Improving Follow-Up of Abnormal Cancer Screens Using Electronic Health Records

2013 ◽  
pp. 297-309
Author(s):  
Hardeep Singh ◽  
Lindsey Wilson ◽  
Laura Petersen ◽  
Mona Sawhney ◽  
Brian Reis ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

USE OF ELECTRONIC HEALTH RECORDS TO CHARACTERIZE A RARE DISEASE IN THE U.S.: TREATMENT, COMORBIDITIES, AND FOLLOW-UP TRENDS AMONG PATIENTS WITH A CONFIRMED DIAGNOSIS OF ACROMEGALY

2018 ◽  
Vol 24 (6) ◽  
pp. 517-526 ◽  
Author(s):  
Julie M. Silverstein ◽  
Erin D. Roe ◽  
Kashif M. Munir ◽  
Janet L. Fox ◽  
Birol Emir ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Rare Disease ◽  
Health Records ◽  
Electronic Health ◽  
The U.S

scholarly journals Electronic health record note review in an outpatient specialty clinic: who is looking?

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Jimmy S Chen ◽  
Michelle R Hribar ◽  
Isaac H Goldstein ◽  
Adam Rule ◽  
Wei-Chun Lin ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Clinical Care ◽  
Office Visit ◽  
Health Record ◽  
Office Visits ◽  
Health Records ◽  
Ancillary Staff ◽  
Electronic Health

Abstract Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.

scholarly journals Improving follow-up of abnormal cancer screens using electronic health records: trust but verify test result communication

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Hardeep Singh ◽  
Lindsey Wilson ◽  
Laura A Petersen ◽  
Mona K Sawhney ◽  
Brian Reis ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health ◽  
Test Result

scholarly journals Costs and outcomes of increasing access to bariatric surgery for obesity: cohort study and cost-effectiveness analysis using electronic health records

2016 ◽  
Vol 4 (17) ◽  
pp. 1-120 ◽  
Author(s):  
Martin C Gulliford ◽  
Judith Charlton ◽  
Helen P Booth ◽  
Alison Fildes ◽  
Omar Khan ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Health Care ◽  
Morbid Obesity ◽  
Cost Effectiveness ◽  
Electronic Health Records ◽  
Diabetes Remission ◽  
Health Records ◽  
Electronic Health ◽  
Care Costs

BackgroundBariatric surgery is known to be an effective treatment for extreme obesity but access to these procedures is currently limited.ObjectiveThis study aimed to evaluate the costs and outcomes of increasing access to bariatric surgery for severe and morbid obesity.Design and methodsPrimary care electronic health records from the UK Clinical Practice Research Datalink were analysed for 3045 participants who received bariatric surgery and 247,537 general population controls. The cost-effectiveness of bariatric surgery was evaluated in severe and morbid obesity through a probabilistic Markov model populated with empirical data from electronic health records.ResultsIn participants who did not undergo bariatric surgery, the probability of participants with morbid obesity attaining normal body weight was 1 in 1290 annually for men and 1 in 677 for women. Costs of health-care utilisation increased with body mass index category but obesity-related physical and psychological comorbidities were the main drivers of health-care costs. In a cohort of 3045 adult obese patients with first bariatric surgery procedures between 2002 and 2014, bariatric surgery procedure rates were greatest among those aged 35–54 years, with a peak of 37 procedures per 100,000 population per year in women and 10 per 100,000 per year in men. During 7 years of follow-up, the incidence of diabetes diagnosis was 28.2 [95% confidence interval (CI) 24.4 to 32.7] per 1000 person-years in controls and 5.7 (95% CI 4.2 to 7.8) per 1000 person-years in bariatric surgery patients (adjusted hazard ratio was 0.20, 95% CI 0.13 to 0.30;p < 0.0001). In 826 obese participants with type 2 diabetes mellitus who received bariatric surgery, the relative rate of diabetes remission, compared with controls, was 5.97 (95% CI 4.86 to 7.33;p < 0.001). There was a slight reduction in depression in the first 3 years following bariatric surgery that was not maintained. Incremental lifetime costs associated with bariatric surgery were £15,258 (95% CI £15,184 to £15,330), including costs associated with bariatric surgical procedures of £9164 per participant. Incremental quality-adjusted life-years (QALYs) were 2.142 (95% CI 2.031 to 2.256) per participant. The estimated cost per QALY gained was £7129 (95% CI £6775 to £7506). Estimates were similar across gender, age and deprivation subgroups.LimitationsIntervention effects were derived from a randomised trial with generally short follow-up and non-randomised studies of longer duration.ConclusionsBariatric surgery is associated with increased immediate and long-term health-care costs but these are exceeded by expected health benefits to obese individuals with reduced onset of new diabetes, remission of existing diabetes and lower mortality. Diverse obese individuals have clear capacity to benefit from bariatric surgery at acceptable cost.Future workFuture research should evaluate longer-term outcomes of currently used procedures, and ways of delivering these more efficiently and safely.FundingThe National Institute for Health Research (NIHR) Health Services and Delivery Research programme. Martin C Gulliford and A Toby Prevost were supported by the NIHR Biomedical Research Centre at Guy’s and St Thomas’ Hospitals. Peter Littlejohns was supported by the South London Collaboration for Leadership in Applied Health Research and Care. The funders did not engage in the design, conduct or reporting of the research.

scholarly journals Validation & Vindication – Comparing Electronic Health Records with Hospital Notes

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Alex Hacker
Keyword(s):  
Electronic Health Records ◽  
Additional Data ◽  
Disease Diagnosis ◽  
Tablet Computers ◽  
Health Records ◽  
Technical Challenge ◽  
Disease Phenotypes ◽  
Custom Software ◽  
Electronic Health

IntroductionNo doubt your Electronic Health Records have been meticulously gathered, imported, validated and standardised. However, if you want to be certain that they are an accurate representation of reality, you can’t beat physically going to hospitals and cross-checking their records against yours. Our biobank did exactly this. Objectives and ApproachOur validation exercise encompassed all reported cases in our follow-up data of three key conditions: stroke, heart disease, and cancer. Key data about each hospitalisation was extracted and exported to tablet computers running custom software. Our staff then visited each hospital in this dataset seeking the corresponding medical notes, and collected additional data from those that they found including photographs of key documents. These results were then adjudicated by specialist physicians to determine the accuracy of the diagnosis, and identify disease phenotypes of interest. Finally, all these results were merged back into our follow-up data. ResultsNot only was gathering the data a huge logistical and technical challenge, integrating it back into the database presented its own difficulties. Our initial plan was to assign each sought event a status of ‘validated’, ‘corrected’ or ‘unfound’. However, this proved inadequate for addressing the complexities of the data, as we will discuss, with examples. Our solution was to initially treat the retrieved hospital notes as simply another source of follow-up data. We were thus able to use our existing systems for validating, standardising and aggregating events; and thus produce validated endpoints that were meaningfully comparable to our reported endpoints. We could then implement and test definitions of the required validation statuses at a participant level for each disease of interest. Conclusion/ImplicationsThis validation project was a huge and daunting undertaking, but repaid our investment with proof that our Electronic Health Records were generally very reliable, and also with much richer data about disease diagnosis and phenotyping. Other projects using Electronic Health Records may wish to adopt this approach.

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