Digital biomarker data is deepening the knowledge of a wide spectrum of diseases. The data is useful in preventive epidemiology as it revolutionizes the way scientists assess the link between an individual and the environment. Also, it influences the determination of effective treatment options.

Patients can be monitored remotely, which is a more pragmatic solution since it allows practitioners to intervene continuously. This approach represents an improvement by enabling physicians to abandon episodic intervention. In turn, patients benefit from personalized care thanks to the expanded intervention opportunities.

On another level, artificial intelligence (AI) is enhancing the creation of digital biomarkers. AI is bolstering the process due to its capacity to perform predictions with large quantities of complex datasets. Healthcare professionals are finding it easier to leverage the technology due to the widespread use of smartphones.

As such, smartphones enable the compilation of digital biomarker data across a broad spectrum of people. The advancement of biomarker methodologies is now more pronounced than ever before. 

Machine learning eliminates ambiguity 

Machine learning plays a critical role when it comes to dealing with the ambiguity of specific variables. It resolves the nature and extent of the prediction of a given set of variables. In the end, it becomes easier to determine the outcome of interest. Ambiguity is common in behavioral interventions.

Predicting an outcome based on the behavior of humans is difficult due to the complex interaction between various features. The anthropogenic aspects pertain to the living environment as well as genetic and epigenetic factors that influence behavior. The same applies to social influences, neurobiology, and, to some extent, chance events. 

Medical practitioners can find it difficult to discern the interplay of variables in an individual. For this reason, clinicians cannot predict an individual’s potential for therapeutic response based on behavioral therapy. Thankfully, digital biomarker data minimizes ambiguity by forecasting the future health status of the individual. The data also predicts the current status of a person. 

Markers of potential future health status contribute to preventive epidemiology by enabling pre-emptive action. Physicians can introduce or withdraw additional medications. In some cases, the patient takes preventive steps by making lifestyle changes. 

Biomarkers data can predict future complications while the patient is undergoing treatment. Tweaking can be done between clinic visits. Preventive epidemiology can also apply to predictions linked to the vulnerability of patients to the novel coronavirus that causes Covid-19.

Preventive interventions

The use of digital biomarker data for preventive epidemiology is vital to the transformation of healthcare practices. Experts are taking advantage of advances in ‘-omic’ technologies to increase the number of biomarkers designed to bolster preventive interventions. Some of the biomarkers are specially designed to deal with cancer and other diseases.

Scientists are working tirelessly to identify and validate the most promising biomarkers. The process is aimed at deploying them at the population level for the early detection of diseases. In a clinical setting, epidemiologic knowledge plays a fundamental role in decision making. It enables healthcare practitioners to maximize positive health outcomes.

Modern approaches to cancer prevention are leveraging technology to identify risk factors. Biomarker data makes it easier to identify many environmental and host risk factors, including somatic and inherited factors. Experts are emphasizing the need to identify the most promising biomarkers based on key differentiating factors.

Some of the factors include disease risk prediction, risk factors, and early detection. The translational process relies heavily on the quantitative analysis of the differentiating factors. 

Empirical medicine to precision healthcare

Empirical medicine entails a strong reliance on matching patterns to confirm a diagnosis. Physicians assess the patient’s laboratory data, medical history, and the results of a physical examination to detect a pattern. As a result, physicians often make decisions based on past experiences with identical symptoms. 

It becomes common that the physicians prescribe a blockbuster for certain patients suffering from a specific ailment. As such, healthcare practitioners make treatment decisions guided by trial and error. Unfortunately, this paradigm may expose patients to unpredictable side effects. In some cases, the drugs may prove ineffective.

On the upside, the increased use of biomarkers and companion diagnostics is enhancing precision as healthcare systems migrate from empirical to precision medicine. Patients can now benefit from high accuracy levels when it comes to the choice of medication, dosages, and timing. 

In the end, healthcare practitioners will find it easier to abandon the one size fits all approach. On the other hand, preventive epidemiology will become more effective in combating the spread of diseases, including unprecedented pandemics like the Covid-19. 

Patients respond differently to specific medications due to factors, such as ethnicity, genetic predisposition, and heterogeneity of the cohorts. Biomarker data change the way clinicians handle patients by making it easier to make decisions accurately and quickly.

Biomarker data and decision making

Biomarker data provides a practical way to enhance patient stratification. Additionally, health workers can develop targeted therapies capable of ensuring positive patient outcomes. On another level, the data facilitates the decision-making process in relation to the development of new medications. 

The data creates opportunities for the adoption of a rational approach. Clinicians use the data to gain insights into the biology of a specific disease and the efficacy of medications. Some experts embrace the concept of fail fast, fail early to identify the presence of compounds that undermine the drug development process.

Preventive epidemiology

The prevention of diseases works on a number of levels, including primordial, primary, secondary, and tertiary prevention. Primordial prevention is aimed at combating diseases at a very early stage. It deals with a variety of aspects even before the presence of a risk factor. 

Preventive measures taken at this stage often focus on exposure patterns, lifestyle, and risky behavior as shown with the efforts to combat the new coronavirus. Biomarker data contributes greatly to the effectiveness of the prevention measures by providing useful insights.

Primary prevention focuses on combating exposure to risk factors. For instance, promoting social distancing to curtail the spread of the coronavirus. These strategies are generally targeted at the population or sub-group level. On the other hand, secondary prevention involves the introduction of effective treatment and other interventions. 

Screening plays a critical role in the process to detect the prevalence of the disease in communities. With the Covid-19 pandemic, health authorities screen millions of people to identify affected persons and isolate or quarantine them. These measures prevent the virus from spreading rapidly in the communities.