Prevalence and Incidence Rates of Dementia: A Nationwide Population-Based Study of Electronic Health Records in Israel1

2021 ◽  
pp. 1-9
Author(s):  
Miri Lutski ◽  
Iris Rasooli ◽  
Shelley Sternberg ◽  
John Lemberger ◽  
Nisim Mery ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
National Level ◽  
Health Maintenance Organizations ◽  
Incidence Rates ◽  
Service Planning ◽  
Prevalence Rates ◽  
Health Records ◽  
Health Maintenance ◽  
Icd Codes ◽  
Electronic Health

Background: Data on the rate of dementia is essential for planning and developing appropriate services at the national level. Objective: We report the prevalence and incidence of dementia, based on electronic health records available for the whole population. Methods: This national dementia dataset was established as a part of the National Program to Address Alzheimer’s and Other Types of Dementia. Data from medical health records for all persons aged 45+ in Israel, for 2016, were extracted from the databases of the four health maintenance organizations. Dementia cases were identified based on either recorded dementia diagnosis, through International Classification of Diseases (ICD-9 and ICD-10) or dispensation of anti-dementia drugs. The date of first diagnosis was determined by the earliest recording. Results: A total of 65,951 persons with dementia, aged 45+, were identified from electronic health data. Based on both ICD codes and anti-dementia drugs, the prevalence rates of dementia among individuals aged 45+ and 65+ in 2016 were 2.5%and 6.4%, respectively, and the incidence rates were 0.49%and 1.3%, respectively. Based on ICD codes alone, the prevalence rates of dementia among individuals aged 45+ and 65+ in 2016 were 2.1%and 5.4%respectively, and the incidence rates were 0.36%and 0.96%respectively. The rates were higher among females compared to males and paradoxically lower in lower socioeconomic status compared to higher statuses. Conclusion: This data collection reflects the present access of dementia patients to medical care resources and provides the basis for service planning and future dementia policies.

scholarly journals Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

Rheumatology ◽  
2019 ◽  
Vol 59 (5) ◽  
pp. 1059-1065 ◽  
Author(s):  
Sizheng Steven Zhao ◽  
Chuan Hong ◽  
Tianrun Cai ◽  
Chang Xu ◽  
Jie Huang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Free Text ◽  
Text Data ◽  
Health Records ◽  
Disease Concepts ◽  
Icd Codes ◽  
Electronic Health

Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

Principles for Safe Implementation of ICD Codes for Human Trafficking

2021 ◽  
Vol 49 (2) ◽  
pp. 285-289 ◽  
Author(s):  
Jordan Greenbaum ◽  
Ashley Garrett ◽  
Katherine Chon ◽  
Matthew Bishop ◽  
Jordan Luke ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Human Trafficking ◽  
Health Consequences ◽  
Health Records ◽  
Adverse Health ◽  
Icd Codes ◽  
Electronic Health

AbstractHuman trafficking is associated with a variety of adverse health and mental health consequences, which should be accurately addressed and documented in electronic health records.

scholarly journals Consistency of denominator data in electronic health records in Australian primary healthcare services: enhancing data quality

2015 ◽  
Vol 21 (4) ◽  
pp. 450 ◽  
Author(s):  
Ross Bailie ◽  
Jodie Bailie ◽  
Amal Chakraborty ◽  
Kevin Swift
Keyword(s):  
Quality Of Care ◽  
Electronic Health Records ◽  
Data Quality ◽  
Primary Healthcare ◽  
Service Planning ◽  
Health Records ◽  
General Practices ◽  
Electronic Health ◽  
Access And Quality

The quality of data derived from primary healthcare electronic systems has been subjected to little critical systematic analysis, especially in relation to the purported benefits and substantial investment in electronic information systems in primary care. Many indicators of quality of care are based on numbers of certain types of patients as denominators. Consistency of denominator data is vital for comparison of indicators over time and between services. This paper examines the consistency of denominator data extracted from electronic health records (EHRs) for monitoring of access and quality of primary health care. Data collection and analysis were conducted as part of a prospective mixed-methods formative evaluation of the Commonwealth Government’s Indigenous Chronic Disease Package. Twenty-six general practices and 14 Aboriginal Health Services (AHSs) located in all Australian States and Territories and in urban, regional and remote locations were purposively selected within geographically defined locations. Percentage change in reported number of regular patients in general practices ranged between –50% and 453% (average 37%). The corresponding figure for AHSs was 1% to 217% (average 31%). In approximately half of general practices and AHSs, the change was ≥20%. There were similarly large changes in reported numbers of patients with a diagnosis of diabetes or coronary heart disease (CHD), and Indigenous patients. Inconsistencies in reported numbers were due primarily to limited capability of staff in many general practices and AHSs to accurately enter, manage, and extract data from EHRs. The inconsistencies in data required for the calculation of many key indicators of access and quality of care places serious constraints on the meaningful use of data extracted from EHRs. There is a need for greater attention to quality of denominator data in order to realise the potential benefits of EHRs for patient care, service planning, improvement, and policy. We propose a quality improvement approach for enhancing data quality.

scholarly journals Development of a Natural Language Processing Machine to Enhance Identification of Opioid Use Disorder in Electronic Health Records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Avery Chadd ◽  
Rebecca Silvola ◽  
Yana Vorontsova ◽  
Andrea Broyles ◽  
Jonathan Cummins ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Health Records ◽  
Icd Codes ◽  
Electronic Health

Background/Objective: Real-world data, including electronic health records (EHRs), has shown tremendous utility in research relating to opioid use disorder (OUD). Traditional analysis of EHR data relies on explicit diagnostic codes and results in incomplete capture of cases and therefore underrepresentation of OUD rates. Machine learning can rectify this by surveying free clinical notes in addition to structured codes. This study aimed to address disparities between true OUD rates and cases identified using traditional ICD codes by developing a natural language processing (NLP) machine for identifying affected patients from EHRs. Methods: Patients (≥12 years old) who had received an opioid prescription from IU Health or Eskenazi Health between 1/1/2009 and 12/31/2015 were identified by the Regenstrief Institute. Exclusion criteria included any cancer, sickle cell anemia, or palliative care diagnoses. Cases of OUD were identified through ICD codes and NLP. The NLP machine was developed using a dictionary of key OUD terms and a training corpus of 300 patient notes. A testing corpus of 148 patient notes was constructed and validated by manual review. The NLP machine and ICD 9/10 codes were independently tested against this corpus. Results: Although ICD codes identified OUD cases with high specificity (98.08%), this method demonstrated moderate sensitivity (53.13%), accuracy (68.92%), and F1 score (68.92%). Testing using the NLP method demonstrated increased sensitivity (93.75%), increased accuracy (89.19%), and increased F1 score (91.84%); specificity mildly decreased (80.77%). Conclusion: Our revised NLP machine was more effective at capturing OUD cases in EHRs than traditional identification using ICD codes. This illustrates NLP’s enhanced capability of identifying OUD cases compared to structured data. Potential Impacts: These findings establish a role for NLP in OUD research involving large datasets. Ultimately, this is intended to improve identification of risk factors for OUD, which is of significant clinical importance during a public health crisis. 

Electronic health records: Context matters!

2016 ◽  
Vol 34 (2) ◽  
pp. 163-165 ◽  
Author(s):  
William B. Ventres ◽  
Richard M. Frankel
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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