scholarly journals Contribution of Different Patient Information Sources to Create the Best Possible Medication History

2020 ◽  
Vol 33 (6) ◽  
pp. 384 ◽  
Author(s):  
Joelizy Oliveira ◽  
Ana Cristina Cabral ◽  
Marta Lavrador ◽  
Filipa A. Costa ◽  
Filipe Félix Almeida ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Medical Records ◽  
Data Sources ◽  
Sources Of Information ◽  
Medication History ◽  
Cross Sectional ◽  
Health Records ◽  
Hospital Medical ◽  
Time Periods ◽  
Electronic Health

Introduction: Obtaining the best possible medication history is the crucial step in medication reconciliation. Our aim was to evaluate the potential contributions of the main data sources available – patient/caregiver, hospital medical records, and shared electronic health records – to obtain an accurate ‘best possible medication history’.Material and Methods: An observational cross-sectional study was conducted. Adult patients taking at least one medicine were included. Patient interview was performed upon admission and this information was reconciled with hospital medical records and shared electronic health records, assessed retrospectively. Concordance between sources was assessed. In the shared electronic health records, information was collected for four time-periods: the preceding three, six, nine and 12-months. The proportion of omitted data between time-periods was analysed.Results: A total of 148 patients were admitted, with a mean age of 54.6 ± 16.3 years. A total of 1639 medicines were retrieved. Only 29% were collected simultaneously in the three sources of information, 40% were only obtained in shared electronic health records and only 5% were obtained exclusively from patients. The total number of medicines gathered in shared electronic health records considering the different time frames were 778 (three-months), 1397 (six-months), 1748 (nine-months), and 1933 (12-months).Discussion: The use of shared electronic health records provides data that were omitted in the other data sources available and retrieving the information at six months is the most efficient procedure to establish the basis of the best possible medication history.Conclusion: Shared electronic health records should be the preferred source of information to supplement the patient or caregiver interview in order to increase the accuracy of best possible medication history of the patient, particularly if collected within the prior six months.

scholarly journals Learning longitudinal patterns and identifying subtypes of pediatric Crohn disease treated with infliximab via trajectory cluster analysis of electronic health records

2021 ◽  
Author(s):  
Andrew Chen ◽  
Ronen Stein ◽  
Robert N. Baldassano ◽  
Jing Huang
Keyword(s):  
Electronic Health Records ◽  
Disease Activity ◽  
Crohn Disease ◽  
Cross Sectional ◽  
Health Records ◽  
Disease Evolution ◽  
Current Classification ◽  
Electronic Health ◽  
Over Time

ABSTRACTBackgroundThe current classification of pediatric CD is mainly based on cross-sectional data. The objective of this study is to identify subgroups of pediatric CD through trajectory cluster analysis of disease activity using data from electronic health records.MethodsWe conducted a retrospective study of pediatric CD patients who had been treated with infliximab. The evolution of disease over time was described using trajectory analysis of longitudinal data of C-Reactive Protein (CRP). Patterns of disease evolution were extracted through functional principal components analysis and subgroups were identified based on those patterns using the Gaussian mixture model. We compared patient characteristics, a biomarker for disease activity, received treatments, and long-term surgical outcomes across subgroups.ResultsWe identified four subgroups of pediatric CD patients with differential relapse-and-remission risk profiles. They had significantly different disease phenotype (p < 0.001), CRP (p < 0.001) and calprotectin (p = 0.037) at diagnosis, with increasing percentage of inflammatory phenotype and declining CRP and fecal calprotectin levels from Subgroup 1 through 4. The risk of colorectal surgery within 10 years after diagnosis was significantly different between groups (p < 0.001). We did not find statistical significance in gender or age at diagnosis across subgroups, but the BMI z-score was slightly smaller in subgroup 1 (p =0.055).ConclusionsReadily available longitudinal data from electronic health records can be leveraged to provide a deeper characterization of pediatric Crohn disease. The identified subgroups captured novel forms of variation in pediatric Crohn disease that were not explained by baseline measurements and treatment information.SummaryThe current classification of pediatric Crohn disease mainly relies on cross-sectional data, e.g., the Paris classification. However, the phenotypic classification may evolve over time after diagnosis. Our study utilized longitudinal measures from the electronic health records and stratified pediatric Crohn disease patients with differential relapse-and-remission risk profiles based on patterns of disease evolution. We found trajectories of well-maintained low disease activity were associated with less severe disease at baseline, early initiation of infliximab treatment, and lower risk of surgery within 10 years of diagnosis, but the difference was not fully explained by phenotype at diagnosis.

Association Between Meaningful Use and Florida Counties’ COVID-19 Case and Death Rates Among Medicaid Providers (Preprint)

2021 ◽  
Author(s):  
Katherine Freeman ◽  
Judith P. Monestime
Keyword(s):  
Electronic Health Records ◽  
Cumulative Incidence ◽  
Meaningful Use ◽  
County Level ◽  
Cross Sectional ◽  
Health Records ◽  
Death Rates ◽  
Public Health Reporting ◽  
Electronic Health ◽  
Population Demographic

BACKGROUND Although the Health Information Technology for Economic and Clinical Health (HITECH) Act has accelerated the adoption of Meaningful Use of Electronic Health Records (EHRs) among Medicaid providers, only about half achieve Meaningful Use. Furthermore, the validity of public health reporting of COVID-19 outcomes, which relies on Meaningful Use advanced functions, remains unknown. OBJECTIVE This study aims to examine the difference between Medicaid providers who did and did not achieve Meaningful Use regarding Florida county-level incidence rates of COVID-19 cases and deaths, accounting for county-level race/ethnicity, unemployment, income, prevalence of respiratory diseases, age, poverty, and healthcare environment. METHODS This cross-sectional ecologic study examined the association between Meaningful Use achievement by Medicaid providers and COVID-19 cases and death rates from 67 Florida counties as of November 19, 2020. Provider information was obtained from the publicly available database from the Florida Medicaid Promoting Interoperability Program, formerly Electronic Health Record Incentive Program. The database includes the Area Health Resources File, capturing provider characteristics and population demographic and socioeconomic characteristics at the county level. Cumulative COVID-19 cases and deaths were obtained from the Florida Department of Health Open Data (FDOH) for zip codes which were aggregated by county. Rates were obtained by dividing cumulative incidence or prevalence by the U.S. Census County population. RESULTS As of November 19, 2020, the cumulative incidence rate of COVID-19 deaths was significantly different between Medicaid providers who achieved Meaningful Use and those who did not (P=.0131), with relatively more deaths reported for those not achieving Meaningful Use. County-level characteristics associated with increased COVID-19 death rates in hierarchical models include greater concentrations of persons of African American or Black race (P<.0001), lower median household income (P<.0001), higher unemployment (P<.0001), and higher concentrations of those living in poverty (P<.0001) and without health insurance (P<.0001). CONCLUSIONS Although Federal subsidies successfully influenced the adoption of Electronic Health Records, our findings suggest an emerging further digital "advanced use" divide among patients cared for by Medicaid providers. Policy interventions need to be reevaluated to address disparities in COVID-19 clinical outcomes which appear exacerbated by the limited use of advanced Electronic Health Records functions. CLINICALTRIAL not applicable

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

scholarly journals Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e029594 ◽  
Author(s):  
Concepción Violán ◽  
Quintí Foguet-Boreu ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Margarita Cabrera-Bean ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Soft Clustering ◽  
Fuzzy C Means ◽  
Electronic Health

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

scholarly journals Provider and Patient Determinants of Generic Levothyroxine Prescribing: An Electronic Health Records–Based Study

2017 ◽  
Vol 51 (8) ◽  
pp. 640-648 ◽  
Author(s):  
Robert J. Romanelli ◽  
Vani Nimbal ◽  
Sarah K. Dutcher ◽  
Xia Pu ◽  
Jodi B. Segal
Keyword(s):  
Electronic Health Records ◽  
Intraclass Correlation ◽  
Fixed Effect ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Health Records ◽  
Residual Variation ◽  
Generic Prescribing ◽  
Electronic Health ◽  
To Receive

Background: Despite the availability of generic levothyroxine products for more than a decade, uptake of these products is poor. Objective: We sought to evaluate determinants of generic prescribing of levothyroxine. Methods: In a cross-sectional analysis of electronic health records data between 2010 and 2013, we identified adult patients with a levothyroxine prescription from a primary-care physician (PCP) or endocrinologist. We used mixed-effect logistic regression models with random intercepts for prescribing provider to examine predictors of generic levothyroxine prescribing. Models include patient, prescription, and provider fixed-effect covariates. Odds ratios (ORs) and 95% CIs were generated. Between-provider random variation was quantified by the intraclass correlation coefficient (ICC). Results: Study patients (n = 63 838) were clustered among 941 prescribing providers within 25 ambulatory care clinics. The overall prevalence of generic prescribing of levothyroxine was 73%. In the multivariable mixed-effect model, patients were significantly less likely to receive generic levothyroxine from an endocrinologist than a PCP (OR = 0.43; 95% CI = 0.33-0.55; P < 0.001). Women were less likely to receive generic levothyroxine than men from endocrinologists (OR = 0.68; 95% CI = 0.59-0.78; P < 0.001) but not from PCPs. Between-provider variation in generic prescribing was 18.3% in the absence of fixed-effect covariates and could be explained marginally by patient, prescription, and provider factors (ICC = 15.9%). Conclusions: Generic levothyroxine prescribing differed by PCPs and endocrinologists. Residual variation in generic prescribing, after accounting for measurable factors, indicates the need for provider interventions or patient education aimed at improving levothyroxine generic uptake.

scholarly journals Scaling-up social needs screening in practice: a retrospective, cross-sectional analysis of data from electronic health records from Bronx county, New York, USA

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e053633
Author(s):  
Kevin P Fiori ◽  
Caroline G Heller ◽  
Anna Flattau ◽  
Nicole R Harris-Hollingsworth ◽  
Amanda Parsons ◽  
...  
Keyword(s):  
Primary Care ◽  
New York ◽  
Electronic Health Records ◽  
Social Care ◽  
Social Needs ◽  
Cross Sectional ◽  
Health Records ◽  
Care Practices ◽  
Electronic Health ◽  
Primary Care Practices

ObjectivesThere has been renewed focus on health systems integrating social care to improve health outcomes with relatively less related research focusing on ‘real-world’ practice. This study describes a health system’s experience from 2018 to 2020, following the successful pilot in 2017, to scale social needs screening of patients within a large urban primary care ambulatory network.SettingAcademic medical centre with an ambulatory network of 18 primary care practices located in an urban county in New York City (USA).ParticipantsThis retrospective, cross-sectional study used electronic health records of 244 764 patients who had a clinical visit between 10 April 2018 and 8 December 2019 across any one of 18 primary care practices.MethodsWe organised measures using the RE-AIM framework domains of reach and adoption to ascertain the number of patients who were screened and the number of providers who adopted screening and associated documentation, respectively. We used descriptive statistics to summarise factors comparing patients screened versus those not screened, the prevalence of social needs screening and adoption across 18 practices.ResultsBetween April 2018 and December 2019, 53 093 patients were screened for social needs, representing approximately 21.7% of the patients seen. Almost one-fifth (19.6%) of patients reported at least one unmet social need. The percentage of screened patients varied by both practice location (range 1.6%–81.6%) and specialty within practices. 51.8% of providers (n=1316) screened at least one patient.ConclusionsThese findings demonstrate both the potential and challenges of integrating social care in practice. We observed significant variability in uptake across the health system. More research is needed to better understand factors driving adoption and may include harmonising workflows, establishing unified targets and using data to drive improvement.

scholarly journals THE IMPACT OF MEETING ACCREDITATION STANDARDS ON THE QUALITY OF THE ELECTRONIC HEALTH RECORDS

2020 ◽  
pp. 114-117
Author(s):  
MOHAMED HOSSAM ATTIA ◽  
ABDELNASSER IBRAHIM
Keyword(s):  
Electronic Health Records ◽  
Patient Information ◽  
Medical Records ◽  
Simple Random Sample ◽  
Health Records ◽  
Entire Study ◽  
Before And After ◽  
Electronic Health ◽  
The Impact

Objective: Electronic health records (EHRs) are considered a way to make the management of patient information easier, improve efficiency, and decrease costs related to medical information management. Compliance with requirements from accreditation bodies on quality of documentation ensures the complete and accurate patient information in the EHR. The purpose of this study is to measure the effect of quality accreditation on the quality of documentation in the EHR. Methods: A simple random sample of 18% of patient records was manually selected each month during the entire study period from the population of discharged patients. The auditing process included 18 months starting from January 2014 until June 2015. The data collection was performed by a quality management unit using a modified medical record completeness checklist adapted from Joint Commission International (JCI) criteria. Results: The results of the study show the improvement in compliance with complete medical records’ documentation after the JCI accreditation. However, after the accreditation, the compliance suffers a dramatic fall which could be referred to the post-accreditation slump. The compliance then improved again to reach higher levels of compliance. Using paired t-test, the mean of total compliance with complete and accurate medical records in October 2014 was less than in May 2015. Conclusion: This study highlighted the performance of one process before and after the first accreditation of the organization showing the real difference between the performance before and after the accreditation and explaining the drop that happened just after the accreditation.

An Empirical Case Analysis on Electronic Health Records on Global Perspectives

2020 ◽  
pp. 160-165
Author(s):  
Arulmurugan Ramu ◽  
Anandakumar Haldorai
Keyword(s):  
Electronic Health Records ◽  
Medical Records ◽  
Data Analytics ◽  
Case Analysis ◽  
Patient Records ◽  
Digital Repository ◽  
Health Records ◽  
Medical Practitioners ◽  
Global Perspectives ◽  
Electronic Health

The maintenance and logging in the health records is always required so that the overall predictive mining can be done on the patient records. In addition, the recording and maintenance of electronic health records is quite mandatory whereby the digital repository related to the patient is very important so that the future based predictions and the analytics can be retained. In addition to this, the patient records are providing the medical practitioners the higher degree of accuracy in the predictions and the aspects related to the knowledge discovery about that particular patient to have the effectiveness. By this way, the overall medical records can be maintained. In this research manuscript, the enormous tools and the vendors are presented usable for the electronic health records. The presented work is having the cavernous analytics on the vendor products associated with the electronic health records whereby the global perspectives and data analytics are cited.

EEMI - An Electronic Health Record for Pediatricians

2020 ◽  
pp. 614-628
Author(s):  
Juan C. Lavariega ◽  
Roberto Garza ◽  
Lorena G Gómez ◽  
Victor J. Lara-Diaz ◽  
Manuel J. Silva-Cavazos
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Data Sharing ◽  
Medical Records ◽  
Data Exchange ◽  
Data Representation ◽  
Health Record ◽  
Health Records ◽  
Health Area ◽  
Electronic Health

The use of paper health records and handwritten prescriptions are prone to preset errors of misunderstanding instructions or interpretations that derive in affecting patients' health. Electronic Health Records (EHR) systems are useful tools that among other functions can assists physicians' tasks such as finding recommended medicines, their contraindications, and dosage for a given diagnosis, filling prescriptions and support data sharing with other systems. This paper presents EEMI, a Children EHR focused on assisting pediatricians in their daily office practice. EEMI functionality keeps the relationships among diagnosis, treatment, and medications. EEMI also calculates dosages and automatically creates prescriptions which can be personalized by the physician. The system also validates patient allergies. This paper also presents the current use of EHRs in Mexico, the Mexican Norm (NOM-024-SSA3-2010), standards for the development of electronic medical records and its relationships with other standards for data exchange and data representation in the health area.

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