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.

Explore healthcare codes and terminology standards such as ICD, SNOMED, LOINC, RxNorm, NDC, and NPH, and data transmission formats HL7 and FHIR across Epic, Meditech, Cerner.

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.

Explain the back office of revenue cycle management, from entering charges and hold days to billing edits, claim submission via edi 837, and remittance advice (edi 835) to accounts receivable.

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.

Identify your KPIs and build a logical data model before analysis to guide data selection. Distinguish master data from transactional data to shape the database and align stakeholders.

Learn how to load data while building the data model in parallel and use healthcare integration tools like Mirth, Cloverleaf, and Rhapsody to read HL7, EDI, CCD, and FHIR standards.

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 cognitive analytics using real time data from diverse media—text, images, video, and audio—to generate actionable insights within the Gartner Ascendancy model extension.

Explore descriptive analytics through an appointment data example, showing how to count recent appointments and break them down by specialty and gender to reveal patterns.

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 prescriptive analytics and optimization by turning forecasts into recommended actions. Use personalized reminders to reduce no-shows and restructured schedules to reduce wait times.

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.

Explore reinforcement learning with real-world examples like classroom point systems, group competition, and robot training using positive and negative reinforcement.

Explore how eugenics relates to heredity by examining Francis Galton's 19th-century analysis of family height data, illustrating regression to the mean and the beginnings of simple linear regression.

Explore the logic of simple linear regression and the best fit line that minimizes distance to points, using y = mx + c to predict outcomes.

Apply linear regression to insurance charges data to predict patient charges using features such as age, BMI, number of children, smoking, and region, while exploring multiple methods for best results.

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 to compute y hat using y = 257.72 x + 3165.9, where x is age and y is the predicted charges.

Apply method 2 of simple linear regression by plotting age against log of charges on a scatter plot, add a trend line and equation, and compare r-squared to the earlier method.

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.

Apply multiple linear regression in Excel by preparing base data: remove text and categorical columns, copy data, paste values to strip formulas, and duplicate a base sheet for regression analysis.

Apply multiple linear regression using the Excel add-in, selecting input ranges for x and y (charges), including the intercept, and interpreting R-squared results to evaluate model performance.

Learn to interpret Excel regression output for healthcare analytics, focusing on fit metrics such as multiple R, r square, adjusted r square, standard error, and anova results.

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.

Explore the Anova output, including degrees of freedom, sum of squares, mean square, and f statistic, to assess overall model significance and interpret the p value.

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.

Learn to calculate predicted values in multiple linear regression using coefficients and intercept in Excel, applying y = sum Mi Xi, with age, BMI, children, gender, smoker, and region.

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.

Use the Excel add-in to compute descriptive statistics for age, including mean, median, mode, standard deviation, skewness, kurtosis, and normality, then extend to other variables.

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.

Apply Breusch-Pagan test and White's test in Excel to detect heteroskedasticity in regression using sample data. Interpret p-values to assess homoscedasticity or heteroscedasticity and consider weighted linear regression when needed.

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

Check heteroskedasticity in insurance charges by plotting predicted vs residuals, confirm with the Bruce Pagan test, then apply weighted linear regression using age ranges and the residual-inverse method.

Apply prediction in weighted linear regression by copying coefficients, pasting values in the lr one tab, and computing predicted values with sum product to compare outputs.

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

Observe how walking distance data informs logistic regression to predict the probability of weight loss, illustrated by the sigmoid curve and a distance-versus-outcome plot.

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.

Apply logistic regression and descriptive statistics to a prostate cancer data set, performing data cleaning and recoding diagnosis labels in Excel, then interpret data quality.

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.

Impute missing values by diagnostic result using mean values for compactness and fractal dimension, then apply logistic regression to the data.

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 the ROC curve for logistic regression, interpreting true and false positive rates, thresholds from 0 to 1, and the confusion matrix to assess model performance and AUC.

Explore logistic regression outputs, including observed vs predicted values, log likelihood, accuracy, confusion matrix, and the coefficient table with exp b and p values.

Explore logistic regression outputs, including ROC, AUC, chi-square tests, and p-values, to assess model significance on insurance data. Identify compactness and fractal dimension as the two statistically significant features.

Compute y hat with y = m1 x1 + m2 x2 + m3 x3 in Excel, copy and transpose coefficients, then apply the intercept and features like radius and texture.

Calculate the predicted output using the logistic function P = 1/(1+e^{-y}) where y = mx + c, and apply the Excel formula to obtain consistent probabilities, such as 0.9997.

Apply predicted formula in logistic regression by repeating the coefficient-based calculation to compute predicted value for given radius, texture, and perimeter; explore using compactness, fractal dimension, and diagnosis as predictors.

Explore logistic regression iteration 2 using compactness, fractal dimension, and diagnosis result in Excel real statistics, and compare its 81% accuracy to the prior 89%.

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.

Continue cleaning data by encoding gender, asthma, and smoking status into numeric indicators using three binary columns and if statements for multinomial logistic regression.

Learn how to clean data for multinomial logistic regression by recoding the medication column into three categories within the same column, and prepare gender and smoking status for modeling.

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.

Explore multinomial logistic regression outputs, including input data from age to peak flow, three outcome groups, counts, coefficients, and the R square value with chi-square significance, and predict values.

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.

Explore multinomial logistic regression on primary tumor data from UCI to predict the location class across more than three categories, using 339 records and 17 attributes, including age and gender.

Clean and recode the tumor data by converting class values 5,11,18,22 to 0,1,2,3, then prepare for multinomial logistic regression in Excel real statistics.

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.

Explore ordinal regression on obesity data by modeling ordered obesity categories from underweight to obese and predicting linked physical activity levels, using age, gender, height, weight, and BMI as features.

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.

Explore ordinal regression to compute predicted obesity categories from five independent variables, using the logit formula and coefficients, and validate results with a pivot table confusion matrix.

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.

Prepare an ordered physical activity data set and apply ordinal regression to predict activity levels, then compute predicted values and the confusion matrix.