Healthcare IT Decoded - Data Analytics

Understand the admissions flow, including registration versus admission and the clinical data collected, then explore financial counseling at the front desk for ballpark costs and NGO referrals.

Track the outpatient journey in the OPD from appointment and registration to nurse intake, vitals, diagnosis, and related tests and prescriptions.

Compare the inpatient department to the OPD, detailing ICU monitoring, hourly vitals and SpO2, arterial blood gas tests, NIV or ventilator decisions, and discharge summaries highlighting Indian versus US practices.

Learn how medical transcription turns physician notes into digital records and how medical coding with ICD diagnosis and procedure codes standardizes billing and drives revenue cycle management.

Identify and map source systems—from emr platforms like meditech, epic, cerner to lab, radiology, and billing—using hl7 concepts and dicom data to guide data provenance before visualization in healthcare analytics.

Identify source systems and define the data extraction approach for health care analytics. Examine standards such as HL7, FHIR, CCD, Dicom, and HIPAA, with an HL7 example of structured data.

Explore healthcare data by analyzing insights and foresights using charts in Tableau or Power BI, and extend with forecasting, prediction, and text mining in R and Python.

Review the patient journey and the end-to-end data analytics process, from identifying source systems to extracting, cleaning, standardizing, transforming, and loading data, then building data models and generating insights.

Turn raw data from source systems into actionable insights using descriptive, diagnostic, predictive, and prescriptive analytics, illustrated by the lion and fox and Gartner's ascendancy model.

Explore diagnostic analytics to uncover why patient appointments are cancelled and why satisfaction scores lag, using appointment examples to identify root causes and improvement opportunities.

Explore descriptive, diagnostic, predictive, and prescriptive analytics in radiology and medical coding, including image volumes by modality, turnaround times, top codes, and forecasted wait times.

Explore artificial intelligence, machine learning, and deep learning, featuring examples from healthcare analytics like forecasting appointment volumes with ARIMA and deploying chatbots powered by neural networks.

Explore unsupervised learning by clustering data without labels, grouping images by observable features like citrus fruits or vitamins, and by basic user info such as age and gender.

This lecture demonstrates simple linear regression in Excel, using age to predict insurance charges, including scatter plots, trend lines, and interpreting the regression equation and R-squared.

Apply simple linear regression using method 3 by analyzing age versus average charges, creating a pivot chart, copying values, and plotting a scatter plot with a trend line.

Calculate predicted charges using simple linear regression method 3 with the formula y = mx + c, applying it to x values to obtain y hat as predicted charges.

Discover how multiple linear regression uses all available variables—age, gender, BMI, children, smoker, and region—to predict charges, and learn about setting up dummy variables.

Turn categorical data into dummy variables to predict charges from age, BMI, children, gender, smoker, and region by encoding gender, smoker, and region with dummy columns and dropping one column.

Apply dummy variables to enable multiple linear regression in Excel by creating gender and smoker columns, using if formulas with absolute references, and extending them down the data.

Add a smoker dummy variable to a multiple linear regression using yes/no categories. Copy formulas from the gender dummy setup and extend the approach to the region variable.

Compare models using Akaike information criteria (AIC), corrected AIC for small samples, and Bayesian information criterion (BIC), where lower values indicate better fit in multiple linear regression.

Learn to interpret the intercept in the coefficient table, including the standard error, t stat, p value, confidence interval, and multicollinearity via VIF, and estimate the prediction range.

Explore multicollinearity in a house price model. Analyze how bedrooms and square footage affect price using variance inflation factor (VIF), coefficients, p-values, and confidence intervals to enable predictions.

Identify statistically significant variables by examining p-values in the coefficient table, highlight results with formatting, and discuss using the model for prediction, similar to y equals mx plus c.

Perform a second iteration of multiple linear regression, remove non-significant variables such as southwest and southeast, and compare the R square to confirm region lacks statistical significance in predicting charges.

Explore homoskedasticity using a housing area vs heating cost data set in Excel, creating scatter plots with a trend line, and analyzing residuals against predicted values for constant variance.

Learn weighted linear regression and how to assign weights, including age-based domain weights and inverse-residual weights in insurance data.

Develop logistic regression intuition through a personal yoga story, framing a binary outcome: weight loss, and probability for healthcare IT data analytics.

Explore the confusion matrix in logistic regression, analyzing its four quadrants—true positives, false negatives, false positives, true negatives—and compute metrics such as true positive rate, false positive rate, and accuracy.

Identify missing values in descriptive statistics for fractal dimension and compactness using conditional formatting, then impute with mean (0.124597 for compactness, 0.06481 for fractal dimension) instead of deleting records.

Apply logistic regression to clean data by making the diagnostic result the last column, then review the ROC, confusion matrix, accuracy, and the intercept, coefficients, and p-values.

Explore nominal versus ordinal data using a restaurant seating and rating example, highlighting no inherent order for seating and an ordered scale for ratings, with multinomial and ordinal regression applications.

Explore multinomial regression on asthma data to predict medication categories (none, inhaler, controller/med) from age, gender, smoking status, asthma diagnosis, and peak flow, after data cleaning.

Prepare a health data set in excel by removing patient id, creating an interim sheet, and encoding gender, asthma diagnosis, and smoking status as dummy variables for multinomial logistic regression.

Finalize the columns by deleting unnecessary ones, converting categories to binary values, and preparing the final data sheet. Then apply multinomial regression on the cleaned data.

Apply multinomial logistic regression in the real statistics add-in to analyze cleaned data from age to medication, configure input ranges and independent variables, and interpret the output for prediction.

Apply multinomial logistic regression to predict category outputs from input data, using the mlogit function and variables like age and peak flow, and interpret the predicted values.

Apply multinomial logistic regression to tumor data using the real statistics add-in, examine multiple coefficient sets for four outcomes, and assess model fit with r-squared and significance tests.

Clean the data by keeping only numeric columns, removing the text values column, and deleting features like gender and obesity category, then apply ordinal regression to the city target.

Apply ordinal regression to a prepared data set, select input columns and the target, and compare it with multinomial regression using 1–4 versus 0–3 values, reviewing coefficients and convergence.

Calculate BMI from height and weight by converting height from centimeters to meters and dividing weight in kilograms by the squared height, then reapply ordinal regression.

Create a four-category confusion matrix for ordinal regression data by computing the max value, deriving predicted and actual categories with xlookup, and using a pivot table to count correctness.