Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 4/2014

01-04-2014 | Education & Training

A Review of Analytics and Clinical Informatics in Health Care

Authors: Allan F. Simpao, Luis M. Ahumada, Jorge A. Gálvez, Mohamed A. Rehman

Published in: Journal of Medical Systems | Issue 4/2014

Login to get access

Abstract

Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. “Big Data”). Health care systems have leveraged Big Data for quality and performance improvements using analytics—the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.
Literature
1.
Stark, P., Congressional intent for the HITECH Act. Am. J. Manag. Care 16:24–28, 2010.
2.
Jamoom, E., Beatty, P., Bercovitz, A., Woodwell, D., Palso, K., and Rechsteiner, E., Physician adoption of electronic health record systems: United States, 2011. NCHS Data Brief 98:1–8, 2012.
3.
Horner, P., and Basu, A., Analytics and the future of health care. Analytics 1:1–7, 2012.
4.
Glaser, J., HITECH lays the foundation for more ambitious outcomes-based reimbursement. Am. J. Manag. Care 16:19–23, 2010.
5.
Diamond, G. A., and Kaul, S., Evidence-based financial incentives for healthcare reform. Circ: Cardiovasc. Qual. Outcomes. 2:134–140, 2009.
6.
7.
8.
Costa, F. F., Big data in biomedicine. Drug Discov. Today. 2013.
9.
Stead, W. W., Searle, J. R., Fessler, H. E., Smith, J. W., and Shortliffe, E. H., Biomedical informatics: changing what physicians need to know and how they learn. Acad. Med. 86:429–434, 2011.CrossRef
10.
Davenport, T. H., Harris, J., and Shapiro, J., Competing on talent analytics. Harv. Bus. Rev. 88:52–8, 150, 2010.
11.
Kudyba, S., Healthcare informatics: increasing efficiency and productivity. Taylor Francis, New York, 2010.CrossRef
12.
Erhardt, R. A., Schneider, R., and Blaschke, C., Status of text-mining techniques applied to biomedical text. Drug Discov. Today 11:315–325, 2006.CrossRef
13.
Michelson, J. D., Pariseau, J. S., and Paganelli, W. C., Assessing surgical site infection risk factors using electronic medical records and text mining. Am. J. Infect. Control 42:333–336, 2014.CrossRef
14.
Gotz, D., Stavropoulos, H., Sun, J., and Wang, F., ICDA: a platform for intelligent care delivery analytics. AMIA Annu. Symp. Proc. 2012:264–273, 2012.
15.
Miriovsky, B. J., Shulman, L. N., and Abernethy, A. P., Importance of health information technology, electronic health records, and continuously aggregating data to comparative effectiveness research and learning health care. J. Clin. Oncol. 30:4243–4248, 2012.CrossRef
16.
Wharam, J. F., and Weiner, J. P., The promise and peril of healthcare forecasting. Am. J. Manag. Care 18:82–85, 2012.
17.
Rojas, C. C., Patton, R. M., and Beckerman, B. G., Characterizing mammography reports for health analytics. J. Med. Syst. 35:1197–1210, 2011.CrossRef
18.
Blount, M., Ebling, M. R., Eklund, J. M., James, A. G., McGregor, C., Percival, N., Smith, K. P., and Sow, D., Real-time analysis for intensive care: development and deployment of the artemis analytic system. IEEE Eng. Med. Biol. Mag. 29:110–118, 2010.CrossRef
19.
Holdsworth, M. T., Fichtl, R. E., Raisch, D. W., Hewryk, A., Behta, M., Mendez-Rico, E., Wong, C. L., Cohen, J., Bostwick, S., and Greenwald, B. M., Impact of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 120:1058–1066, 2007.CrossRef
20.
van Rosse, F., Maat, B., Rademaker, C. M., van Vught, A. J., Egberts, A. C., and Bollen, C. W., The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: a systematic review. Pediatrics 123:1184–1190, 2009.CrossRef
21.
Chau, A., and Ehrenfeld, J. M., Using real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol. Clin. 29:57–69, 2011.CrossRef
22.
Resetar, E., Reichley, R. M., Noirot, L. A., Dunagan, W. C., and Bailey, T. C., Customizing a commercial rule base for detecting drug-drug interactions. AMIA Annu. Symp. Proc. 1094, 2005.
23.
Guzek, M., Zorina, O. I., Semmler, A., Gonzenbach, R. R., Huber, M., Kullak-Ublick, G. A., Weller, M., and Russmann, S., Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System). Pharmacoepidemiol. Drug Saf. 20:930–938, 2011.
24.
Slonimc, N., Carmeli, B., Goldsteen, A., Keller, O., Kent, C., and Rinott, R., Knowledge-analytics synergy in clinical decision support. Stud. Health Technol. Inform. 180:703–707, 2012.
25.
Chan, M., Estève, D., Fourniols, J. Y., Escriba, C., and Campo, E., Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56:137–156, 2012.CrossRef
26.
Banaee, H., Ahmed, M. U., and Loutfi, A., Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13:17472–17500, 2013.CrossRef
27.
Tseng, K. C., Hsu, C. L., and Chuang, Y. H., Designing an intelligent health monitoring system and exploring user acceptance for the elderly. J. Med. Syst. 37:9967, 2013.CrossRef
28.
Baig, M. M., and Gholamhosseini, H., Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37:9898, 2013.CrossRef
29.
Schouten, P., Big data in health care: solving provider revenue leakage with advanced analytics. Healthc. Finan. Manag. 67:40–42, 2013.
30.
Kudyba, S., and Gregorio, T., Identifying factors that impact patient length of stay metrics for healthcare providers with advanced analytics. Health Inf. J. 16:235–245, 2010.CrossRef
31.
Bradley, P., and Kaplan, J., Turning hospital data into dollars. Healthc. Finan. Manage 64:64–68, 2010.
32.
Costantino, M. E., Frey, B., Hall, B., and Painter, P., The influence of a postdischarge intervention on reducing hospital readmissions in a medicare population. Popul. Health Manag. 16:310–316, 2013.CrossRef
33.
Buell, D., Leveraging data and analytics to generate new revenue. Healthc. Financ. Manage 67:40–2, 44, 2013.
34.
Tufte, E. R., The visual display of quantitative information, 2nd edition. Graphics Press, Cheshire, 2001.
35.
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., and Becker, B. (Eds.), The data warehouse lifecycle toolkit, 2nd edition. Wiley, Hoboken, 2008.
36.
Barton, D., and Court, D., Making advanced analytics work for you. Harv. Bus. Rev. 90:78–83, 128, 2012.
37.
Thomas, J. J., and Cook, K. A., A visual analytics agenda. IEEE Comput. Graph. Appl. 26:10–13, 2006.CrossRef
38.
Kumasaka, N., Nakamura, Y., and Kamatani, N., The textile plot: a new linkage disequilibrium display of multiple-single nucleotide polymorphism genotype data. PLoS One 5:e10207, 2010.CrossRef
39.
Naumova, E. N., Visual analytics for immunologists: data compression and fractal distributions. Self Nonself 1:241–249, 2010.CrossRef
40.
Chui, K. K., Wenger, J. B., Cohen, S. A., and Naumova, E. N., Visual analytics for epidemiologists: understanding the interactions between age, time, and disease with multi-panel graphs. PLoS One 6:e14683, 2011.CrossRef
41.
Wang, T. D., Wongsuphasawat, K., Plaisant, C., and Shneiderman, B., Extracting insights from electronic health records: case studies, a visual analytics process model, and design recommendations. J. Med. Syst. 35:1135–1152, 2011.CrossRef
42.
Mane, K. K., Bizon, C., Owen, P., Gersing, K., Mostafa, J., and Schmitt, C., Patient electronic health data-driven approach to clinical decision support. Clin. Transl. Sci. 4:369–371, 2011.CrossRef
43.
Mane, K. K., Bizon, C., Schmitt, C., Owen, P., Burchett, B., Pietrobon, R., and Gersing, K., VisualDecisionLinc: a visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry. J. Biomed. Inform. 45:101–106, 2012.CrossRef
44.
Gálvez, J. A., Ahumada, L., Simpao, A. F., Lin, E. E., Bonafide, C. P., Choudhry, D., England, W. R., Jawad, A. F., Friedman, D., Sesok-Pizzini, D. A., and Rehman, M. A., Visual analytical tool for evaluation of 10-year perioperative transfusion practice at a children’s hospital. J. Am. Med. Inform. Assoc. 2013.
45.
Kang, Y. A., Görg, C., and Stasko, J., How can visual analytics assist investigative analysis? Design implications from an evaluation. IEEE Trans. Vis. Comput. Graph 17:570–583, 2011.CrossRef
46.
Goldsmith, M. R., Transue, T. R., Chang, D. T., Tornero-Velez, R., Breen, M. S., and Dary, C. C., PAVA: physiological and anatomical visual analytics for mapping of tissue-specific concentration and time-course data. J. Pharmacokinet. Pharmacodyn. 37:277–287, 2010.CrossRef
47.
Perer, A., and Sun, J., MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. AMIA Annu. Symp. Proc. 2012:716–725, 2012.
48.
Lo, Y. S., Lee, W. S., and Liu, C. T., Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections. J. Med. Syst. 37:9923, 2013.CrossRef
49.
Rajwan, Y. G., Barclay, P. W., Lee, T., Sun, I. F., Passaretti, C., and Lehmann, H., Visualizing central line-associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners-an evaluation study. Online J. Public Health Inform. 5:218, 2013.
50.
Fouzas, S., Priftis, K. N., and Anthracopoulos, M. B., Pulse oximetry in pediatric practice. Pediatrics 128:740–752, 2011.CrossRef
51.
Edelstein, P., Emerging directions in analytics. Predictive analytics will play an indispensable role in healthcare transformation and reform. Health Manag. Technol. 34:16–17, 2013.
52.
Detmer, D. E., Munger, B. S., and Lehmann, C. U., Clinical informatics board certification: history, current status, and predicted impact on the clinical informatics workforce. Appl. Clin. Inform. 1:11–18, 2010.CrossRef
53.
Lehmann, C. U., Shorte, V., and Gundlapalli, A. V., Clinical informatics sub-specialty board certification. Pediatr. Rev. 34:525–530, 2013.CrossRef
54.
Galvez, J. A., Simpao, A. F., Utidjian, L. H., and Rehman, M. A., Clinical informatics: bridging the gap between physician and information technology. AAP News 11:14, 2013.
55.
Kerr, W. T., Lau, E. P., Owens, G. E., and Trefler, A., The future of medical diagnostics: large digitized databases. Yale J. Biol. Med. 85:363–377, 2012.
56.
Chawla, N. V., and Davis, D. A., Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28(Suppl 3):S660–S665, 2013.CrossRef
Metadata
Title
A Review of Analytics and Clinical Informatics in Health Care
Authors
Allan F. Simpao
Luis M. Ahumada
Jorge A. Gálvez
Mohamed A. Rehman
Publication date
01-04-2014
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2014
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-014-0045-x

Other articles of this Issue 4/2014

Journal of Medical Systems 4/2014 Go to the issue

Research Article

Detection of Breast Abnormality from Thermograms Using Curvelet Transform Based Feature Extraction

Systems-Level Quality Improvement

Developing a Real Time Electrocardiogram System Using Virtual Bio-Instrumentation

Patient Facing Systems

Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders

Systems-Level Quality Improvement

MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network

Systems-Level Quality Improvement

Automated Bleeding Detection in Capsule Endoscopy Videos Using Statistical Features and Region Growing

Patient Facing Systems

A Clinical Decision Support System with an Integrated EMR for Diagnosis of Peripheral Neuropathy