To determine the attitudes of internists and surgeons to problems in clinical information management and to the use of a computer database, a survey of 529 internists and 487 otolaryngologists was conducted. The survey group consisted of a national distribution of academic physicians, private practitioners, and residents. Results of 296 completed questionnaires were analyzed for this report. More than one third of all responders were not satisfied with their ability to keep up with new developments; less than 10% were very satisfied. Ninety percent thought that a computer database would improve their access to information in the literature, and 85% thought it would improve their practice of medicine. Among internists, randomized control trials (RCTs) were viewed as clearly the most useful source of data. Otolaryngologists indicated that RCTs and patient registries were both very helpful. Most physicians indicated significantly greater preference for literature summary and patient registry features over probability estimation capabilities of a computer database. Sources of data to be incorporated into a successful database would be different for systems designed for internists and otolaryngologists.

Physician Attitudes Toward Applications of Computer Data Base Systems

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

This article describes the process of developing an advanced pharmacogenetics clinical decision support at one of the United States’ leading pediatric academic medical centers. This system, called CHRISTINE, combines clinical and genetic data to identify the optimal drug therapy when treating patients with epilepsy or Attention Deficit Hyperactivity Disorder. In the discussion a description of clinical decision support systems is provided, along with an overview of neurocognitive computing and how it is applied in this setting.

https://journals.sagepub.com/doi/pdf/10.4137/BII.S2506

Personalizing Drug Selection Using Advanced Clinical Decision Support

National pilot data bank networks for stroke and traumatic coma have recently been initiated at multiple centers by the National Institute of Neurological and Communicative Disorders and Stroke. The characteristics of these pilot data bank projects include: 1) the overall development and statement of research issues by a multidisciplinary team; 2) dual emphasis on patient management and clinical research; 3) the definition and use of a uniform clinical vocabulary; 4) the use of a clinically-oriented data base management system; and 5) the use of intelligent terminals for data entry, retrieval, and patient management. This paper will describe the data bank approach used by the neurological disorders programs.

Pilot data bank networks for neurological disorders.

By the late 1960s, mainframe-based hospital information systems (HISs) had been developed that could integrate patient data in a single database. In the 1970s, minicomputers made it possible to link the subsystem databases for clinical subspecialties and ancillary services to the mainframe and integrate patient data into the patient records stored there. In the 1980s, microcomputer-based systems that had evolved independently for specialized services became subsystems of larger medical information systems with an integrating central database management system. Storage grew cheaper; registries became databases; databases became data warehouses; and secondary clinical databases were developed. The recognition that databases were equally as important for querying and retrieving data as for documenting care lead to addressing issues of terminologies and other data standards. The fact that much data was unstructured led to the development of natural language processing (NLP) for retrieving and understanding unstructured data. In the 1990s, patient care data expanded in volume and complexity, and innovative clinical information systems offered hospitals and clinics new capabilities. From the 1990s on, the impact of the Internet and the Web grew, enabling global exchange of clinical data and medical knowledge. In the 2000s, distributed information systems allowed physicians using clinical workstations to enter orders and retrieve test results across multiple medical center databases. In the 2010s, federal support greatly increased the use of computer-based patient records. Global wireless communications with cloud storage for translational networks evolved that linked data warehouses in collaborating medical centers nationally and offered mobile e-health care for individual patients.

Development of Medical Information Systems (MISs)

Emerging progress in clinical applications of patient care computing is identified. The essential clinical skill is understanding what data are appropriate in any given patient care situation and extracting enough information to make the correct management decision. The value of the computer has less to do with the internal intellectual process of diagnosis than its contribution to the more manifest actions in support of clinical patient management. Techniques with which the computer is assisting in improving the clinical decisionmaking process are reviewed, and a mechanism to link them to active patient care settings is described. In addition, a trend toward the integration of various independent subsystems, so that expensive resources can be optimized for patient needs, is noted.

Clinical patient management and the integrated health information system.

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Clinical patient management and the integrated health information system.