Clinical Decision Support System

Intermountain Healthcare has encircled analytics to improve operations in order to achieve better health care outcomes and make a big difference in patients’ lives.

Technology is always out there and improving. Many hospitals and practices have electronic health records. Electronic records make it easier for a patient to access their own records and to increase the quality of care for a person and their safety (Sittig & Singh, 2012). The purpose of this paper is to address electronic health records and the different steps a facility goes through to obtain an electronic health record

Health Information Management (HIM) is the process of protecting, analyzing, inspecting and acquiring medical information such as health records, each time a patient is seen by a healthcare provider. The HIM professional is an important connection between doctors, nurses, patients, insurance companies and everyone in the medical field. Every time a healthcare professional sees and treats a patient, they record what they observed, how the patient was treated medically, and future steps in the treatment plan discussed between the patient and the healthcare worker. The medical record includes the patient’s symptoms, medical history that includes past, present, and family history, results of studies, such as x-ray reports, or lab results, diagnosis,

1.Identify the problem being addressed and is it a new problem or a well known problem?

We must filter and customize that downloaded data for the health conditions that we primarily try to improve. Once data is customized and filtered properly, it gives us “care gaps”. Those care gaps can be easily closed out by accessing patient’s EMR or by referral. This updated data then gets uploaded back to the healthcare insurance company data set for reporting purpose. Data analytics helps health profession close the care gaps and improv care coordination between

Hennepin County Medical Center owns Epic system and EHR, and clinical applications. The clinical data is stored in the clinical data repository, and the data warehouse involves extracting and cleaning data from a variety of organizational databases. HC business partners access the EPIC system from a web application on the Citrix server. The Epic data system EHR, and clinical data flows through secured fire walls throughout the HC network. Data warehouses supports and transform enormous of data from single transactional files into single decision-backing database technology (K. Wagner, F.Lee, J. Glaser, 2013). Also, data mining is an IT concepts that Epic system has for extracting and identify specific clinical data. This transaction occurs when the tool is programmed to look for patterns, trends and/or trend rules. For example, North Point Health and Wellness Clinic render the following services: dental, mental health, primary care, lab, ex-rays, mammograms, vision and pharmacy services. The clinic does not have a radiologist on staff at the clinic to review and assess x-ray or mammogram performed at the clinic. Therefore, EHR files transmitted to HCMC, on a secured network and the data communication were compatible, because the devices and the computer networks were able to communicate with one another. The radiologist, result is entered in the clinical application clinical

Analyzed statistical report of outpatient and inpatient visits, admissions, dispositions, and other selected clinical workload data and presented in command meetings. Accurately reported communicable disease to military treatment facility and civilian health authorities. Improved accuracy in reporting procedures of clinical visits. Trained staff in reporting clinical visits properly. Ensured staff utilized new techniques/procedures and had appropriate clinical privileges prior to performing procedures and duties. Arranged and trained individual clinic demonstrations and training of Ambulatory Data System (ADS) and Composite Health Care System (CHCS) Working knowledge of CPT and ICD-9 Coding. Applied knowledge of administrative review

The core information system collects data from various sources and reorganizes it to optimize data presentation and facilitate physician work flow.

The good interaction between care providers and service users with the exchanging of information about conditions and diagnosis of clients is eased by using IT.

Technology has become an essential part of our everyday life therefore, it makes sense that doctors and hospitals get rid of the old fashioned paper charting and use technology to access patient records. Electronic health records (EHR) provide quick access to information, as doctors no longer have to wait for other providers to fax previous records to them. The accessibility of Electronic Health Records assist medical providers to make quick medical care decisions, by accessing previous care provided to patients including treatment and diagnosis. Quick access to information through EHR enables health care providers to treat patients faster as there is no need for records to be mailed or

The Health Information Technology for Economic and Clinical Health Act promoted the adoption and meaningful use of health information technology. This Act enacted as part of the American Recovery and Reinvestment Act of 2009. It encouraged the widespread use of electronic health records across the country; the largest in United States to date. The purpose of this paper will summarize the benefits of an Electronic Health Record. The three key functionalities of Electronic Health Records are computerized order entry systems, health information exchange and clinical decision support systems. Some benefits of an Electronic Health Records include: improved population health, improved quality, financial and operational benefits, the ability to conduct

Decision support system (DSS) is gaining increased recognition in healthcare organizations. This is due to an increasing recognition that a stronger DSS is crucial to achieve a high quality of patients care and safety.1,2 DSS is a class of computerized information system that supports decision-making activities.2 It uses patient data to provide tailored patient assessments and evidence-based treatment recommendations for healthcare providers to consider.2,3 DSS can vary greatly in design and function, undergoing a constant evolution of their scope and application.4 My favorite DSS is Isabel; I preferred this DSS to other DSSs based on the following reasons:

The need of barcode scanners issues relates to my selected advance role, as a Nursing Informaticist, because nursing has transformed through technology. Nursing informatics supports many area in healthcare. Nurse informaticist supports nurses, physicians, and patients in improving quality of care, through technology. As a nurse informaticists, one is able to be productive to facilitate change, improve continuity of care, and collaborate in decision-making by having the right communication tools or devices in place. Nursing are on the edge of moving beyond the electronic health record to a dynamic clinically intelligent system that can provide the nurse and other professionals with useable evidence-based data at point of care (Nickitas,

As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions.

The sinking of the RMS Titanic caused the death of thousands of passengers and crew is one of the deadliest maritime disasters in history. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there were some elements of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. The objective is to apply different machine learning models to complete the analysis of what sorts of people were likely to survive. The result of applying machine learning algorithms are compared and analysed on the basis of accuracy.