Artificial Intelligence Of Medicine ( Aim )

Recently technology has become a significant part of society, specifically for the medical field. People in the past have expressed concerns about the security and safety of implementing artificial intelligence (AI) into the medical field. Artificial intelligence is a computer system with human capabilities, such as decision making. Research has shown that AI could increase the efficiency and quality of patient care in the medical field. AI could greatly improve efficiency by using software that can analyze all of the symptoms the patient has and the patient’s family history in a shorter period of time than a human doctor could. For the time period from 2000 to 2010 the conversation about artificial intelligence was focused on the ethical

Due to the large number of consumers being prescribed multiple medications, and the complexity of managing those medications, it is of a major safety concern that systems are in place for clinicians to reconcile patients medications to resolve any discrepancies in what the patient is using, or should be using, and newly added ones.

These systems will also help cut down on medication errors by comparing the patient’s to medications or interventions so that it is given to the correct patient. Also documents the care given so there would be no human error in the case of questioning whether care had been given as long as the caregiver documents in the record. These features of the electronic health record are in place to promote patient safety by reducing errors.

The argument of the author consists of the limitations of the practical model of diagnosis, which have current systems based on the AI. Salvado underlines, even throughout the significant gap between the amount of information of which the computer may know, compared to the human brain, there is still a great difference between the accuracy of diagnosis provided by a real doctor and a program. The examples provided by the author brightly support his argument and show disadvantages and gaps in the current model of artificial intelligence technology application.

An interview with an Assistant Professor at Duke University Health System in the Department of Medicine, Maestro Care Provider Champion and Clinical Content Architect. This physician works to incorporate clinical decision support tools into the electronic health record at Duke Health System. He manages the best practice advisory committee that may provide a way to deploy alerts to clinicians at the point of care. Alerts with order sets and recommended actions are created and updated to notify providers of current patient care guidelines or patient safety concerns.

The healthcare industry has evolved in the technology age from medical devices and advanced surgeries to the implementation of ICD-10 and the electronic health record. Progression is not only inevitable for the healthcare industry but also for society as a whole. This drives the expectation of increased workflow to ensure continuum of care is being met. A major concern that the Health Information Management (HIM) industry facing is the wrongful clinical documentation which contributes to coding errors. This causes rejected claims and inaccurate statistics which can affect a facility’s revenue and morale. With the ICD-10 transition here, there is an expectation of high standards in processing medical records while enduring the massive amount of workload that comes along with thousands of new codes. Fortunately, with technology by our side one can now utilize resources that aid in achieving the status quo and beyond. One of those resources is Computer Assisted Coding software.

The advent of the Electronic Health Record is ushering in a new era in advanced healthcare. The next 20 years will be an exciting time to be involved in health care as a Health Information Manager. Analyzing the myriad of health information which could reveal astonishing medical breakthroughs excites my imagination. I see the perfect pathway for my future. I am on track to graduate with Honors from Portland Community College’s Health Information Management Program in June 2016.

Information overload is a significant liability that a majority of the clinicians, working in the healthcare facilities within the United States of America, face. Due to the fact that the adoption of the EHR leads to the storage of virtually large amounts of data concerning patients; it is difficult for the clinicians to review all the relevant health information of the patients in a limited timeframe. In this case, a legal suite may ensue in the event that a clinician omits some crucial information relevant for treatment while reviewing records for a patient. The clinician could be sued for negligence with the understanding that the information was at his/her disposal (Blumenthal & Tavenner, 2013).

In order to utilize the electronic health record (EHR) system to match a patient to clinical trials, match patients to applicable clinical evidence and re-use of eligibility criteria from related clinical research studies on similar patients during the design of a new study, several eligibility criteria knowledge representations were developed(Weng, Tu, Sim, & Richesson, 2010), such as SAGE, Eligibility Rule Grammar and Ontology (EGRO) (Tu et al., 2011). Analysis of Common Eligibility Features (CEFs) (Boland & Weng, 2013) and clustering of similar eligibility criteria (Hao, Rusanov, Boland, & Weng, 2014; Luo, Yetisgen-Yildiz, & Weng, 2011) explored some applications of semantic similarity between clinical trial eligibility criteria.

The role of the public health is to detect a disease for preventing a disease, and the complexity of the public health is not able to detect a disease for not preventing a disease. The important element of public health is to provide adequate, timely medical intervention for tracking, monitoring, and controlling disease outbreak. However, the challenge is that medical intelligence that allows common and expected diseases or infectious agent at endemic level that usually present in a community to be disregarded to focus on deadly diseases or infectious agent at epidemic level for outbreak of biochemical agent that have the potential to cause mass harm internationally for the medical intelligence.

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

Accuracy and currency of data will be a particular challenge for the PCEHR when there are multiple sources of information. For example, medicines information may be contained in multiple documents (for example the shared health summary, specialists' letters and consumer-entered notes). The challenge will be to synchronise this information to build a complete picture of the medicines that are being taken. An electronic system will not replace the need for the patient–clinician interaction to confirm the validity of the information contained in the PCEHR. In addition, as medical care involves more use of, and reliance upon, electronically recorded information, the same robust processes of clinical governance must apply to it as to all other products used in the healthcare sector.10 Safety governance for clinical information systems is long overdue.10,

Machines with AI could be used in medical facilities to diagnose illnesses or to search for symptoms or the presence of anomalies in the bodies of patients (Keiser, 2017). Doctors constantly lament the delays in diagnosing diseases in a developing country. The use Artificial

Having a single view of the patient and their treatment and recovery plan is invaluable in ascertaining which are the most and least effective tactics in treatment. The 360-degree view of the patient and the many processes supporting them is crucial for increasing the accuracy, effectiveness and performance of treatment programs over time (Blakeman, 1985). Computerized management systems are critical for organizing, analyzing and translating the massive amount of data captured on patients, treatment and recovery processes, and the use of supporting IT systems to optimize patient health and organizational provider performance (Peshek, Cubera, Gleespen, 2010). The ability to aggregate and intelligently use all available data, information, patient-based and process-generated data to deliver higher levels of quality care is possible when computerized management systems are used throughout healthcare organizations.

When it comes to health care, diagnostic errors have been occurring since the beginning of time and most of the time they go unreported. As technology and medical knowledge increases between both the patient and provider diagnostic errors are becoming more frequent. One solution that was presented to help eliminate or decrease the frequency of these errors were the development of health information systems. In my opinion, I believe that information systems are ready to take on the challenge of reducing diagnostic errors, especially cognitive issues by the health professional (Croskerry, 2003). These systems will be able to help providers find the patient’s medical history faster, as well as contributing to the overall care of the patient in any facility across the region increasing efficiency in allocating resources regarding the delivery of care in all departments.