Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results

2017 ◽  
Author(s):  
Mohamed Ghalwash ◽  
Ying Li ◽  
Ping Zhang ◽  
Jianying Hu
Keyword(s):  
Electronic Health Records ◽  
Laboratory Test ◽  
Drug Effects ◽  
Test Results ◽  
Health Records ◽  
Laboratory Test Results ◽  
Electronic Health

scholarly journals Graphical display of diagnostic test results in electronic health Records: a comparison of 8 systems

2015 ◽  
Vol 22 (4) ◽  
pp. 900-904 ◽  
Author(s):  
Dean F Sittig ◽  
Daniel R Murphy ◽  
Michael W Smith ◽  
Elise Russo ◽  
Adam Wright ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Laboratory Test ◽  
Clinical Laboratory ◽  
Negative Impact ◽  
Laboratory Data ◽  
Test Results ◽  
Health Records ◽  
Laboratory Test Results ◽  
Data Points ◽  
Electronic Health

Abstract Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x–y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

scholarly journals An individualized risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong

2020 ◽  
Author(s):  
Lin Yang ◽  
Tsun Kit Chu ◽  
Jinxiao Lian ◽  
Cheuk Wai Lo ◽  
Shi Zhao ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Laboratory Test ◽  
List Type ◽  
Test Results ◽  
Health Records ◽  
Laboratory Test Results ◽  
Electronic Health ◽  
New Onset

AbstractObjectivesThis study is aimed to develop and validate a prediction model for multi-state transitions across different stages of chronic kidney disease in patients with type 2 diabetes mellitus under primary care.SettingWe retrieved the anonymized electronic health records of a population based retrospective cohort in Hong Kong.ParticipantsA total of 26,197 patients were included in the analysis.Primary and secondary outcome measuresThe new-onset, progression, and regression of chronic kidney disease were defined by the transitions of four stages that were classified by combining glomerular filtration rate and urine albumin-to-creatinine ratio. We applied a multi-scale multi-state Poisson regression model to estimate the rates of the stage transitions by integrating the baseline demographic characteristics, routine laboratory test results and clinical data from electronic health records.ResultsDuring the mean follow-up time of 1.7 years, there were 2,935 patients newly diagnosed with chronic kidney disease, 1,443 progressed to the next stage and 1,971 regressed into an earlier stage. The models achieved the best performance in predicting the new-onset and progression with the predictors of sex, age, body mass index, systolic blood pressure, diastolic blood pressure, serum creatinine, HbA1c, total cholesterol, LDL, HDL, triglycerides and drug prescriptions.ConclusionsThis study demonstrated that individual risks of new-onset and progression of chronic kidney disease can be predicted from the routine physical and laboratory test results. The individualized prediction curves developed from this study could potentially be applied to routine clinical practices, to facilitate clinical decision making, risk communications with patients and early interventions.Article summaryStrengths of this studyEarly predictions for chronic kidney disease progression and timely intervention is critical for clinical management of patients with diabetes.We successfully developed a multi-scale multi-state Poisson regression models that achieved the satisfactory performance in predicting the new-onset and progression of chronic kidney diseases.The model incorporates the predictors of demographic characteristics, routine laboratory test results and clinical data from electronic health records.The individualized prediction curves could potentially be applied to facilitate clinical decision making, risk communications with patients and early interventions of CKD progression.Limitations of this studyThe cohort has a relatively short follow-up period and the retrospective study design might suffer from report bias and selection bias.

scholarly journals Evaluating the Use of Electronic Health Records for Type 2 Diabetes Surveillance in 2 California Counties, 2010-2014

2017 ◽  
Vol 132 (4) ◽  
pp. 463-470 ◽  
Author(s):  
Maxwell J. Richardson ◽  
Stephen K. Van Den Eeden ◽  
Eric Roberts ◽  
Assiamira Ferrara ◽  
Susan Paulukonis ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Diagnosis ◽  
Family Income ◽  
Census Data ◽  
Diabetes Prevalence ◽  
Future Research ◽  
Test Results ◽  
Health Records ◽  
Hba1c Test ◽  
Electronic Health

Objectives: Electronic health records (EHRs) and electronic laboratory records (ELRs) are increasingly seen as a rich source of data for performing public health surveillance activities and monitoring community health status. Their potential for surveillance of chronic illness, however, may be underused. Our objectives were to (1) evaluate the use of EHRs and ELRs for diabetes surveillance in 2 California counties and (2) examine disparities in diabetes prevalence by geography, income, and race/ethnicity. Methods: We obtained data on a clinical diagnosis of diabetes and hemoglobin A1c (HbA1c) test results for adult members of Kaiser Permanente Northern California living in Contra Costa County or Solano County at any time during 2010-2014. We evaluated the validity of using HbA1c test results to determine diabetes prevalence, using clinical diagnoses as a gold standard. We estimated disparities in diabetes prevalence by combining HbA1c test results with US Census data on income, race, and ethnicity. Results: When compared with a clinical diagnosis of diabetes, data on a patient’s 5-year maximum HbA1c value ≥6.5% yielded the best combination of sensitivity (87.4%) and specificity (99.2%). The prevalence of 5-year maximum HbA1c ≥6.5% decreased with increasing median family income and increased with greater proportions of residents who were either non-Hispanic black or Hispanic. Conclusions: Timely diabetes surveillance data from ELRs can be used to document disparities, target interventions, and evaluate changes in population health. ELR data may be easier to access than a patient’s entire EHR, but outcome metric validation with diabetes diagnoses would need to be ongoing. Future research should validate ELR and EHR data across multiple providers.

scholarly journals Exploring patients’ perceptions of accessing electronic health records: Innovation in healthcare

2017 ◽  
Vol 25 (1) ◽  
pp. 203-215 ◽  
Author(s):  
Sofie Wass ◽  
Vivian Vimarlund ◽  
Axel Ros
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Patient Involvement ◽  
Decision Makers ◽  
Health Record ◽  
Test Results ◽  
Healthcare Information ◽  
Health Records ◽  
Widespread Implementation ◽  
Electronic Health

The more widespread implementation of electronic health records has led to new ways of providing access to healthcare information, allowing patients to view their medical notes, test results, medicines and so on. In this article, we explore how patients perceive the possibility to access their electronic health record online and whether this influences patient involvement. The study includes interviews with nine patients and a survey answered by 56 patients. Our results show that patients perceive healthcare information to be more accessible and that electronic health record accessibility improves recall, understanding and patient involvement. However, to achieve the goal of involving patients as active decision-makers in their own treatment, electronic health records need to be fully available and test results, referrals and information on drug interactions need to be offered. As patient access to electronic health records spreads, it is important to gain a deeper understanding of how documentation practices can be changed to serve healthcare professionals and patients.

scholarly journals Reminders of Drug Effects on Laboratory Test Results

2000 ◽  
Vol 46 (9) ◽  
pp. 1395-1400 ◽  
Author(s):  
Marita Kailajärvi ◽  
Timo Takala ◽  
Paula Grönroos ◽  
Nils Tryding ◽  
Jorma Viikari ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Laboratory Tests ◽  
Drug Effects ◽  
Study Drug ◽  
Test Results ◽  
Potential Drug ◽  
Reminder System ◽  
Patient Medication ◽  
Laboratory Test Results ◽  
Laboratory Results

Abstract Drug effects on laboratory test results are difficult to take into account without an online decision support system. In this study, drug effects on hormone test results were coded using a drug-laboratory effect (DLE) code. The criteria that trigger the reminders were defined. To issue reminders, it was necessary to write a computer program linking the DLE knowledge base with databases containing individual patient medication and laboratory test results. During the first 10 months, 11% of the results from hormone samples were accompanied by one or more DLE reminders. The most common drugs to trigger reminders were glucocorticoids, furosemide, and metoclopramide. Physicians facing the reminders completed a questionnaire on the usefulness of the reminders. All respondents considered them useful. In addition, DLE reminders had caused 74% of respondents to refrain from additional, usually performed examinations. In conclusion, drug effects on laboratory tests should always be considered when interpreting laboratory results. An online reminder system is useful in displaying potential drug effects alongside test results.

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