scholarly journals Free Clinic Diagnosis Data Improvement Project Using International Classification of Diseases and Electronic Health Record

2021 ◽  
Vol 28 (6) ◽  
Author(s):  
Shanahan
Keyword(s):  
Electronic Health Record ◽  
International Classification Of Diseases ◽  
International Classification ◽  
Health Record ◽  
Free Clinic ◽  
Improvement Project ◽  
Classification Of Diseases ◽  
Electronic Health ◽  
Data Improvement

scholarly journals An electronic health record-based strategy to recruit for a Patient Advisory Council for Research: Implications for inclusion

2019 ◽  
Vol 4 (1) ◽  
pp. 69-72
Author(s):  
Nassira Bougrab ◽  
Dadong Li ◽  
Howard Trachtman ◽  
Scott Sherman ◽  
Rachel Thornton ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
International Classification Of Diseases ◽  
Advisory Council ◽  
Health Record ◽  
Patient Portal ◽  
Classification Of Diseases ◽  
Clinical Research Informatics ◽  
Race Ethnicity ◽  
Electronic Health

AbstractIn 2017, the NYU Clinical and Translational Science Institute’s Recruitment and Retention Unit created a Patient Advisory Council for Research (PACR) to provide feedback on clinical trials and health research studies. We collaborated with our clinical research informatics team to generate a random sample of patients, based on the International Classification of Diseases, Tenth Revision codes and demographic factors, for invitation via the patient portal. This approach yielded in a group that was diverse with regard to age, race/ethnicity, sex, and health conditions. This report highlights the benefits and limitations of using an electronic health record-based strategy to identify and recruit members for a PACR.

scholarly journals Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

2022 ◽  
Author(s):  
Veronica Brady ◽  
Meagan Whisenant ◽  
Xueying Wang ◽  
Vi K. Ly ◽  
Gen Zhu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Temporal Patterns ◽  
International Classification Of Diseases ◽  
Symptom Clusters ◽  
Health Record ◽  
Patient Reported ◽  
Classification Of Diseases ◽  
Electronic Health

<b>Purpose. </b>A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. <p><b>Methods. </b>We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (<i> n </i>= 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on <i>International Classification of Diseases</i>,<i> </i>9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. </p> <p><b>Results.</b> Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.</p> <p><b>Conclusion.</b> To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified. </p>

scholarly journals Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

2022 ◽  
Author(s):  
Veronica Brady ◽  
Meagan Whisenant ◽  
Xueying Wang ◽  
Vi K. Ly ◽  
Gen Zhu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Temporal Patterns ◽  
International Classification Of Diseases ◽  
Symptom Clusters ◽  
Health Record ◽  
Patient Reported ◽  
Classification Of Diseases ◽  
Electronic Health

OBJECTIVE A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. Methods We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes–related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

scholarly journals Using the Electronic Health Record to Identify Educational Gaps for Internal Medicine Interns

2017 ◽  
Vol 9 (1) ◽  
pp. 109-112 ◽  
Author(s):  
Alvin Rajkomar ◽  
Sumant R. Ranji ◽  
Bradley Sharpe
Keyword(s):  
Internal Medicine ◽  
Electronic Health Record ◽  
Medical Center ◽  
Academic Medical Center ◽  
International Classification Of Diseases ◽  
Health Record ◽  
Clinical Notes ◽  
Classification Of Diseases ◽  
Clinical Classification Software ◽  
Electronic Health

ABSTRACT Background  An important component of internal medicine residency is clinical immersion in core rotations to expose first-year residents to common diagnoses. Objective  Quantify intern experience with common diagnoses through clinical documentation in an electronic health record. Methods  We analyzed all clinical notes written by postgraduate year (PGY) 1, PGY-2, and PGY-3 residents on medicine service at an academic medical center July 1, 2012, through June 30, 2014. We quantified the number of notes written by PGY-1s at 1 of 3 hospitals where they rotate, by the number of notes written about patients with a specific principal billing diagnosis, which we defined as diagnosis-days. We used the International Classification of Diseases 9 (ICD-9) and the Clinical Classification Software (CCS) to group the diagnoses. Results  We analyzed 53 066 clinical notes covering 10 022 hospitalizations with 1436 different ICD-9 diagnoses spanning 217 CCS diagnostic categories. The 10 most common ICD-9 diagnoses accounted for 23% of diagnosis-days, while the 10 most common CCS groupings accounted for more than 40% of the diagnosis-days. Of 122 PGY-1s, 107 (88%) spent at least 2 months on the service, and 3% were exposed to all of the top 10 ICD-9 diagnoses, while 31% had experience with fewer than 5 of the top 10 diagnoses. In addition, 17% of PGY-1s saw all top 10 CCS diagnoses, and 5% had exposure to fewer than 5 CCS diagnoses. Conclusions  Automated detection of clinical experience may help programs review inpatient clinical experiences of PGY-1s.

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