Clinical decision support system in medical knowledge literature review

The current study involved methodology and content analyses of abstracts of 30 clinical decision support system (CDSS) related studies with high impact factors. The main aim of the current work was to identify the performance and efficiency of CDSS, and enhance the understanding of CDSS for a better health management among the physicians and the patients. To add structure to the current study, major research areas were categorized based on a multidimensional unfolding analysis. In this regard, eight studies were conducted based on theoretical research, ten studies were related to the system and performance of CDSS, and 12 studies verified the efficacy through analysis and evaluation of CDSS. The results indicated that the above-mentioned studies on improvement in systematic performance. Then, based on the improvement, effectively used evaluations were conducted comparably. Moreover, 14 studies analyzed patients’ data and assessed decision support system (DSS). The related findings denoted that DSS has been mainly used for patient management and a large number of studies have verified its effectiveness, using several data to ensure its accuracy and reliability. In addition, the analyzed results of the abstracts and the titles were compared to find whether the titles of the literature articles reveal their content. Using these methodological studies, the academic outlook of medical informatics could be forecasted and the academic quality could be improved by resolving the problems, arising out of system development and realization processes. Such problems can be solved through analyses and interpretation of multilateral parameters, such as the trend in academic development, research direction, topics and methods.

[1]  C. Forrest,et al.  Improving Adherence to Otitis Media Guidelines With Clinical Decision Support and Physician Feedback , 2013, Pediatrics.

[2]  Guy A. Dumont,et al.  An Evaluation of an Expert System for Detecting Critical Events During Anesthesia in a Human Patient Simulator: A Prospective Randomized Controlled Study , 2013, Anesthesia and analgesia.

[3]  J. Birkmeyer,et al.  Strategies to reduce variation in the use of surgery , 2013, The Lancet.

[4]  Junggi Yang,et al.  Cardiovascular disease prediction models on Linear Discriminant Analysis of depression , 2014, CIT 2014.

[5]  Adam P. Gibson,et al.  Recommendations for research design and reporting in computer-assisted diagnosis to facilitate meta-analysis , 2012, J. Biomed. Informatics.

[6]  Gerard C Kelly,et al.  Further shrinking the malaria map: how can geospatial science help to achieve malaria elimination? , 2013, The Lancet. Infectious diseases.

[7]  S. Steele,et al.  Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model , 2012, Annals of Surgical Oncology.

[8]  Anne Kao,et al.  Natural Language Processing and Text Mining , 2006 .

[9]  Junggi Yang,et al.  Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent , 2014, Healthcare informatics research.

[10]  Todd H. Stokes,et al.  Pathology imaging informatics for quantitative analysis of whole-slide images , 2013, Journal of the American Medical Informatics Association : JAMIA.

[11]  Arch G. Mainous,et al.  Impact of a clinical decision support system on antibiotic prescribing for acute respiratory infections in primary care: quasi-experimental trial , 2013, J. Am. Medical Informatics Assoc..

[12]  Sooyoung Yoo,et al.  Semantic concept-enriched dependence model for medical information retrieval , 2014, J. Biomed. Informatics.

[13]  Jae-Kwon Kim,et al.  Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS–LDA) , 2013, Personal and Ubiquitous Computing.

[14]  I-Chiu Chang,et al.  Developing a disability determination model using a decision support system in Taiwan: a pilot study. , 2013, Journal of the Formosan Medical Association = Taiwan yi zhi.

[15]  Anant Madabhushi,et al.  Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS , 2013, Medical Image Anal..

[16]  S. Colan,et al.  Risk stratification at diagnosis for children with hypertrophic cardiomyopathy: an analysis of data from the Pediatric Cardiomyopathy Registry , 2013, The Lancet.

[17]  Ronald N. Kostoff,et al.  Text mining using database tomography and bibliometrics: A review , 2001 .

[18]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[19]  P. Groenen,et al.  Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation , 2005 .

[20]  Ronald N. Kostoff,et al.  Database tomography for technical intelligence , 1993 .

[21]  Brian W. Pickering,et al.  Connecting the dots: rule-based decision support systems in the modern EMR era , 2013, Journal of Clinical Monitoring and Computing.

[22]  Ah-Hwee Tan,et al.  Text Mining: The state of the art and the challenges , 2000 .

[23]  Marc B Rosenman,et al.  A regional informatics platform for coordinated antibiotic-resistant infection tracking, alerting, and prevention. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[24]  C. Whibley,et al.  Risk factors for congenital anomaly in a multiethnic birth cohort: an analysis of the Born in Bradford study , 2013, The Lancet.

[25]  Naomi S. Bardach,et al.  Effect of pay-for-performance incentives on quality of care in small practices with electronic health records: a randomized trial. , 2013, JAMA.

[26]  Jian-Bo Yang,et al.  Clinical Decision Support Systems: A Review on Knowledge Representation and Inference Under Uncertainties , 2008, Int. J. Comput. Intell. Syst..

[27]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[28]  Monica Chiarini Tremblay,et al.  Identifying fall-related injuries: Text mining the electronic medical record , 2009, Inf. Technol. Manag..

[29]  Hsinchun Chen,et al.  Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Operations Research/Computer Science Interfaces) , 2005 .

[30]  Young-Ho Lee,et al.  Development of Measurement Model for the Value of QOL as an Influential Factor of Metabolic Syndrome , 2014, Wirel. Pers. Commun..

[31]  R. Gerkin,et al.  Improving clinical interpretation of the anti-platelet factor 4/heparin enzyme-linked immunosorbent assay for the diagnosis of heparin-induced thrombocytopenia through the use of receiver operating characteristic analysis, stratum-specific likelihood ratios, and Bayes theorem. , 2013, Chest.

[32]  Peter McCulloch,et al.  Understanding regional variation in the use of surgery , 2014 .

[33]  Young-Ho Lee,et al.  A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis , 2013, 2013 International Conference on Information Science and Applications (ICISA).

[34]  J. Beilby,et al.  A discussion of cases in the 2001 RCPA‐AQAP Chemical Pathology Case Report Comments Program , 2003, Pathology.

[35]  Ling Li,et al.  An emergency response decision support system framework for application in e-government , 2012, Information Technology and Management.

[36]  Alea M. Fairchild Decision management: Role and effect of using an intelligent intermediary to aid in information sharing , 2006, Inf. Technol. Manag..

[37]  Mor Peleg,et al.  Computer-interpretable clinical guidelines: A methodological review , 2013, J. Biomed. Informatics.

[38]  Stephen B. Johnson,et al.  Conceptual knowledge acquisition in biomedicine: A methodological review , 2007, J. Biomed. Informatics.

[39]  John D. McGreevey,et al.  Order sets in electronic health records: principles of good practice. , 2013, Chest.

[40]  R. Grol,et al.  Patient characteristics as predictors of primary health care preferences: a systematic literature analysis , 2003, Health expectations : an international journal of public participation in health care and health policy.

[41]  Hong Wang,et al.  Knowledge management component in managing human resources for enterprises , 2012, Inf. Technol. Manag..

[42]  M. Dixon-Woods,et al.  Improving quality and safety of care using "technovigilance": an ethnographic case study of secondary use of data from an electronic prescribing and decision support system. , 2013, The Milbank quarterly.

[43]  S. Ornstein,et al.  Use of an Electronic Health Record Clinical Decision Support Tool to Improve Antibiotic Prescribing for Acute Respiratory Infections: The ABX-TRIP Study , 2013, Journal of General Internal Medicine.

[44]  Kathy L. MacLaughlin,et al.  Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening , 2013, Journal of the American Medical Informatics Association : JAMIA.

[45]  Jessica L. Milstead,et al.  ASIS&T thesaurus of information science, technology, and librarianship , 2005 .

[46]  Ronald N. Kostoff,et al.  Database Tomography for Technical Intelligence: A Roadmap of the Near-Earth Space Science and Technology Literature , 1998, Inf. Process. Manag..

[47]  K. Hood,et al.  Oral ibandronic acid versus intravenous zoledronic acid in treatment of bone metastases from breast cancer: a randomised, open label, non-inferiority phase 3 trial. , 2014, The Lancet. Oncology.

[48]  L. Svensson,et al.  Developing a decision support system for geriatric patients in prehospital care , 2013, European journal of emergency medicine : official journal of the European Society for Emergency Medicine.

[49]  Hien Nguyen,et al.  From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system , 2014, J. Am. Medical Informatics Assoc..

[50]  Adam Wright,et al.  A qualitative study of the activities performed by people involved in clinical decision support: recommended practices for success , 2013, J. Am. Medical Informatics Assoc..

[51]  Fumie Yokota,et al.  Value of Information Literature Analysis: A Review of Applications in Health Risk Management , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[52]  Andrew Worster,et al.  Clinical decision rules to rule out subarachnoid hemorrhage for acute headache. , 2013, JAMA.

[53]  Fatih Basçiftçi,et al.  Using reduced rule base with Expert System for the diagnosis of disease in hypertension , 2013, Medical & Biological Engineering & Computing.

[54]  B. Marcus,et al.  The Seamos Saludables study: A randomized controlled physical activity trial of Latinas. , 2013, American journal of preventive medicine.

[55]  R Brian Haynes,et al.  Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials , 2013, BMJ : British Medical Journal.

[56]  Honglu Liu,et al.  Introduction to the special issue on information technologies in logistics and service science , 2013, Inf. Technol. Manag..

[57]  M. Callon,et al.  Mapping the Dynamics of Science and Technology , 1986 .

[58]  G O Barnett,et al.  Quality assurance through automated monitoring and concurrent feedback using a computer-based medical information system. , 1978, Medical care.

[59]  Suzanne Bakken,et al.  Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: A sociotechnical analysis , 2013, J. Biomed. Informatics.

[60]  Antonio Colombo,et al.  Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II , 2013, The Lancet.

[61]  Y.-H. Lee,et al.  Study on a HDSS-based PEI model for chronic disease management , 2014, ICUIMC.