Patient facing decision support system for interpretation of laboratory test results

BackgroundIn some healthcare systems, it is common that patients address laboratory test centers directly without a physician’s recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results.Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctor’s referral.MethodsWe have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results.The Wilson and Lankton’s assessment model was applied to measure patients’ acceptance of the solution.ResultsA first order predicates-based decision support system has been implemented to analyze laboratory test results and deliver reports in natural language to patients. The evaluation of the system showed a high acceptance of the decision support system and of the reports that it generates.ConclusionsDetailed notification of the laboratory service patients with elements of the decision support is significant for the laboratory data management, and for patients’ empowerment and safety.

[1]  Urmimala Sarkar,et al.  Frequency of failure to inform patients of clinically significant outpatient test results. , 2009, Archives of internal medicine.

[2]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Elizabeth W. Staton,et al.  Patient preferences for notification of normal laboratory test results: A report from the ASIPS Collaborative , 2005, BMC family practice.

[4]  Georgy Kopanitsa,et al.  Implementation of a Clinical Decision Support System for Interpretation of Laboratory Tests for Patients , 2016, pHealth.

[5]  J C Wyatt,et al.  Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems. , 2010, Health technology assessment.

[6]  Michael E Matheny,et al.  Impact of an automated test results management system on patients' satisfaction about test result communication. , 2007, Archives of internal medicine.

[7]  K. Courneya,et al.  Development and Evaluation of a Theory-Based Physical Activity Guidebook for Breast Cancer Survivors , 2008, Health education & behavior : the official publication of the Society for Public Health Education.

[8]  David W Bates,et al.  "I wish I had seen this test result earlier!": Dissatisfaction with test result management systems in primary care. , 2004, Archives of internal medicine.

[9]  Dean F Sittig,et al.  Effectiveness of an Electronic Health Record-based Intervention to Improve Follow-up of Abnormal Pathology Results: A Retrospective Record Analysis , 2012, Medical care.

[10]  Georgy Kopanitsa.  Standard Based Multiclient Medical Data Visualization , 2012, MIE.

[11]  Rong Chen,et al.  How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments , 2016, BMC Medical Informatics and Decision Making.

[12]  Georgy Kopanitsa,et al.  Analysis of Metrics for the Usability Evaluation of EHR Management Systems , 2012, MIE.

[13]  I. Litchfield,et al.  Adaption, implementation and evaluation of collaborative service improvements in the testing and result communication process in primary care from patient and staff perspectives: a qualitative study , 2017, BMC Health Services Research.

[14]  Cui Tao,et al.  Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain , 2016, Comput. Methods Programs Biomed..

[15]  Sharon Sung BS,et al.  Direct reporting of laboratory test results to patients by mail to enhance patient safety , 2008, Journal of General Internal Medicine.

[16]  Andrew Georgiou,et al.  Failure to Follow-Up Test Results for Ambulatory Patients: A Systematic Review , 2011, Journal of General Internal Medicine.

[17]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[18]  Xin Lu,et al.  The PAADRN study: a design for a randomized controlled practical clinical trial to improve bone health. , 2013, Contemporary clinical trials.

[19]  Manuel Baena-García,et al.  DB4US: A Decision Support System for Laboratory Information Management , 2012, Interactive journal of medical research.

[20]  J. Rogel-Salazar.  Essential MATLAB and Octave , 2014 .

[21]  Chung-Feng Liu,et al.  An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems , 2013, BMC Medical Informatics and Decision Making.

[22]  David W. Bates,et al.  Primary care physician attitudes concerning follow-up of abnormal test results and ambulatory decision support systems , 2003, Int. J. Medical Informatics.

[23]  Donald S Young,et al.  The ideal laboratory information system. , 2013, Archives of pathology & laboratory medicine.

[24]  Martin R. W. Hiebl.  Applying the four-eyes principle to management decisions in the manufacturing sector: Are large family firms one-eye blind? , 2015 .

[25]  B L Drinkwater,et al.  A comparison of the direction-of-perception technique with the Likert method in the measurement of attitudes. , 1965, The Journal of social psychology.

[26]  Yung-Hsiu Lin,et al.  Development and Evaluation of Theory-Based Diabetes Support Services , 2013, Computers, informatics, nursing : CIN.

[27]  Georgy Kopanitsa,et al.  Development of a Clinical Decision Support System for the Patients of a Laboratory Service , 2016, MIE.

[28]  B. Walker,et al.  Development and Evaluation of a Smartphone Application for Managing Gestational Diabetes Mellitus , 2016, Healthcare informatics research.

[29]  Blackford Middleton,et al.  Sharing Electronic Laboratory Results in a Patient Portal - a Feasibility Pilot , 2007, MedInfo.

[30]  Shu Yu,et al.  Applying the technology acceptance model to explore public health nurses' intentions towards web-based learning: a cross-sectional questionnaire survey. , 2008, International journal of nursing studies.

[31]  Nicolette de Keizer,et al.  The role of standardized data and terminological systems in computerized clinical decision support systems: Literature review and survey , 2011, Int. J. Medical Informatics.

[32]  C. Teasdale,et al.  Communicating the result of breast biopsy by telephone or in person , 1997, The British journal of surgery.

[33]  Georgy Kopanitsa.  Evaluation study for a multi-user oriented medical data visualization method. , 2014, Studies in health technology and informatics.

[34]  Yoichi Hayashi,et al.  Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system , 2004, Artif. Intell. Medicine.

[35]  Janis E. Johnston,et al.  Weighted Kappa for Multiple Raters , 2008, Perceptual and motor skills.

[36]  Anne-Marie Guerguerian,et al.  “Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology” , 2017, BMC Medical Informatics and Decision Making.

[37]  Georgy Kopanitsa,et al.  Analysis of Metrics for the Usability Evaluation of Electronic Health Record Systems , 2012, EFMI-STC.

[38]  F. Arzideh,et al.  Proposed classification of various limit values (guide values) used in assisting the interpretation of quantitative laboratory test results , 2009, Clinical chemistry and laboratory medicine.

[39]  T. Kawada.  Sample size in receiver-operating characteristic (ROC) curve analysis. , 2012, Circulation journal : official journal of the Japanese Circulation Society.

[40]  E. Vance Wilson,et al.  Modeling patients' acceptance of provider-delivered e-health. , 2004, Journal of the American Medical Informatics Association : JAMIA.