computer assisted decision making

Summary

Summary: Use of an interactive computer system designed to assist the physician or other health professional in choosing between certain relationships or variables for the purpose of making a diagnostic or therapeutic decision.

Top Publications

  1. Anhøj J, Nielsen L. Quantitative and qualitative usage data of an Internet-based asthma monitoring tool. J Med Internet Res. 2004;6:e23 pubmed
    ..The primary reason for this was that LinkMedica did not fit into their everyday lives because of technical and psychological aspects. A number of recommendations to improve LinkMedica are suggested. ..
  2. Antoniou A, Pharoah P, Smith P, Easton D. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91:1580-90 pubmed
    ..We conclude that this model provides a rational basis for risk assessment in individuals with a FH of breast or ovarian cancer. ..
  3. Kawamoto K, Lobach D. Proposal for fulfilling strategic objectives of the U.S. Roadmap for national action on clinical decision support through a service-oriented architecture leveraging HL7 services. J Am Med Inform Assoc. 2007;14:146-55 pubmed
    ..Our experiences, and those of others, indicate that the proposed SOA approach to CDS could enable the widespread adoption of effective CDS within the U.S. health care system. ..
  4. Zhu M, Chen W, Hirdes J, Stolee P. The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol. J Clin Epidemiol. 2007;60:1015-21 pubmed
    ..Compared using likelihood ratio statistics, KNN is uniformly more informative than the ADLCAP. This article illustrates the potential for a machine-learning algorithm to enhance clinical decision making. ..
  5. Schurink C, Lucas P, Hoepelman I, Bonten M. Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units. Lancet Infect Dis. 2005;5:305-12 pubmed
  6. Rood E, Bosman R, Van der Spoel J, Taylor P, Zandstra D. Use of a computerized guideline for glucose regulation in the intensive care unit improved both guideline adherence and glucose regulation. J Am Med Inform Assoc. 2005;12:172-80 pubmed
    ..3%). Implementing a computerized version of a guideline significantly improved timeliness of measurements and glucose level regulation for critically ill patients compared with implementing a paper-based version of the guideline. ..
  7. Coiera E, Walther M, Nguyen K, Lovell N. Architecture for knowledge-based and federated search of online clinical evidence. J Med Internet Res. 2005;7:e52 pubmed
    ..Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead. ..
  8. Roitberg B. Fuzzy logic in the neurosurgical intensive care unit. Surg Neurol. 2006;65:217 pubmed
  9. Osheroff J, Teich J, Middleton B, Steen E, Wright A, Detmer D. A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007;14:141-5 pubmed
    ..It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder. ..

More Information

Publications62

  1. Emery J. The GRAIDS Trial: the development and evaluation of computer decision support for cancer genetic risk assessment in primary care. Ann Hum Biol. 2005;32:218-27 pubmed
    ..This research evaluates an approach to support high-quality advice about cancer genetics in primary care which could be applied more broadly as our understanding of complex disease genetics increases. ..
  2. Magrabi F, Coiera E, Westbrook J, Gosling A, Vickland V. General practitioners' use of online evidence during consultations. Int J Med Inform. 2005;74:1-12 pubmed
    ..General practitioners will use an online evidence retrieval system in routine practice, and report that its use improves the quality of patient care. ..
  3. Berry D, Iversen E, Gudbjartsson D, Hiller E, Garber J, Peshkin B, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. J Clin Oncol. 2002;20:2701-12 pubmed
    ..In the populations studied, breast cancer susceptibility genes other than BRCA1 and BRCA2 either do not exist, are rare, or are associated with low disease penetrance. ..
  4. Wang D, Peleg M, Tu S, Boxwala A, Greenes R, Patel V, et al. Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models. Int J Med Inform. 2002;68:59-70 pubmed
    ..Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another. ..
  5. Fitzmaurice J, Adams K, Eisenberg J. Three decades of research on computer applications in health care: medical informatics support at the Agency for Healthcare Research and Quality. J Am Med Inform Assoc. 2002;9:144-60 pubmed
    ..The results and relative roles of these related efforts are beyond the scope of this review. ..
  6. Dong P, Mondry A. Enhanced quality and quantity of retrieval of Critically Appraised Topics using the CAT Crawler. Med Inform Internet Med. 2004;29:43-55 pubmed
    ..In summary, the application provides physicians with a common interface to retrieve relevant CATs on particular clinical topics from multiple resources, and thus speeds up the decision making process. ..
  7. Dart T, Xu Y, Chatellier G, Degoulet P. Computerization of guidelines: towards a "guideline markup language". Stud Health Technol Inform. 2001;84:186-90 pubmed
    ..We conclude that XML can be used as a description format to structure guidelines and as an interface between paper-based guidelines and computer applications. ..
  8. Shiffman R, Liaw Y, Brandt C, Corb G. Computer-based guideline implementation systems: a systematic review of functionality and effectiveness. J Am Med Inform Assoc. 1999;6:104-14 pubmed
    ..There were 10 controlled trials (9 randomized) and 10 time-series correlational studies. Guideline adherence improved in 14 of 18 systems in which it was measured. Documentation improved in 4 of 4 studies. ..
  9. Eccles M, McColl E, Steen N, Rousseau N, Grimshaw J, Parkin D, et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. BMJ. 2002;325:941 pubmed
  10. Flower J. Transformations of 21st century health care, Part 1. Beyond the digital divide. Health Forum J. 2003;46:8-13, 1 pubmed
    ..Speed, control, accountability, cost containment, and customer satisfaction are the first-level benefits of digitization. We'll see these benefits grow, and we'll see much more in the new century. ..
  11. Manotti C, Moia M, Palareti G, Pengo V, Ria L, Dettori A. Effect of computer-aided management on the quality of treatment in anticoagulated patients: a prospective, randomized, multicenter trial of APROAT (Automated PRogram for Oral Anticoagulant Treatment). Haematologica. 2001;86:1060-70 pubmed
  12. Mikulich V, Liu Y, Steinfeldt J, Schriger D. Implementation of clinical guidelines through an electronic medical record: physician usage, satisfaction and assessment. Int J Med Inform. 2001;63:169-78 pubmed
    ..These data illuminate both the potentials of computer-assisted decision making and the need for context-specific approaches when attempting to implement guidelines. ..
  13. Taylor P, Fox J, Pokropek A. The development and evaluation of CADMIUM: a prototype system to assist in the interpretation of mammograms. Med Image Anal. 1999;3:321-37 pubmed
    ..CADMIUM has been evaluated as an aid to the differential diagnosis of microcalcifications on mammographic images. Radiographers who had been trained to interpret images performed better when using the advice provided by the system. ..
  14. Ohno Machado L, Gennari J, Murphy S, Jain N, Tu S, Oliver D, et al. The guideline interchange format: a model for representing guidelines. J Am Med Inform Assoc. 1998;5:357-72 pubmed
    ..GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty. ..
  15. Clarke J, Hayward C, Santora T, Wagner D, Webber B. Computer-generated trauma management plans: comparison with actual care. World J Surg. 2002;26:536-8 pubmed
  16. Rousseau N, McColl E, Newton J, Grimshaw J, Eccles M. Practice based, longitudinal, qualitative interview study of computerised evidence based guidelines in primary care. BMJ. 2003;326:314 pubmed
    ..Key issues include the relevance and accuracy of messages and the flexibility to respond to other factors influencing decision making in primary care. ..
  17. Bates J, Young M. Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med. 2003;167:948-52 pubmed
  18. Loprinzi C, Ravdin P. Decision-making for patients with resectable breast cancer: individualized decisions for and by patients and their physicians. J Natl Compr Canc Netw. 2003;1:189-96 pubmed
    ..Two computer-based tools (Numeracy and Adjuvant!) are available to facilitate this process. ..
  19. German E, Leibowitz A, Shahar Y. An architecture for linking medical decision-support applications to clinical databases and its evaluation. J Biomed Inform. 2009;42:203-18 pubmed publisher
    ..Runtime access of 10,000 records required one second. We conclude that mapping MDSSs to different local clinical DBs, using the three-phase methodology and several term-mapping heuristics, is both feasible and efficient. ..
  20. Christensen T, Grimsmo A. Expectations for the next generation of electronic patient records in primary care: a triangulated study. Inform Prim Care. 2008;16:21-8 pubmed
    ..Results from this study could contribute to further development of the next generation of EPRs in primary care, as well as inspire the application of EPRs in other parts of the health sector. ..
  21. Mahnken J, Mayo M, Nudo R. A decision algorithm for translating preclinical trial results to enhance recovery after stroke. J Biopharm Stat. 2009;19:204-16 pubmed publisher
    ..84). Similar algorithms may be adapted for other milestone-driven projects. ..
  22. Im E, Chee W, Tsai H, Bender M, Lim H. Internet communities for recruitment of cancer patients into an Internet survey: a discussion paper. Int J Nurs Stud. 2007;44:1261-9 pubmed
    ..The findings suggest that researchers thoroughly review the ICs' information, be recognizant of potential gender and ethnic issues and current trends in Internet interaction, and consider potential selection bias. ..
  23. Wright A, Maydom B. Improving the implementation of community-acquired pneumonia guidelines. Intern Med J. 2004;34:507-9 pubmed
    ..In the present report, the use of a -computer-based assistant to decision-making was -successfully developed and tested, improving the application of well-known guidelines. ..
  24. Doughty C, MacCormick A, Roake J, Fraser J, Hider P, Kirk R, et al. Prioritisation of elective surgery in New Zealand: The Reliability Study. N Z Med J. 2005;118:U1590 pubmed
    ..Further work from this study will focus on the individual results for each specialty and examining whether altering ethnicity status in vignettes had any effect on scoring behaviour. ..
  25. King J, Gonzalez J, Fuller M. Development of a vibrotactile tasking device for use in vestibular assessment. J Vestib Res. 2006;16:57-67 pubmed
    ..Overall, the results showed that the vibrotactile tasking device (VTD) is an effective alternative means of providing mental alerting during vestibular testing, specifically that of caloric examination. ..
  26. Arya S, Agarwal N, Agarwal S. Re: Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. Int J Qual Health Care. 2006;18:389 pubmed
  27. Grando A, Peleg M, Glasspool D. A goal-oriented framework for specifying clinical guidelines and handling medical errors. J Biomed Inform. 2010;43:287-99 pubmed publisher
    ..We demonstrate our approach using a generic plan for management of a chronic disease and a particular instantiation for hypertension management. ..
  28. Druzovec M, Welzer T, Brumen B. Agent oriented approach to handling medical data. J Med Syst. 2005;29:45-57 pubmed
    ..Such a decentralized approach mirrors the organizational structure of a health service and it is very similar to an agent-oriented view of the world. ..
  29. Ram P, Berg D, Tu S, Mansfield G, Ye Q, Abarbanel R, et al. Executing clinical practice guidelines using the SAGE execution engine. Stud Health Technol Inform. 2004;107:251-5 pubmed
    ..In this paper, we describe our test implementation and highlight the significance and implications of each component of our deployment architecture ..
  30. Liu J, Li M. Finding cancer biomarkers from mass spectrometry data by decision lists. J Comput Biol. 2005;12:971-9 pubmed
    ..Such a feature will provide clues for medical experts to thoroughly investigate the roles of protein in cancer development and progression. ..
  31. Bastholm Rahmner P, Andersen Karlsson E, Arnhjort T, Eliasson M, Gustafsson L, Jacobsson L, et al. Physicians' perceptions of possibilities and obstacles prior to implementing a computerised drug prescribing support system. Int J Health Care Qual Assur Inc Leadersh Health Serv. 2004;17:173-9 pubmed
    ..Alerts and producer-independent drug information are valuable in reducing workload. However, technical prerequisites form the base for a successful implementation. Time must be given to adapt to new ways of working. ..
  32. Ho C, Lin M, Lo S. Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network. Environ Monit Assess. 2010;166:177-89 pubmed publisher
    ..Likewise, the methodology can overcome the difficulty of prioritizing pipeline replacement even in situations where the break-event records are unavailable. ..
  33. Johannigman J, Muskat P, Barnes S, Davis K, Beck G, Branson R. Autonomous control of oxygenation. J Trauma. 2008;64:S295-301 pubmed publisher
  34. Okayasu S, Nakamura M, Sugiyama T, Chigusa K, Sakurai K, Matsuura K, et al. Development of computer-assisted biohazard safety cabinet for preparation and verification of injectable anticancer agents. Chemotherapy. 2009;55:234-40 pubmed publisher
    ..The present computer-assisted biohazard safety cabinet for preparation of the mixture of anticancer agents is considered to be potentially useful for the safe management in cancer chemotherapy. ..
  35. Ayer T, Alagoz O, Chhatwal J, Shavlik J, Kahn C, Burnside E. Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration. Cancer. 2010;116:3310-21 pubmed publisher
    ..13. The authors' ANN can effectively discriminate malignant abnormalities from benign ones and accurately predict the risk of breast cancer for individual abnormalities. ..
  36. Merlot C. Computational toxicology--a tool for early safety evaluation. Drug Discov Today. 2010;15:16-22 pubmed publisher
    ..The current trend is to make simpler predictions, closer to the mechanism of action, and to follow them up with in vitro or in vivo assays as appropriate. ..
  37. Eslami S, Abu Hanna A, de Jonge E, de Keizer N. Tight glycemic control and computerized decision-support systems: a systematic review. Intensive Care Med. 2009;35:1505-17 pubmed publisher
  38. Mujat M, Ferguson R, Hammer D, Gittins C, Iftimia N. Automated algorithm for breast tissue differentiation in optical coherence tomography. J Biomed Opt. 2009;14:034040 pubmed publisher
  39. O Neill M, Carter R, Kish J, Gronlund C, White Newsome J, Manarolla X, et al. Preventing heat-related morbidity and mortality: new approaches in a changing climate. Maturitas. 2009;64:98-103 pubmed publisher
    ..A new computer-based decision tool will enable local estimates of heat-related health effects and potential savings from implementing a range of prevention strategies. ..
  40. Hubbard D. How to find clinical information quickly at the point of care. Fam Pract Manag. 2008;15:23-8 pubmed
  41. Boussadi A, Bousquet C, Sabatier B, Colombet I, Degoulet P. Specification of business rules for the development of hospital alarm system: application to the pharmaceutical validation. Stud Health Technol Inform. 2008;136:145-50 pubmed
    ..We produced 3 business rules patterns and 427 instances of rules. As SBVR is close to natural language, pharmacists were able to understand rules and participate to their design. ..
  42. Bandos A, Rockette H, Song T, Gur D. Area under the free-response ROC curve (FROC) and a related summary index. Biometrics. 2009;65:247-56 pubmed publisher
    ..We provide an example with a dataset sampled from a diagnostic imaging study and conduct simulations that demonstrate the appropriateness of the developed procedure for the considered sample sizes and ranges of parameters. ..
  43. Brooks D. Use of computer based testing of youth hockey players with concussions. NeuroRehabilitation. 2007;22:169-79 pubmed
    ..It also appears to enhance the education process for players, coaches, and parents on the potential seriousness of concussion for these young athletes. ..
  44. Bouaud J, Seroussi B, Brizon A, Culty T, Mentre F, Ravery V. How updating textual clinical practice guidelines impacts clinical decision support systems: a case study with bladder cancer management. Stud Health Technol Inform. 2007;129:829-33 pubmed
    ..A single new practice that modifies a decision taken in 49% of all recorded decisions leads to a fall from 67% to 46% of the compliance rate of decisions. ..
  45. Warrick P, Precup D, Hamilton E, Kearney R. Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis. Methods Inf Med. 2007;46:196-201 pubmed
    ..The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series. ..
  46. Kirkman B, Rosen B, Tesluk P, Gibson C. Enhancing the transfer of computer-assisted training proficiency in geographically distributed teams. J Appl Psychol. 2006;91:706-16 pubmed
  47. Muller M, Bergmann B, Koch T, Heller A. [Dynamic decision making in emergency medicine. Example of paraplegia after a traffic accident]. Anaesthesist. 2005;54:781-6 pubmed
    ..Dynamic decision-making has been in practise for a long time in aviation, similarities to decisions in medicine and the psychological background are described on the basis of the case report. ..
  48. Thomas L, Wickens C. Display dimensionality and conflict geometry effects on maneuver preferences for resolving in-flight conflicts. Hum Factors. 2008;50:576-88 pubmed
    ..Investigating maneuver preferences using the strategic flight planning paradigm employed in this study may be the key to better ensure pilot acceptance of computer-generated resolution maneuvers. ..
  49. Seyfang A, Paesold M, Votruba P, Miksch S. Improving the execution of clinical guidelines and temporal data abstraction high-frequency domains. Stud Health Technol Inform. 2008;139:263-72 pubmed
    ..This network performs the content of the plans triggered by the arriving patient data. Our approach evaluated to be efficient enough to handle high-frequency data while coping with complex guidelines and temporal data abstraction. ..
  50. Emmett C, Murphy D, Patel R, Fahey T, Jones C, Ricketts I, et al. Decision-making about mode of delivery after previous caesarean section: development and piloting of two computer-based decision aids. Health Expect. 2007;10:161-72 pubmed
    ..In general, participating women viewed the decision aids as a welcome addition to routine antenatal care. A randomized trial has been conducted to establish the effectiveness and cost-effectiveness of the decision aids. ..
  51. Bobb A, Payne T, Gross P. Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry. J Am Med Inform Assoc. 2007;14:41-7 pubmed
  52. Groenewold M. Enhancing local health department disaster response capacity with rapid community needs assessments: validation of a computerized program for binary attribute cluster sampling. Prehosp Disaster Med. 2006;21:32-9 pubmed
  53. Jordan D, Rose S. Multimedia abstract generation of intensive care data: the automation of clinical processes through AI methodologies. World J Surg. 2010;34:637-45 pubmed publisher
    ..MAGIC provides 200% more information, twice the accuracy, and enhances situational awareness. This study demonstrates that the automation of clinical processes through AI methodologies yields positive results. ..