medical informatics applications


Summary: Automated systems applied to the patient care process including diagnosis, therapy, and systems of communicating medical data within the health care setting.

Top Publications

  1. Mosa A, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak. 2012;12:67 pubmed publisher
    ..Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients. ..
  2. Pilemalm S, Timpka T. Third generation participatory design in health informatics--making user participation applicable to large-scale information system projects. J Biomed Inform. 2008;41:327-39 pubmed
    ..Future research should involve evaluations of the framework in other health service settings where comprehensive HISs are developed. ..
  3. Demiris G, Afrin L, Speedie S, Courtney K, Sondhi M, Vimarlund V, et al. Patient-centered applications: use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group. J Am Med Inform Assoc. 2008;15:8-13 pubmed
    ..It reviews current and emerging trends; highlights challenges related to design, evaluation, reimbursement and usability; and reaches conclusions for next steps that will advance the domain. ..
  4. Serdar M, Turan M, Cihan M. Rapid access to information resources in clinical biochemistry: medical applications of Personal Digital Assistants (PDA). Clin Exp Med. 2008;8:117-22 pubmed publisher
  5. Kushniruk A, Patel V. Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform. 2004;37:56-76 pubmed
    ..Emerging trends in the evaluation of complex information systems are discussed. ..
  6. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144:742-52 pubmed
    ..Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear. ..
  7. Patel A, Gilbertson J, Parwani A, Dhir R, Datta M, Gupta R, et al. An informatics model for tissue banks--lessons learned from the Cooperative Prostate Cancer Tissue Resource. BMC Cancer. 2006;6:120 pubmed
  8. Sanders D, Aronsky D. Biomedical informatics applications for asthma care: a systematic review. J Am Med Inform Assoc. 2006;13:418-27 pubmed
    ..Few studies demonstrated evidence of computerized applications improving clinical outcomes. Further research is needed to prospectively evaluate the impact of using biomedical informatics to improve care of asthmatic patients. ..
  9. Dinu V, Nadkarni P, Brandt C. Pivoting approaches for bulk extraction of Entity-Attribute-Value data. Comput Methods Programs Biomed. 2006;82:38-43 pubmed
    ..An alternative but more complex approach that utilizes hash tables and the presence of abundant random-access-memory can achieve improved performance by reducing the load on the database server. ..

More Information


  1. Andre D, Teller A. Health. Care. Anywhere. Today. Stud Health Technol Inform. 2005;118:89-110 pubmed
    ..This discussion will show how the convergence of design for wearability, advances in machine learning, and improvements in wireless technology will manifest the future of health care as personal, ubiquitous, and collaborative. ..
  2. Los R, van Ginneken A, van der Lei J. Extracting data recorded with OpenSDE: possibilities and limitations. Int J Med Inform. 2005;74:473-80 pubmed
    ..The extraction tool (entity export) provides a successful technical solution for data extraction. Using the extracted data, however, leads to obstacles that are a result of a fundamental design principle of OpenSDE. ..
  3. Los R, van Ginneken A, van der Lei J. OpenSDE: a strategy for expressive and flexible structured data entry. Int J Med Inform. 2005;74:481-90 pubmed
    ..The OpenSDE application is currently in use at several departments in our academic hospital, including radiology, neurology, pediatrics, and child psychiatry. OpenSDE is available for all in open source. ..
  4. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7:e11 pubmed
    ..A "run-in and withdrawal" trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users. ..
  5. Grossman P. The LifeShirt: a multi-function ambulatory system monitoring health, disease, and medical intervention in the real world. Stud Health Technol Inform. 2004;108:133-41 pubmed
    ..Clinical applications include sleep diagnostics, heart disease, pulmonary disorders, cardiopulmonary rehabilitation, early hospital discharge and pre- and post-operative monitoring, human-factors in ergonomics and behavioral medicine. ..
  6. Gilbertson J, Gupta R, Nie Y, Patel A, Becich M. Automated clinical annotation of tissue bank specimens. Stud Health Technol Inform. 2004;107:607-10 pubmed
    ..The result has been much more extensive and accurate initial tissue annotation with less effort in the tissue bank, as well as dynamic ongoing annotation as the cancer registry follows patients over time. ..
  7. Porter S, Cai Z, Gribbons W, Goldmann D, Kohane I. The asthma kiosk: a patient-centered technology for collaborative decision support in the emergency department. J Am Med Inform Assoc. 2004;11:458-67 pubmed
    ..The asthma kiosk successfully links parents' data to guideline recommendations and identifies data critical to health improvements for asthmatic children that otherwise remains undocumented during ED-based care. ..
  8. Schleyer T, Spallek H. Dental informatics. A cornerstone of dental practice. J Am Dent Assoc. 2001;132:605-13 pubmed
    ..Dental informatics will produce an increasing number of applications and tools for clinical practice. Dentists must keep up with these developments to make informed choices. ..
  9. Cluzeau T, Mounier N. [Patients and the Web]. Bull Cancer. 2010;97:1133-6 pubmed publisher
    ..The information from the Internet is mostly good but not updated and erroneous data are regularly found. This confirms that the consultation by a specialist doctor referral should remain the main source of information for the patient. ..
  10. Wozak F, Ammenwerth E, Hörbst A, Sögner P, Mair R, Schabetsberger T. IHE based interoperability - benefits and challenges. Stud Health Technol Inform. 2008;136:771-6 pubmed
    ..Irrespective of the so far practical significance of the IHE profiles it appears to be of great importance, that the profiles are constantly checked against practical experiences and are continuously adapted. ..
  11. Scott P, Rigby M, Ammenwerth E, McNair J, Georgiou A, Hyppönen H, et al. Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis. Yearb Med Inform. 2017;26:59-67 pubmed publisher
    ..A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics. ..
  12. Rebollo P, Castejón I, Cuervo J, Villa G, García Cueto E, Díaz Cuervo H, et al. Validation of a computer-adaptive test to evaluate generic health-related quality of life. Health Qual Life Outcomes. 2010;8:147 pubmed publisher
  13. Neches R, Ryutov T, Kichkaylo T, Burke R, Claudius I, Upperman J. Design and evaluation of a disaster preparedness logistics tool. Am J Disaster Med. 2009;4:309-20 pubmed
    ..The authors believe the PEDSS tool will help hospital disaster response personnel produce and maintain disaster response plans that apply best practice pediatric recommendations to their particular local conditions and requirements. ..
  14. Bott O, Dresing K, Wagner M, Raab B, Teistler M. Informatics in radiology: use of a C-arm fluoroscopy simulator to support training in intraoperative radiography. Radiographics. 2011;31:E65-75 pubmed publisher
    ..Supplemental material available at ..
  15. Bernstein I, Lindorff Larsen K, Timshel S, Brandt C, Dinesen B, Fenger M, et al. Biomedical informatics as support to individual healthcare in hereditary colon cancer: the Danish HNPCC system. Hum Mutat. 2011;32:551-6 pubmed publisher
    ..Several gaps were identified: lack of standards for data to be exchanged, lack of local databases suitable for direct communication, reporting being time-consuming and dependent on interest and feedback. ..
  16. Diamantidis C, Zuckerman M, Fink W, Aggarwal S, Prakash D, Fink J. Usability testing and acceptance of an electronic medication inquiry system for CKD patients. Am J Kidney Dis. 2013;61:644-6 pubmed publisher
  17. Byrne C, Pan E, Russell C, Finley S, Rippen H. Applying an organizational framework for health information technology to alerts. AMIA Annu Symp Proc. 2012;2012:67-76 pubmed
  18. Barrington R. Navigating an ocean of information: how Community Care of North Carolina uses data to improve care and control costs. N C Med J. 2014;75:183-7 pubmed
    ..This article describes some of the ways that these networks use data to improve patient self-management, to meet providers' needs, to improve quality of care, and to control costs. ..
  19. Neururer S, Lasierra N, Peiffer K, Fensel D. Formalizing the Austrian Procedure Catalogue: A 4-step methodological analysis approach. J Biomed Inform. 2016;60:1-13 pubmed publisher
  20. Olive M, Rahmouni H, Solomonides T, Breton V, Legre Y, Blanquer I, et al. SHARE, from vision to road map: technical steps. Stud Health Technol Inform. 2007;129:1149-53 pubmed
    ..The project explores the ways in which the healthgrid approach supports modern trends both in research in biomedicine and in healthcare, such as evidence-based practice and information integration. ..
  21. Estellat C, Tubach F, Costa Y, Hoffmann I, Mantz J, Ravaud P. Data capture by digital pen in clinical trials: a qualitative and quantitative study. Contemp Clin Trials. 2008;29:314-23 pubmed
    ..The DP system has a good acceptability among all investigators in a clinical setting, whether they are experienced with computers or not, and a good accuracy, as compared with double manual data entry. ..
  22. Branson A, Hauer T, McClatchey R, Rogulin D, Shamdasani J. A data model for integrating heterogeneous medical data in the Health-e-Child project. Stud Health Technol Inform. 2008;138:13-23 pubmed
    ..Pointers are given to future work relating the model to medical ontologies and challenges to the use of fully integrated models and ontologies are identified. ..
  23. Franko O. Smartphone apps for orthopaedic surgeons. Clin Orthop Relat Res. 2011;469:2042-8 pubmed publisher
    ..However, few highly ranked apps specifically related to orthopaedic surgery are available, and the types of apps available do not appear to be the categories most desired by residents and surgeons. ..
  24. Laleci G, Yuksel M, Dogac A. Providing semantic interoperability between clinical care and clinical research domains. IEEE J Biomed Health Inform. 2013;17:356-69 pubmed publisher
    ..The results indicate that it is possible to build a robust and scalable semantic framework with a solid theoretical foundation for achieving interoperability between the clinical research and clinical care domains. ..
  25. Goff D. iPhones, iPads, and medical applications for antimicrobial stewardship. Pharmacotherapy. 2012;32:657-61 pubmed publisher
    ..This article reviews medical apps for antimicrobial stewardship programs to use on the iPhone or iPad. ..
  26. Deus H, Prud hommeaux E, Miller M, Zhao J, Malone J, Adamusiak T, et al. Translating standards into practice - one Semantic Web API for Gene Expression. J Biomed Inform. 2012;45:782-94 pubmed publisher
  27. Schattner P, Saunders M, Stanger L, Speak M, Russo K. Data extraction and feedback - does this lead to change in patient care?. Aust Fam Physician. 2011;40:623-8 pubmed
  28. Davis N, Myers L, Myers Z. Physician ePortfolio: the missing piece for linking performance with improvement. Am J Manag Care. 2010;16:SP57-61 pubmed
  29. Borbolla D, Giunta D, Figar S, Soriano M, Dawidowski A, de Quiros F. Effectiveness of a chronic disease surveillance systems for blood pressure monitoring. Stud Health Technol Inform. 2007;129:223-7 pubmed
    ..50% (239) respectively (p=0.002) for condition 2. Patients under the surveillance system have higher proportion of blood pressure measurements, showing this study an improvement on the process of care with this IT tool. ..
  30. Romá Ferri M, Palomar M. [Analysis of health terminologies for use as ontologies in healthcare information systems]. Gac Sanit. 2008;22:421-33 pubmed
  31. Hägglund M, Scott Duncan T, Kai Larsen K, Hedlin G, Krakau I. IntegrIT - Towards Utilizing the Swedish National Health Information Exchange Platform for Clinical Research. Stud Health Technol Inform. 2017;235:146-150 pubmed
  32. Lester W, Ashburner J, Grant R, Chueh H, Barry M, Atlas S. Mammography FastTrack: an intervention to facilitate reminders for breast cancer screening across a heterogeneous multi-clinic primary care network. J Am Med Inform Assoc. 2009;16:187-95 pubmed publisher
    ..Overall, 63% of patients were successfully contacted. Systematic population-based efforts are promising tools to improve preventative care. ..
  33. Saeed M, Villarroel M, Reisner A, Clifford G, Lehman L, Moody G, et al. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Crit Care Med. 2011;39:952-60 pubmed publisher
    ..It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. ..
  34. de Lusignan S, Chan T. The development of primary care information technology in the United kingdom. J Ambul Care Manage. 2008;31:201-10 pubmed publisher
    ..All 4 elements of this model have been tilted in favor of the utilization of information technology; lessons from the United Kingdom may help other health systems looking to implement information technology systems in primary care. ..
  35. Rossille D, Burgun A, Pangault Lorho C, Fest T. Integrating clinical, gene expression, protein expression and preanalytical data for in silico cancer research. Stud Health Technol Inform. 2008;136:455-60 pubmed
    ..A secure web-based platform allows any collaborative team to request the data warehouse and access basic statistics on integrated data. The presented system is currently in use. ..
  36. Eminaga O, Semjonow A, Eltze E, Bettendorf O, Schultheis A, Warnecke Eberz U, et al. Analysis of topographical distribution of prostate cancer and related pathological findings in prostatectomy specimens using cMDX document architecture. J Biomed Inform. 2016;59:240-7 pubmed publisher
    ..0001). The cMDX-based technical approach facilitates analysis of the topographical distribution of PCa foci and related pathologic findings in prostatectomy specimens. ..
  37. Taneja U, Sushil -. e-Healthcare in India: critical success factors for sustainable health systems. Stud Health Technol Inform. 2007;129:257-61 pubmed
    ..Instead the nontechnology factors such as healthcare provider and consumer mindsets should be addressed to increase acceptance of, and enhance the effectiveness of, sustainable e-Healthcare services. ..
  38. Mertz L. Ultrasound? Fetal monitoring? Spectrometer? There's an app for that!: biomedical smart phone apps are taking healthcare by storm. IEEE Pulse. 2012;3:16-21 pubmed publisher
  39. Flanagan P, Relyea Chew A, Gross J, Gunn M. Using the Internet for image transfer in a regional trauma network: effect on CT repeat rate, cost, and radiation exposure. J Am Coll Radiol. 2012;9:648-56 pubmed publisher
  40. Ammann A, Kiss T, Hirsch M, Matthies H. Where, what, why: Mr. Q on the Web. Int J Comput Dent. 2008;11:183-200 pubmed
    ..Semantic search engines are one current approach to this problem. For this reason, a project entitled "Mr. Q, your personal Web Assistant" has been initiated and will be introduced in this paper. ..
  41. Caban J, Joshi A, Nagy P. Rapid development of medical imaging tools with open-source libraries. J Digit Imaging. 2007;20 Suppl 1:83-93 pubmed
  42. Bashir S, Qamar U, Khan F. IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed Inform. 2016;59:185-200 pubmed publisher
    ..An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice. ..
  43. Workman T, Fiszman M, Cairelli M, Nahl D, Rindflesch T. Spark, an application based on Serendipitous Knowledge Discovery. J Biomed Inform. 2016;60:23-37 pubmed publisher
    ..A detailed overview of the Spark system illustrates how methodologies in design and retrieval functionality enable production of semantic predication graphs tailored to evoke Serendipitous Knowledge Discovery in users. ..
  44. Cresswell K, Bates D, Sheikh A. Ten key considerations for the successful implementation and adoption of large-scale health information technology. J Am Med Inform Assoc. 2013;20:e9-e13 pubmed publisher
  45. Martins S, Shahar Y, Goren Bar D, Galperin M, Kaizer H, Basso L, et al. Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. Artif Intell Med. 2008;43:17-34 pubmed publisher
  46. Alexandrou D, Pardalis K, Bouras T, Karakitsos P, Mentzas G. SEMPATH Ontology: modeling multidisciplinary treatment schemes utilizing semantics. IEEE Trans Inf Technol Biomed. 2012;16:235-40 pubmed publisher
    ..Finally, SEMPATH Ontology is utilized for the definition of a set of SWRL rules for the human papillomavirus) disease and its treatment scheme. ..
  47. Haran M, Schattner A. On the clinical encounter with 'zebras' - the science and art of rare diseases. Eur J Intern Med. 2011;22:235-7 pubmed publisher
    ..In particular, using computerized searches when facing patients with unexplained symptoms, and adopting an honest, supportive attitude when uncertainty persists, seems important. ..
  48. Buettner L, Yu F, Burgener S. Evidence supporting technology-based interventions for people with early-stage Alzheimer's disease. J Gerontol Nurs. 2010;36:15-9 pubmed publisher
    ..We believe the use of technology has the potential to save health care costs, ease caregiver stress, and help people with dementia live better, safer, and more fulfilling lives. ..
  49. Lorence D, Chin J, Richards M. Meeting the ONCHIT population health mandate: a proposed model for security in selective transportable distributed environments. J Med Syst. 2010;34:563-72 pubmed publisher
    ..Proposed here is a model for health information management in such population-based environments, which allows selective access and use of information, and maintains transportability while ensuring security and confidentiality. ..
  50. Martin Sanchez F, Hermosilla Gimeno I. Translational bioinformatics. Stud Health Technol Inform. 2010;151:312-37 pubmed
    ..The main lines for research and development in translational medicine, its main applications in the field of genomics medicine and future challenges raised by the new trends in medicine. ..
  51. McKinlay A, McVittie C, Reiter E, Freer Y, Sykes C, Logie R. Design issues for socially intelligent user interfaces. A discourse analysis of a data-to-text system for summarizing clinical data. Methods Inf Med. 2010;49:379-87 pubmed publisher
    ..These results indicate potential future improvements to the system. Discourse analysis as used here may offer significant advantages in evaluating and developing similar medical informatics systems. ..
  52. Montagnolo A. The hospital of tomorrow. Trustee. 2013;66:25-6, 1 pubmed
    ..Advances in technology will address health care's biggest challenges: engagement, service and value. ..
  53. Honka A, Kaipainen K, Hietala H, Saranummi N. Rethinking health: ICT-enabled services to empower people to manage their health. IEEE Rev Biomed Eng. 2011;4:119-39 pubmed publisher
    ..It looks into the theoretical foundations of behavior change support, the maturity of the technologies for behavior change support, and the business context in which behavior change support systems are used. ..