Medical Data: Sources and Examples

Introduction

Medical data is any data related to medical conditions, reproductive outcomes, causes of mortality, and the quality of life for a person or a community. Medical data is derived from the clinical parameters and environmental, socioeconomic, and behavioral information related to health. Health data is broadly classified as either structured or unstructured. Structured medical data is standardized data that the various entities in the health care system can easily share between the various entities in the health care systems. Examples include the name of a patient, date of birth, and laboratory results. Unstructured medical data is not standardized, for example, doctor’s notes about a patient, audio records, and emails (Beam, 2018). This paper seeks to elucidate the various medical data sources, their examples and the relevance of these data to the healthcare system of every country.

Survey Data

Surveys help collect health data from a sample of people to understand a larger population. In addition, leading questions are discouraged for they prompt a specific desired answer. The national health interview survey provides data on how people utilize healthcare facilities, insurance, and access to care (Beam, 2018). The national health and nutrition examination survey provide data based on personal interviews, physical evaluations of patients, and laboratory tests. The survey provides data on disease conditions such as diabetes and hypertension.

Example Of Survey Medical Data

The national ambulatory medical care survey is a provider survey that involves doctors. It aims to provide data on patient’s demographics, diagnoses, and how medical practitioners use electronic medical records.

Administrative and Medical Records

Administrative and medical records provide another vital source of medical data. Medical records trace events and transactions between patients and healthcare workers. With the advent of technology, tracking medical records has become easier with the development of electronic health records. Researchers and governments can then use the data from these electronic interfaces. The advantage of using the administrative medical data as a source of medical data is that they are reliable and are both accurate and detailed since they are produced by the healthcare workers. The data also contains additional information that patients might forget to add or feel uncomfortable sharing through other sources, such as surveys (Beam, 2019). The main challenge facing this source of medical data is since the information is in a specific format, it can be easily misinterpreted if taken out of context.

Example Of Administrative Data

An example of these data include; critical diagnoses data, procedures performed, and the laboratory test information. In addition, the trends in health care, characteristics of patients, and the quality of care provided are also key aspects of administrative data.

Claims Data

Claims data is an electronic record that provides more meaningful information on doctors’ appointments, bills, and insurance. They are a massive driving force in the improvement of population health. It helps address issues on cost, quality, and outcome of healthcare. It complements the electronic health records system by illustrating a more comprehensive view of the patient’s interactions across the spectrum of the health care system (Kruse, 2018). It increases the diversity of samples collected, reducing selection bias. It can be challenging to assess the quality of the data provided and account for missing or incomplete data when using claims data as a source for medical data. In addition, data integration from multiple avenues is also a drawback related to these data sources.

Examples of Claims Data

Claims data includes information regarding diagnoses, treatments, and cost of services at the patient encounter.

Vital Medical Data

Vital records are collected and maintained by the government and provide detailed information about rare disorders that culminate in death. The top ten leading causes of death in the United States of America in 2019 include; heart diseases, malignant neoplasms, accidents, chronic lower respiratory diseases, cerebrovascular accidents, Alzheimer’s disease and diabetes mellitus. The information collected may be inconsistent owing to the many state and local governments involved. The vital records only provide information regarding diseases and illnesses that lead to death (Huang, 2018). Therefore, vital records are essential sources of medical data as they provide the authorities with information needed to mitigate fatal illnesses and conditions.

Examples of Vital Data

These data include births, deaths, marriages, divorces, and fetal deaths.

Surveillance Data

Surveillance is a continuous, systematic collection, analysis, and interpretation of data with the timely transfer of these data to the personnel responsible for controlling disease and injury. The method is preferentially used in the study of infectious diseases (Beer, 2019). The National Notifiable Diseases Surveillance system (NNDSS) is an automated program containing databases that track and monitor outbreaks of certain diseases, such as HIV. Such systems work through the local and state health departments alongside healthcare providers who are required by the law to report these outbreaks. The National Center for Emerging Zoonotic Infectious Diseases (NCEZID) tracks emerging zoonotic infectious diseases (Van Mourik, 2021). Therefore, surveillance enables the local and state governments to detect individual cases, control outbreaks, and implement preventive and interventional strategies.

Example of Surveillance Data

The Morbidity and Mortality Weekly Report (MMWR) is an example of such medical data since it provides weekly and annual data from fifty-seven local, state, and territorial governing bodies.

Disease Registry

Diseases registries enable researchers to collect, store, retrieve, analyze and share information concerning patients with a specific disease or condition. They provide researchers with a glimpse of how significant a health problem is, the incidence of the disease, the trends of the disease over time, and the effects of specific environmental exposures. The data collected from registries help improve the quality of care and compare the various treatment options. Registries are maintained by governments and organizations such as hospitals and universities (Kruse, 2018). The clear flow of data from various points of patient care ensures proper tracking and understanding of rare diseases.

Examples of Registries

They include information extracted from hospital records and laboratory reports.

Census Data

According to the United Nations, census is the total process of collecting, compiling and publishing demographic, economic and social data at a specified time to all persons in a country or a delimited territory. The data is used for planning the healthcare system and family welfare services, elucidating the growth rate of a population, and evaluating indices such as birth, death, and morbidity rates (Riihimaa, 2020). For example, the census held in the United States in the year 2021 indicated an annual growth rate of about 1.24% (Van Mourik, 2021). The government can use these figures to plan and implement various public health projects.

Examples of Census Data

The examples of census data include essential information on age, sex, marital status, language, education, occupation, number of children born alive to a woman, and other demographic and social data.

Sample Registration System Data

The sample registration system provides essential information concerning urban and rural areas’ birth rates, death rates, and infant mortality rates. India has made tremendous progress in vital statistics achieving an improvement in the range of demographic parameters. Biobanks collect and store samples such as tissues and blood alongside the health data of the donors. (Paivi, 2019). Biobanks provide a future for research and development and the data can provide healthcare workers with more targeted, preventive, and personalized solutions for healthcare.

Examples of Sample Registration System Data

Examples of such data include use of digital and mobile devices such as pedometers, glucometers, sleep quality, and heart rate measuring instruments by the patients.

Record Linkage

Record linkage refers to bringing together information related to an individual or family from different data sources. It helps follow the chronological sequences of health events of these individuals. It is essential in the study of disease association.

Examples of Record Linkage

Examples of events commonly recorded include birth, marriage, death, hospital admissions and discharge, and medical and surgical procedures.

Environmental Health Data

It is a discreet method of data collection which entails gathering medical data from the environment and studying its effect on the health of an individual or a community.

Examples of Environmental Health Data

Environmental cues such as industrial toxicants, harmful food additives, and inadequate waste disposal mechanisms provide a great medical data source.

Conclusion

In conclusion, medical data can be derived from various sources and they provide vital information for improving patient care by providing the governments and relevant stakeholders with the necessary parameters. The different sources of medical data vary in the accuracy and validity of the results. Still, with careful analysis and scrutiny of the data, the government can use the information to improve the healthcare system.

References

Beam, A. L., Kompa, B., Fried, I., Palmer, N. P., Shi, X., Cai, T., & Kohane, I. S. (2018). Clinical concept embeddings learned from massive sources of medical data. Journal Of Medical Engineering

Beam, A. L., Kompa, B., Schmaltz, A., Fried, I., Weber, G., Palmer, N.,… & Kohane, I. S. (2019). Clinical concept embeddings learned from massive sources of multimodal medical data. In Pacific Symposium on Biocomputing 2020 (Pp. 295-306).

Beer, L., Johnson, C. H., Fagan, J. L., Frazier, E. L., Nyaku, M., Craw, J. A.,… & Shouse, R. L. (2019). A national behavioral and clinical surveillance system of adults with diagnosed HIV (The Medical Monitoring Project): protocol for an annual cross-sectional interview and medical record abstraction survey. Journal of research protocols, 8(11), e15453.

Huang, M., Han, H., Wang, H., Li, L., Zhang, Y., & Bhatti, U. A. (2018). A clinical decision support framework for heterogeneous data sources. IEEE journal of biomedical and health informatics, 22(6), 1824-1833.

Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018). The use of electronic health records to support population health: a systematic review of the literature. Journal of medical systems, 42(11), 1-16.

Riihimaa, P. (2020). Impact of machine learning and feature selection on type 2 diabetes risk prediction. Journal of Medical and Artificial Intelligence, 3, 10.

Van Mourik, M. S., Van Rooden, S. M., Abbas, M., Aspevall, O., Astagneau, P., Bonten, M. J.,… & Gastmeier, P. (2021). PRAISE: providing a roadmap for automated infection surveillance in Europe. Clinical Microbiology and Infection, 27, S3-S19.

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