Decoding the Costs of Care: A Dive at Sutter Health's Hospital Prices
How far can I get analyzing publicly available data by myself?
Introduced in 2021, the Hospital Price Transparency Rule was designed to peel back the layers of healthcare pricing, offering patients a clearer view of medical costs. But has it delivered on its promise of greater transparency? To explore this, I dove into a hospital file from Sutter Health, a well-known healthcare provider in Northern California, which is also part of my local community.
Understanding the Data
Analyzing Sutter Health's hospital file was no simple task. It demanded a combination of data analysis skills and specialized knowledge in healthcare billing to make sense of the information. With over 45 columns, the file spans a range of negotiated rates from commercial and government to what patients pay out of pocket.
Breaking Down the Data Columns
Each column provides a glimpse into different aspects of healthcare pricing. These distinctions are key to understanding the financials of healthcare services.
Commercial Rates: The negotiated prices between the hospital and private insurance companies.
Medicare Rate: The price sets by the government.
Gross Charge: The total charge before any negotiations or discounts.
Discounted Cash Price: The price for individuals paying out of pocket without insurance.
Organizing the Data
Upon initial inspection, I encountered several formatting challenges. Here's how I tackled the data cleaning process:
Identified the Correct Header Row: Located the starting row where the actual data columns began. This step was crucial to accurately interpret the data, as the correct headers provide context to the values in each column.
Renamed Columns for Clarity: Revised column names to be more readable. For example, a column originally labeled 'Proc Code' was renamed to 'Procedure Code,' clarifying its content.
Converted Data Types: Changed columns into their appropriate data formats. Price-related columns, initially read as text, were converted into numerical formats. This conversion was key for conducting quantitative analysis, such as comparing costs or calculating averages.
Focusing on Cataract Surgery Costs
Sutter Health's dataset includes over 6,000 unique billing codes, each linked to a specific medical service. These codes are more than just identifiers; they are essential in determining how much each medical service will cost.
Cataract surgery, represented by the billing code CPT 66984, stands out in the dataset for a personal reason. It's not only one of the most common surgical procedures in the U.S., but also one that a family member recently had.
Here's a snapshot of the raw data:
Analyzing the Data
I honed in on two key areas to conduct a more focused analysis:
Commercial Insurer Price Comparisons: I compared the average costs of cataract surgery billed through different private insurers. Originally, I thought comparing the differences in costs would give insight into the negotiation power of different insurance companies. However, one observation emerged from this analysis—there was surprisingly little variance in the costs.
Commercial vs. Medicare Price Analysis: The data analysis revealed that Medicare plans offered the cataract surgery at the lowest cost ($2,932). Commercial rates are almost twice as much in cost, no matter which private insurance the patient has. This provides us with a baseline understanding of how much the procedure could cost ($2,932) and how much private insurance can mark these services up.
Python code used for this analysis can be found in my GitHub repository.
Validating my Findings
I tried comparing my findings against Sutter’s Cost Estimator Tool but was met with error issues.
Source: Sutter Health
Conclusion
Exploring Sutter Health's hospital data was like navigating a maze – it showed us how complicated and confusing healthcare pricing can be. This journey through world of medical billing highlights an important issue: the need for clarity and simplicity. Understanding healthcare costs should be straightforward for everyone. Clear information is crucial not only for helping patients make informed decisions about their health but also for guiding those who make healthcare policies.