In the idōs CDSS, rules are programmed into the system (knowledge-based), while algorithms are used to model the decision (non-knowledge-based). Using machine learning or artificial intelligence rules and data structures, our process generates recommendations or actions tailored to the patient's clinical data, which are presented to the user (for example, a doctor). User interfaces are how a website, application, or EHR interacts with the end user.
There are many functions provided by idōs CDSS that include diagnostics, alerts, disease management, prescriptions (Rx), and drug control. In addition to alarms and reminders, clinical workflow tools may include order sets, patient data reports, documentation templates, and documentation templates.
idōs has created a digital library of disaster medicine and health threats and hazards. In it, digital threats and hazards from historic events are discussed by newsworthy events, local police departments, emergency operations centers, NASA, NOAA, and USGS sensors, and National Geospatial Intelligence Agency (NGA) datasets. Moreover, we've included media items from the National Library of Medicine's archives and from collaborating institutions. It also contains a database of descriptive, technical, and administrative metadata that can be accessed
We made sure the choices we made in building the digital library maximized sustainability and long-term studies of epidemiology and how certain threats and hazards may affect the environment around a person. Our digital library then includes that metadata and its components. Thus, nonproprietary software can improve sustainability--either through in-house expertise or through the open source community.
We've created proprietary operating systems, scanning software, search engines, and productivity software. We did this to meet the cost-benefit of maintenance, upgrades, and migration from obsolete digital files to a compatible format as the technological landscape changed. The fact that we had to keep a proprietary operating system allowed us to keep the cost of product licensing, product support, incompatibility with other software, prohibited use due to changing security policies, and product abandonment on us.
NIH, known as the National Institutes of Health, is an agency of the Department of Health and Human Services from within the United States of America. As the nation's leading medical research agency, it is dedicated to improving health and saving lives through the discovery of significant scientific discoveries.
Scientists at NIH study living systems to find out how they work. In order to reduce the burden of disease and disability, NIH applies that knowledge to extend healthy lives. The NIH develops and supports medical libraries, trains medical librarians and other health information professionals, and collects, disseminates, and exchanges medical and health information.
To attain the idōs mission, it understand that the NIH conducts and supports research that helps to improve the health of the nation by:
Reducing incidence of medication/prescribing errors and adverse events.
The most critical topic of the CDSS is patient safety. The implications of CDSS for patient safety are paramount and cannot be overstated. By using our technology, we reduce errors related to prescribing, dosing, contraindications, drug-event monitoring, and other issues. Medication error reduction strategies are the backbone of the use of CDSS. Up to 65% of inpatients are exposed to one or more potentially harmful drug-drug interactions (DDIs), which are common and preventable. In idōs, drug safety algorithms prevent dosing errors, duplicate therapies, and DDIs.
Compliance with clinical guidelines, follow-up reminders, etc.
Clinical guidelines can be adhered to more effectively with CDSS, according to research. The problem is that traditional clinical guidelines and care pathways are hard to implement and clinicians don't follow them. It can also assist clinicians in identifying patients who are eligible for research and follow up with those who haven't followed their treatment plan, as well as identifying patients who are not adhering to their treatment plan.
Increasing provider productivity by reducing duplicate tests and orders, suggesting cheaper medications or treatment options, automating tedious steps, etc.
Through clinical interventions, computer prescriber order entry (CPOE) integrated systems suggesting cheaper medication alternatives, or reducing test duplication, CDSS can be cost-effective for health systems. In addition to information about cheaper alternatives to drugs, CDSS can provide information about conditions that are covered by insurance.
Selecting diagnostic codes, automating documentation, and auto-filling notes.
Establishes methods for acquiring and implementing relevant knowledge, streamlines processes for obtaining physician feedback, and instructs users on why certain data entry should be done a certain way. Over time, it is crucial to identify changes in performance and usage. Furthermore, it is imperative to monitor the quality of the data repository and to avoid conclusions based on corrupted or poor quality data before drawing conclusions.
Analyzing patient data to provide diagnostic suggestions, automating test results output.
The user should be able to verify the reason for the recommendation. Additionally, our system may ask why a recommendation was not followed in order to determine the source of mistrust. A computerized 'consultation' or 'filtering' step that hosts data/user selections, then outputs possible or probable diagnoses. idōs CDSS uses standards for structural and semantic interoperability, and major standards are being developed and improved by organizations like HL7.
Through their personal health records (PHR), patients have direct access to decision support.
We expect CDSS functionality to be integrated into electronic health records (EHRs) with the patient as the end user or 'manager'. CDS-supported Personal Health Records are a great starting point for integrating shared decision-making between patients and providers, especially since CDSS removes the 'lack of information' obstacle to patient participation. A growing focus has been placed on designing patient health records (PHRs) to facilitate shared decision making among patients and providers, as well as to serve as interactive tools to increase patient participation and knowledge as they have become more advanced with CDSS capabilities in the last few years.
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