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Validation of rule-based algorithms to determine colorectal, breast, and cervical cancer screening status using electronic health record data from an urban healthcare system in New York City.

ABSTRACT: Although cancer screening has greatly reduced colorectal cancer, breast cancer, and cervical cancer morbidity and mortality over the last few decades, adherence to cancer screening guidelines remains inconsistent, particularly among certain demographic groups. This study aims to validate a rule-based algorithm to determine adherence to cancer screening. A novel screening algorithm was applied to electronic health record (EHR) from an urban healthcare system in New York City to automatically determine adherence to national cancer screening guidelines for patients deemed eligible for screening. First, a subset of patients was randomly selected from the EHR and their data were exported in a de-identified manner for manual review of screening adherence by two teams of human reviewers. Interrater reliability for manual review was calculated using Cohen's Kappa and found to be high in all instances. The sensitivity and specificity of the algorithm was calculated by comparing the algorithm to the final manual dataset. When assessing cancer screening adherence, the algorithm performed with a high sensitivity (79%, 70%, 80%) and specificity (92%, 99%, 97%) for colorectal cancer, breast cancer, and cervical cancer screenings, respectively. This study validates an algorithm that can effectively determine patient adherence to colorectal cancer, breast cancer, and cervical cancer screening guidelines. This design improves upon previous methods of algorithm validation by using computerized extraction of essential components of patients' EHRs and by using de-identified data for manual review. Use of the described algorithm could allow for more precise and efficient allocation of public health resources to improve cancer screening rates.

SUBMITTER: Leder Macek AJ

PROVIDER: S-EPMC8683885 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR. METHODS:We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ??1:80), and a keyword of Raynaud's phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms. RESULTS:PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ??3 or ??4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ??4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword. CONCLUSIONS:Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.

Project description:Background:Globally, electronic information and communication technology has been applied and much expanded in the healthcare industry. However, in developing counties including Ethiopia, EMR system adoption and utilization for medical practice are still inconsistent, and healthcare institutions which started utilization currently have also failed to sustain. A desirable readiness of healthcare experts is mandatory to expand digital health service delivery. Thus, this study is aimed at estimating the proportion of the willingness of professionals in Bahir Dar city to use EMR and at identifying factors associated with this proportion. Methods:An institution-based cross-sectional study was conducted from September 1 to October 30, 2019, among 634 health professionals. Respondents were selected using a simple random sampling method. Data were entered into EpiData version 3.1 and exported to SPSS version 23 for further analysis. Descriptive statistics were computed to describe study variables and presented using tables. Willingness to use the EMR system was computed. Bivariable and multivariable binary logistic regression models were fitted to identify the associated factors. The odds ratio with 95% confidence interval was used to measure the strength of association. Results:A total of 616 health professionals participated in the study with a response rate of 97%. The proportion of willingness to use the EMR system was 85.9%. Among health professionals who were not willing to use EMR, lack of access to EMR training (73.4%) was a major barrier to the willingness to use EMR. A multivariable logistic regression analysis showed that those health professionals who had good computer skill (AOR = 2.5; 95% CI: 1.3-4.6), good knowledge on EMR (AOR = 2.1; 95% CI: 1-4.4), gotten EMR training (AOR = 3.8; 95% CI: 1.7-8.1), EMR guideline access (AOR = 2.8; 95% CI: 1.4-5.6), and management support (AOR = 2.6; 95% CI: 1.4-4.8) were more likely willing to use the EMR system. Conclusions:Majority of the professionals were willing to use the EMR system. EMR program should involve computer illiterate, less knowledgeable, those unable to access EMR guidelines, and managerially unsupported professionals. Enhancing health professionals' attitude and contextualizing EMR training in the healthcare curricula are highly recommended to scale up EMR use.

Project description:Urbanization is an increasingly pervasive form of land transformation that reduces biodiversity of many taxonomic groups. Beetles exhibit a broad range of responses to urbanization, likely due to the high functional diversity in this order. Carrion beetles (Order: Coleoptera, Family: Silphidae) provide an important ecosystem service by promoting decomposition of small-bodied carcasses, and have previously been found to decline due to forest fragmentation caused by urbanization. However, New York City (NYC) and many other cities have fairly large continuous forest patches that support dense populations of small mammals, and thus may harbor relatively robust carrion beetle communities in city parks. In this study, we investigated carrion beetle community composition, abundance and diversity in forest patches along an urban-to-rural gradient spanning the urban core (Central Park, NYC) to outlying rural areas. We conducted an additional study comparing the current carrion beetle community at a single suburban site in Westchester County, NY that was intensively surveyed in the early 1970's. We collected a total of 2,170 carrion beetles from eight species at 13 sites along this gradient. We report little to no effect of urbanization on carrion beetle diversity, although two species were not detected in any urban parks. Nicrophorus tomentosus was the most abundant species at all sites and seemed to dominate the urban communities, potentially due to its generalist habits and shallower burying depth compared to the other beetles surveyed. Variation between species body size, habitat specialization, and % forest area surrounding the surveyed sites also did not influence carrion beetle communities. Lastly, we found few significant differences in relative abundance of 10 different carrion beetle species between 1974 and 2015 at a single site in Westchester County, NY, although two of the rare species in the early 1970's were not detected in 2015. These results indicate that NYC's forested parks have the potential to sustain carrion beetle communities and the ecosystem services they provide.

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Validation of rule-based algorithms to determine colorectal, breast, and cervical cancer screening status using electronic health record data from an urban healthcare system in New York City.

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