For the quality category, providers choose six measures from a list that includes 271 measures as of 2018. Examples of measures include Acute Otitis Externa (ear infection): Topical Therapy and Varicose Vein Treatment with Saphenous Ablation: Outcome Survey. All medical specialties are represented so that providers can choose the measures most suitable for them. The providers then collect and submit the data corresponding to the specifications of the measure.
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- Introduction to Healthcare Analytics
- Healthcare Foundations
- Machine Learning Foundations
- Model frameworks for medical decision making
- Tree-like reasoning
- Probabilistic reasoning and Bayes theorem
- Using Bayes theorem for calculating clinical probabilities
- Calculating the baseline MI probability
- 2 x 2 contingency table for chest pain and myocardial infarction
- Interpreting the contingency table and calculating sensitivity and specificity
- Calculating likelihood ratios for chest pain (+ and -)
- Calculating the post-test probability of MI given the presence of chest pain
- Corresponding machine learning algorithm – the Naive Bayes Classifier
- Using Bayes theorem for calculating clinical probabilities
- Criterion tables and the weighted sum approach
- Pattern association and neural networks
- Machine learning pipeline
- Summary
- References and further reading
- Model frameworks for medical decision making
- Computing Foundations – Databases
- Introduction to databases
- Data engineering with SQL – an example case
- Case details – predicting mortality for a cardiology practice
- Starting an SQLite session
- Data engineering, one table at a time with SQL
- Query Set #0 – creating the six tables
- Query Set #0a – creating the PATIENT table
- Query Set #0b – creating the VISIT table
- Query Set #0c – creating the MEDICATIONS table
- Query Set #0d – creating the LABS table
- Query Set #0e – creating the VITALS table
- Query Set #0f – creating the MORT table
- Query Set #0g – displaying our tables
- Query Set #1 – creating the MORT_FINAL table
- Query Set #2 – adding columns to MORT_FINAL
- Query Set #3 – date manipulation – calculating age
- Query Set #4 – binning and aggregating diagnoses
- Query Set #5 – counting medications
- Query Set #6 – binning abnormal lab results
- Query Set #7 – imputing missing variables
- Query Set #8 – adding the target variable
- Query Set #9 – visualizing the MORT_FINAL_2 table
- Query Set #0 – creating the six tables
- Summary
- References and further reading
- Computing Foundations – Introduction to Python
- Variables and types
- Data structures and containers
- Programming in Python – an illustrative example
- Introduction to pandas
- Introduction to scikit-learn
- Additional analytics libraries
- Summary
- Measuring Healthcare Quality
- Introduction to healthcare measures
- US Medicare value-based programs
- The Hospital Value-Based Purchasing (HVBP) program
- The Hospital Readmission Reduction (HRR) program
- The Hospital-Acquired Conditions (HAC) program
- The End-Stage Renal Disease (ESRD) quality incentive program
- The Skilled Nursing Facility Value-Based Program (SNFVBP)
- The Home Health Value-Based Program (HHVBP)
- The Merit-Based Incentive Payment System (MIPS)
- Other value-based programs
- Comparing dialysis facilities using Python
- Comparing hospitals
- Summary
- References
- Making Predictive Models in Healthcare
- Introduction to predictive analytics in healthcare
- Our modeling task – predicting discharge statuses for ED patients
- Obtaining the dataset
- Starting a Jupyter session
- Importing the dataset
- Making the response variable
- Splitting the data into train and test sets
- Preprocessing the predictor variables
- Visit information
- Demographic variables
- Triage variables
- Financial variables
- Vital signs
- Reason-for-visit codes
- Injury codes
- Diagnostic codes
- Medical history
- Tests
- Procedures
- Medication codes
- Provider information
- Disposition information
- Imputed columns
- Identifying variables
- Electronic medical record status columns
- Detailed medication information
- Miscellaneous information
- Final preprocessing steps
- Building the models
- Using the models to make predictions
- Improving our models
- Summary
- References and further reading
- Healthcare Predictive Models – A Review
- The Future – Healthcare and Emerging Technologies
- Other Books You May Enjoy
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