Cohort analysis in the medical field involves grouping patients based on certain characteristics or experiences and studying their health outcomes over time. It is an important tool for identifying risk factors, evaluating treatments, improving patient care, and advancing medical research.
By comparing the outcomes of different patient groups, it is possible to understand the factors that influence patient health and to develop targeted interventions and approaches to care that are tailored to the needs of specific patient groups.
The complexity and size of medical data can pose challenges for cohort analysis. The data collected during a single office visit may be interdependent and may have multiple relationships with other data points. This size and interdependency is often too difficult for traditional business intelligence tools; however, the Snowflake Data Cloud and Sigma Computing were intentionally built to handle these large complex problems.
This data was created through a combination of populating fake data from Mockorro and manually overwriting data in Alteryx. The data contains basic patient information such as a patient ID, gender, race, and birthday. It also contains a wide variety of ICD10 Diagnosis Descriptions and diagnosis dates for each patient. Lastly, drug names were randomly associated with the patients and diagnosis.
This dashboard example demonstrates how Sigma can process incredibly large datasets and while simultaneously providing flexibility in analyzing the data to address a diverse range of inquiries.
The data was uploaded to Snowflake using materialization with Sigma’s datasets.
This dashboard is intended primarily for individuals in the medical field who want to identify individuals that meet a certain criteria. This list could help healthcare providers inform treatment decision, improve medical care. Medical researchers may use cohort analysis to study the underlying causes of diseases and conditions, and to develop new treatments and therapies.
However, government agencies, such as public health agencies, may be interested in cohort analysis to understand the prevalence of certain diseases or conditions within a population, and to develop strategies for prevention and control. Additionally, pharmaceutical companies may use cohort analysis to study the effectiveness of different treatments, and to inform the development of new drugs.
Healthcare patient cohort creation refers to the process of identifying and grouping patients with similar characteristics, such as medical conditions, demographics, or treatment outcomes. This can be done for a variety of purposes, including research, quality improvement, or population health management.
One common approach to cohort creation involves using electronic health records (EHRs) or other data sources to identify and select patients who meet certain criteria. This can be done manually, by a healthcare provider or researcher, or using algorithms or other automated approaches.
Cohorts can be created based on a wide range of characteristics, such as age, sex, diagnosis, treatment, or other factors. Once a cohort has been created, it can be used to study the health outcomes, patterns of care, or other aspects of the patient population.
Overall, the goal of healthcare patient cohort creation is to improve our understanding of disease, treatment effectiveness, and other factors that impact patient health, so that we can provide better care to patients in the future.
To find a list of patients, use the filters listed on each page to filter down to individuals meeting a certain criteria such as race or gender with a specific diagnosis code. Users will see a high level snapshot of the top 10 most common patient differentiator, the selector above this graph let’s individual choose which characteristics or experiences are important to them.
Medical data is one of the most complex and regulated types of data used in the analytics world. Difficulties can include the frequency of data collection, storage and performance implications to access it, and privacy concerns.
However, analyzing medical data is also one of the most meaningful ways technology can help improve people’s day to day lives. Its is important to not disregard the potential findings due to the difficult to find them. This dashboard demonstrates how easy analyzing medical data can be with the right team and tools.
If you’re interested in working with the right team and tools reach out to phData today!
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