Analysis Of ADE

Clinical decision making is a complicated process that depends on human capability to provide full attention to memorize, and create enormous amounts of data to all areas. IT systems are able to access information, arrange them, and recognize links between them. Clinicians often ‘know’ information such as a patient’s allergies, drug interaction and if that drug is on recall.

The role of knowledge base in a Clinical Decision Support System is to reduce medical errors and provide the best quality healthcare available. Knowledge base refers to the practice of medicine based on the best available scientific evidence (JAMIA). Knowledgebase continues to expand and it is crucial in developing clinical decision support systems by encoding concepts coming from experts in the field to provide a way to analyze the data and generate expert advice. With a knowledgebase system in place healthcare providers will have a more focused diagnosis of the disease and the treatment that will need to take

Researcher believe that the availability of big data will be of great impact especially in the medical field. They believe that when medical records are easily available to Doctors, doctors will be able to provide immediate diagnosis and medical treatment to their patients. Using this data is a great benefit to Doctors because they can look up prior successful treatment from other patients to help treat new patient. The challenge Doctors may encounter with so much information is that they may become overwhelmed and incorrectly delays treatment or not provide treatment to their patients. Also, the searchable information can be misleading or misunderstood by the medical provider and he or she based on the searchable information can incorrectly providing the same treatment to all the patient with the same

Throughout the course of Health Information 371, we have looked at various tools that are used or could be used in the field to help design, build, and implement systems. These tools explored concepts that were mostly related to the medical standpoint on how to diagnose patient’s through decision-making techniques. This helps the health professional make an accurate diagnosis based off of evidence and not speculation. Within the Health Information field, as informatics specialists we need to consider all of these techniques when designing the systems, creating policies, and updating the servers. Having knowledge on these tools will assist us when creating technologies, which will give health professionals all the necessary techniques to give the patient the best possible care available. Four tools that I believe will have a significant potential to affect my approach while practicing

Clinical adverse drug event alert system is a unique system in so many ways in the sense it allows physicians reduce medication errors by alerting them when something goes wrong. This paper will talk about all the benefits that clinical adverse drug event alert system has to offer.

Clinical decision support (CDS) tools provide information to help providers diagnose patients and assist with managing treatment plans. CDS systems “range from online reference materials to guidelines, and alerts built in electronic prescribing and computerized physician order entry (CPOE)” (Menachemi & Collum, p.48). For example, when a physician prescribes new medications for a patient, the system could automatically do background checks and alert physicians about patient information such as allergies and conflicting drugs that could present problems.

NLP is one such technology that confronts the challenges of improving clinical research and medical care. In this study an automated system is developed, which integrates with NLP to annotate and analyze the data in large scale for the detection of venous thromboembolic disease. This approach may be used in many applications, such as eligibility screening in clinical trials or analysis of retrospective e-cohorts

The ability to predict patient’s condition is very important in hospital settings. Patients’ condition is based upon diverse factors like personal, psychological and other health problems. Different data mining algorithms are used to achieve the goal of predicting the patient’s condition.

Data mining companies and pharmaceutical manufacturers are able easily access information about physicians’ prescribing behaviors. For instance, IMS Health, an American healthcare IT company, sends weekly updates to pharmaceutical sales representatives outlining the drugs that physicians prescribe. Data collected over time can reveal a physician’s prescribing history, his or her preference for brand-name drugs, and the probability of prescribing new drugs. The data can help pharmaceutical companies identify which physicians prescribe their companies’ drugs and can reinforce the physicians’ choices if needed. Drug companies are able to find out exactly when a physician

In the clinical practice area of health care; clinical, imaging and bio-informatics help support making diagnoses and treatment planning for patients. As seen in the article by Frankovich et al., and the promotional video of the supercomputer Watson for oncology, the informatics systems sort through the data and is able to provide the best treatment plan possible

This would contain all essential health records on file including genetic analysis data and what it means with regards to specific drugs, when a check-up is needed from the doctor or reminders about appointments, reminders about when medication is required and needed for collection and an easy to function system where current medications can be recorded and able to be editing by the patient; this will also include an interaction indicator and also a small drug information database (similar to eMIMS). Some features that this technology would incorporate include a new medication function which will analyse current medications for any possible interactions that will also give advice about when to check with health care professionals. There will also be a function where personal genomic data is used to provide information about certain medications and whether it will be as effective or not, with a note about their potential risk of side effects. The phone application will have a barcode scanner function where the patient can scan medication barcodes and will come up with information on the drug, their personalised metabolism of the drug, any interactions and also other brand names of the drug to help decrease confusion. The design of this technology will have emphasise on ease of use and navigation (for the elderly) and also a customer service call

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

Both a Clinical Data Repository (CDR) and Clinical Data Warehouse (CDW) have many benefits and limitations as they save data in databases, which consolidates data from a range of clinical sources. The databases are optimized to allow physicians to retrieve data on a specific patient instead of trying to identify a common ethnic group, population, or characteristics of patients. Clinical Data Repository is used in hospital settings to track patients’ records and suggesting trends as well as observing diseases or illnesses. CDR's are specifically used in the pharmaceutical industry when prescribing medications. CDR has some positive characteristics, such as clinical laboratory test results, patient demographics, pharmacy information, radiology reports and images, discharge and transfer datas, ICD-9 codes, and discharge summaries. These benefits will help provide more knowledge about patients their medical conditions and their outcomes. Clinical Data Repository is well designed to deliver the EHR content to users to access patient information and to help save time by limiting paper records, advances results through traditional displays, and also individual patients are able to access their data quicker and easier. Outcomes of patient’s care will be monitored and analyzed patient’s health information, etc.

Millions of researches are being carried out across the world. Hence the scope is very limited for a person/organization to follow all the studies going on. Even if you track a study, it becomes a real problem to analyze data that is being updated continuously on that trial. Apart from this problem, there are many other obstacles like inaccurate results in search of a particular medical problem and even if you do find accurate results, it becomes hard to put all information at one place retrieved from various sources. In order to overcome the above problems, we are developing a one-stop clinical trials user-friendly application that addresses all the related problems.

Research shows that screening in both high risk and healthy populations provides an excellent opportunity for the early detection of cancers and creates greater opportunities and chances for successful treatment and cure of cancers (Schiffman, Fisher & Gibbs, 2015). However, at present, every screening test has its imitations, and improved methods of screening and managing knowledge for cancer is needed to attain better outcomes. Big data analytics and artificial intelligence offers much promise in this area. One area that has been signled out as a gap in effective cancer research and treatment is that most information hospitals record about patients is not being effectively used for better outcomes fr cancer patients (Guillen, 2017). cancer will be detected in far les shorter time in future using AI; present research shows that it is possible to accurately detect cancers in less tha a minute through the use of AI with very high levels of accuracy (Donnelly, 2017). Clinical decisions usually are made based on the results from the few persons that participate in these trials, usually just 3% of all patients so that the data collected about other 97% of patients is never used and this is an area big data, knowledge management (KM) and analytics can play a very huge role.