How to Leverage EHR Data Analytics for Accurate Clinical Decision-Making?

In the healthcare industry, every decision can impact a patient’s life. To ensure correct clinical decisions, healthcare providers rely on ePHI (Protected Health Information) or healthcare data that encompasses patients’ medical history, medications, surgeries, allergies, lab results, etc. Healthcare data is analyzed using tools and techniques such as artificial intelligence to extract vital information that helps providers in clinical decision-making. As per Harvard Business Review, data analytics in healthcare is used to detect anomalies in scans, predict outbreaks, and evaluate practitioners.

EHR (Electronic Health Information) which is a source of healthcare data is extensively used for analysis. EHR data analytics is conducted to discover patterns and improve medical health. It also helps in preventive care, accelerates treatment, and assists in identifying high-risk patients.

Data Analytics Techniques for EHR

1. Sentiment Analysis

The sentiment analysis technique belongs to text analytics (the process of segregating and understanding textual data). Sentiment analysis makes use of NLP (Natural Language Processing) to interpret and classify emotions within EHR data. It allows healthcare practitioners to comprehend what their patients are going through during the diagnosis and treatment process.

Sentiment analysis identifies emotions within the data such as happiness, irritation, excitement, frustration, anger, sadness, etc., and segregates them into either positive or negative emotions. Sentiment analysis is used during patient-doctor communication, mental health diagnosis, cancer therapies, and any other diseases that affect human emotions.

2. Monte Carlo Simulation

As per AWS, Monte Carlo simulation is a mathematical technique that anticipates outcomes of an uncertain event. This technique is used by businesses, financial corporations, and even healthcare to analyze past data and predict possible outcomes based on choices or actions.

For example, with the help of Monte Carlo simulation healthcare software development companies can decide whether it is feasible to expand their budget to hire five more backend developers. They can use the technique on uncertain factors such as retention, acquisition cost, training expenses, and medicals to take the final call. From the context of EHR data analytics, this technique can be used to predict the healthcare outcomes of a population, initiate healthcare interventions, and provide adequate treatment.

3. Cohort Analysis

Cohort analysis is popularly used to study the human population. It is a part of longitudinal studies in which participants are observed over a time span. To kickstart cohort analysis, researchers first gather participants’ data to get a detailed picture of them, then they start observing them.

Cohort analysis can be used in EHR data analytics to identify causes of diseases and risk factors associated with them. For instance, a prospective cohort analysis can be to understand the impact of zero fitness activities on mental health. A cohort study allows healthcare providers to keep track of their patients and observe changes in their medical state based on drugs and care routines offered to them.

4. Regression Analysis

Regression analysis is a statistical technique that displays the relationship between two or more variables. It shows the relationship between independent and dependent variables. Regression analysis is used to understand how a dependent variable is affected by independent variables.

In EHR data analytics, this technique can contribute to the study of cardiovascular diseases against variables like BMI, cholesterol, genetics, stress, obesity, and gender. Data analysts can note how each of these variables impacts cardiovascular diseases and what’s the intensity of their impact.

5. Time Series Analysis

The time series analysis technique is used to identify cycles and trends over time. It measures the same variables at different points in time. Electronic health records are leveraged to predict disease trajectories through time series analysis. The progress of chronic diseases such as cancer, cardiovascular diseases, and diabetes are studied with the help of time series analysis.

Process of EHR Data Analytics

  • Define the Objective of the Analysis: The first step for an EHR data analyst is to define the objective of the analysis or the problem statement. For example, to improve population health, chronic disease management, preventive care, or to ameliorate patient outcomes.
  • Data Collection: After defining the objective of EHR data analytics, it’s time to gather relevant data. For instance, in the case of preventive care, the type of data required could be genetic reports, lab tests, vaccinations, medical history, and family medical history.
  • Data Cleaning: The data cleaning process takes up a majority of the time because it removes duplicate files, document errors, and unwanted data points. It also fixes typos, and layout issues, and sends alerts if any data is missing.
  • Data Analysis: Once data cleaning is finished, it is taken up for analysis. Several analytics techniques have been discussed in this article. During this process, the best possible technique is utilized.
  • Data Visualization: After EHR data analytics, insights are converted into a readable format which includes graphs, charts, and tables. Healthcare providers analyze these visuals before taking a final call on treatment and care.

Use Cases of EHR Data Analytics

1. Preventive Care

EHR data analytics is used to prevent the progress of chronic diseases. A large section of electronic healthcare records is monitored to avoid the onset of diseases such as diabetes, hypertension, brain disorders, etc. Data analytics techniques are used to identify high-risk patients and intervene to prevent disease progression and occurrence.

Artificial intelligence technology is integrated into EHR to categorize, analyze, and gather surplus data that is generated during or after chronic disease treatment. EHR data is usually available in an unstructured format, which is then converted to structured data using text analytics for health. Furthermore, EHR data analytics can help to increase value-based care that aims to augment population health, and patient experience, and address social determinants of health that usually impact an individual’s health.

2. Clinical Workflow Management

For smooth clinical workflow, it is necessary to automate the process of hospital admissions, discharge, and appointment booking. Without automation, patients may have to wait for long hours to book an appointment or to go home, which results in frustration. EHR is used to automate the process and capture patient data during admittance and appointment booking.

From the context of clinical workflow, EHR analyzes patient data and generates alerts if any data is missing and if any patient is admitted more than three times in 90 days. Providers take note of these alerts and manage their workflow to dispense home care and fill out missing details.

3. Improve Healthcare Outcome

EHR data analytics helps to improve the treatment and diagnosis of high-risk patients, thereby improving healthcare outcomes. Data analytics techniques examine the data of high-risk patients such as cancer or brain defects and determine which medicines can work best for them. EHR data analytics helps providers and researchers to evaluate the efficiency of drugs on diseases and offer personalized treatment plans.

Data analytics techniques like regression analysis and Monte Carlo simulation are used to understand aspects that influence disease progression, so doctors can initiate measures to improve outcomes and care—for example, identifying environmental elements that give rise to certain diseases such as lung disorders.

From this article, EHR data analytics can produce lucrative opportunities for healthcare practitioners to ameliorate treatment strategies, care routines, and patient outcomes. EHR software is loaded with multiple features and functionalities that are not only user-friendly but also convenient for remote care. This software can be a game changer for clinics and hospitals as it builds seamless clinical workflows and ensures secured payment gateways as well.

If you’re looking for an EHR system that offers you exceptional features and compliance, then connect with Arkenea, a leading healthcare software development company that builds and delivers world-class healthcare software.