically ill outpatient non-adherence.
Non-adherence (also known as 'non-compliance') is a major barrier undermining healing efforts within out-of-hospital self-management programmes, resulting in waste of human and social resources. This study suggests a theoretical framework of activities through which mobile patient solutions might address non-adherence determinants in a broader context of clinical interventions. The goal of the paper is to explore a dilemma associated with such interventions: the uncertainty regarding the level of patient involvement and technology support. We follow a critical orientation approach in discussing this multi-faceted conundrum: we summarise the latest vision on adherence factors, we suggest several types of interventions through which mobile healthcare solutions could address them, and we explore in detail the dilemma of patient and technology roles. We conclude that there is no universally optimal solution, and practical conditions depending on patient, disease, treatment and healthcare system are determining factors in prescribing the level of patient involvement and technology support. Our work is intended to stimulate further research into the nature of mobile solutions in health care and, especially, into patient acceptance aspects, in an endeavour to contribute to improving adherence with minimum obtrusiveness.
Chronic Disease↗
Integrating pharmacokinetics into point-of-care information systems.
Computer-based patient care information systems (PCIS) have emerged as an integral component of healthcare organisations. Currently, 4 models of PCIS exist: the centralised model, the hub-and-spoke model, the network model, and the distributed model. The centralised model has the advantage of a central patient database; however, a major disadvantage of this model is the inability to easily interface with other software packages. The hub-and-spoke model links satellite or feeder systems into a mainframe computer; thus, each satellite has the ability to work independently. This system is limited by the ability to interface satellite systems with the mainframe computer. The network model works via a local area network (LAN) using client server technology which allows for high speed data access and transfer. The network model does not provide an integrated view of patient information and can access only 1 host system at a time. The distributed model is similar to the network model in design but provides for data and system integration via relational databases. This allows for the creation of a central data repository and support for decision-support tools. Computer-assisted decision support has the potential to significantly improve clinical decision-making. Six types of computer-assisted decision-support have been defined: alerting, interpreting, assisting, critiquing, diagnosing and managing. Software representing each type of decision-support software has been incorporated into clinical practice; however, with the exception of drug interaction programs, widespread incorporation of decision-support software into PCIS is uncommon. Clinical pharmacokinetic programs are a category of pharmacy-related decision-support software, and current clinical pharmacokinetic software systems can be categorised as interpreting, assisting or critiquing decision-support. Despite the potential for significant clinical contributions, the integration of clinical pharmacokinetic software into PCIS is uncommon. Most packages are available only as stand alone programs or as a module of a pharmacy information system. These packages usually maintain their own centralised database and require special file transfer protocols for integration. Although PCIS are becoming more commonplace, the integration of commercial clinical pharmacokinetic packages into PCIS is limited. New technology using standardised and relational databases should allow for easier integration in the future.
Computers, Mainframe↗
The use of an explanation algorithm in a clinical event monitor.
Clinical event monitors (CEMs) seek to improve patient care and reduce its cost by prompting clinicians to take actions that have these effects. To persuade clinicians to act, CEMs have used prewritten-text explanations. However, we encountered limitations of prewritten-text explanations in our CEM. Therefore, we decided to implement an advanced method for explanation (Suermondt's method for belief-network explanation). This method is promising, but whether it is generally applicable to all of clinical event monitoring and whether it is as efficacious as prewritten-text explanations remain areas for future research.
Algorithms↗
Design, implementation, use, and preliminary evaluation of SEBASTIAN, a standards-based Web service for clinical decision support.
Despite their demonstrated ability to improve care quality, clinical decision support systems are not widely used. In part, this limited use is due to the difficulty of sharing medical knowledge in a machine-executable format. To address this problem, we developed a decision support Web service known as SEBASTIAN. In SEBASTIAN, individual knowledge modules define the data requirements for assessing a patient, the conclusions that can be drawn using that data, and instructions on how to generate those conclusions. Using standards-based XML messages transmitted over HTTP, client decision support applications provide patient data to SEBASTIAN and receive patient-specific assessments and recommendations. SEBASTIAN has been used to implement four distinct decision support systems; an architectural overview is provided for one of these systems. Preliminary assessments indicate that SEBASTIAN fulfills all original design objectives, including the re-use of executable medical knowledge across diverse applications and care settings, the straightforward authoring of knowledge modules, and use of the framework to implement decision support applications with significant clinical utility.
Decision Making, Computer-Assisted↗
Managing the alert process at NewYork-Presbyterian Hospital.
Clinical decision support can improve the quality of care, but requires substantial knowledge management activities. At NewYork-Presbyterian Hospital in New York City, we have implemented a formal alert management process whereby only hospital committees and departments can request alerts. An explicit requestor, who will help resolve the details of the alert logic and the alert message must be identified. Alerts must be requested in writing using a structured alert request form. Alert requests are reviewed by the Alert Committee and then forwarded to the Information Systems department for a software development estimate. The model required that clinical committees and departments become more actively involved in the development of alerts than had previously been necessary. In the 12 months following implementation, 10 alert requests were received. The model has been well received. A lot of the knowledge engineering work has been distributed and burden has been removed from scarce medical informatics resources.
Decision Support Systems, Clinical↗
Preferences of interns and residents for E-mail, paging, or traditional methods for the delivery of different types of clinical information.
We elicited from medical house staff their preferences for e-mail and alphanumeric pager as communication channels for the delivery of 18 different types of clinical information about their inpatients. For each type, we calculated the proportion of users who preferred delivery by e-mail, pager, both, or neither (usual delivery). For 14/18 (78%) types, more users preferred delivery by pager than by the other options. For 2/18 (11%) types, e-mail was preferred. For 2/18 (11%) types, more users preferred redundant delivery using both channels. For no types did more users prefer neither, meaning that the information would be delivered by traditional channels, if any. We conclude that medical house staff in the inpatient setting prefer to receive many types of clinical information by pager. The reason may be that they otherwise would have to query clinical information systems for these data, which is wasteful of their time and introduces delays into the process of care. Additionally, we found significant inter-user variability, suggesting that it may be useful for the notification services of an enterprise to employ user profiles for the delivery of clinical information.
Attitude of Health Personnel↗
[Diagnosis of the somatisation disorder: usefulness of a mnemonic system].
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Humans↗
Setting up a foolproof follow-up system.
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Continuity of Patient Care↗
Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis.
CONTEXT: There have been numerous reports of interventions designed to improve the care of patients with diabetes, but the effectiveness of such interventions is unclear. OBJECTIVE: To assess the impact on glycemic control of 11 distinct strategies for quality improvement (QI) in adults with type 2 diabetes. DATA SOURCES AND STUDY SELECTION: MEDLINE (1966-April 2006) and the Cochrane Collaboration's Effective Practice and Organisation of Care Group database, which covers multiple bibliographic databases. Eligible studies included randomized or quasi-randomized controlled trials and controlled before-after studies that evaluated a QI intervention targeting some aspect of clinician behavior or organizational change and reported changes in glycosylated hemoglobin (HbA1c) values. DATA EXTRACTION: Postintervention difference in HbA1c values were estimated using a meta-regression model that included baseline glycemic control and other key intervention and study features as predictors. DATA SYNTHESIS: Fifty randomized controlled trials, 3 quasi-randomized trials, and 13 controlled before-after trials met all inclusion criteria. Across these 66 trials, interventions reduced HbA(1c) values by a mean of 0.42% (95% confidence interval [CI], 0.29%-0.54%) over a median of 13 months of follow-up. Trials with fewer patients than the median for all included trials reported significantly greater effects than did larger trials (0.61% vs 0.27%, P = .004), strongly suggesting publication bias. Trials with mean baseline HbA1c values of 8.0% or greater also reported significantly larger effects (0.54% vs 0.20%, P = .005). Adjusting for these effects, 2 of the 11 categories of QI strategies were associated with reductions in HbA(1c) values of at least 0.50%: team changes (0.67%; 95% CI, 0.43%-0.91%; n = 26 trials) and case management (0.52%; 95% CI, 0.31%-0.73%; n = 26 trials); these also represented the only 2 strategies conferring significant incremental reductions in HbA1c values. Interventions involving team changes reduced values by 0.33% more (95% CI, 0.12%-0.54%; P = .004) than those without this strategy, and those involving case management reduced values by 0.22% more (95% CI, 0.00%-0.44%; P = .04) than those without case management. Interventions in which nurse or pharmacist case managers could make medication adjustments without awaiting physician authorization reduced values by 0.80% (95% CI, 0.51%-1.10%), vs only 0.32% (95% CI, 0.14%-0.49%) for all other interventions (P = .002). CONCLUSIONS: Most QI strategies produced small to modest improvements in glycemic control. Team changes and case management showed more robust improvements, especially for interventions in which case managers could adjust medications without awaiting physician approval. Estimates of the effectiveness of other specific QI strategies may have been limited by difficulty in classifying complex interventions, insufficient numbers of studies, and publication bias.
Blood Glucose↗
Changing physician behavior: what does it take?
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Attitude of Health Personnel↗
Diabetes education via mobile text messaging.
Living with diabetes makes great educational demands on a family. We have tested the feasibility of using the mobile phone short message service (SMS) for reaching people with diabetes information. We also assessed user satisfaction and perceived pros and cons of the medium through interviews. Eleven parents of children with type 1 diabetes received messages for 11 weeks. The parents were positive about the system and said that they would like to continue to use it. The pop-up reminding effect of SMS messages in busy everyday life was noted as positive. Some parents experienced the messages as somewhat intrusive, arriving too often and at inconvenient times. The parents also noted the potential of the messages to facilitate communication with their adolescent children. The inability to store all of the messages or to print them out were seen as major disadvantages. Overall, the SMS seems to hold promise as means of delivering diabetes information.
Adolescent↗
Patient safety in guideline-based decision support for hypertension management: ATHENA DSS.
The Institute of Medicine recently issued a landmark report on medical error.1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.
Artificial Intelligence↗
Patient participation in EHR benefits.
Patient participation in EHR creation can significantly broaden the range of healthcare information in the record and, ultimately, improve clinical decision support. The care with which patient-authored information is handled, however, must equal the care devoted to clinician data entry and review. Web-based access, self-service management, consent delegation and agent permissions management are just a few of the features that will encourage active and positive patient involvement with their healthcare records and with their own health management.
Computer Security↗
Guideline interaction: a study of interactions among drug-disease contraindication rules.
To study the interactions among the drug-disease contraindiction guidelines in a real clinical setting by using clinical databases.
Contraindications↗