This morning, I asked Chat GPT a question about Medicare Advantage. I noted that many state that Medicare Advantage is cost saving for patients, because its premiums are about the same as Medicare Part B, but you get extra benefits (e.g. vision) and don't have to buy a Medigap plan (about $200/mo).
I suspected that was true, but incomplete. Traditional Medicare A+B, and MediGap, provide nearly "first dollar" coverage all year. (There's a Part B deductible, but it's 'starbucks money" compared to other health costs).
But in Medicare Advantage, you may have office copays ($10-20), specialist copay s($30-40), out of network copays (more, or the whole cost if non-covered). These may reach a $5000 copay cap, adding about $400/month to our total costs of Medicare Advantage (enough to have paid for a Medigap plan). (And any out of network fees might be a different cap.)
###
I asked Chat GPT if this sounded right and to research it, and it "thought" for nine minutes.
I clicked on the "thought for 9 minutes" line, and the result is clipped below.
It's...very, very long.
###
Thinking
I need to gather info on typical copay and coinsurance structures for Medicare Advantage (MA) plans compared to Original Medicare + Medigap. I'll search for details and offer context about copay differences, including premiums, cost-sharing, and coverage examples in HMOs and PPOs. It'll also be good to check ranges for 2025/2026 where possible.
I have KFF's 2025 premiums and out-of-pocket limits, as well as Medicare.gov costs. Now, I need more precise info on typical copays for office visits, like PCP rates. I’ll search specifically for KFF data on these copays to get a detailed breakdown. Let's look up more details!
I still need to find the average copays for MA, like for office visits. Oliver Wyman blog mentions specialist costs of $31 in 2025 and $32 in 2026, but I need more on office visits. Let’s search KFF for "primary care copay" and check if they have cost-sharing details we need.
I
The KFF Spotlight pages have helpful info on premiums and benefits, but I still need cost-sharing details. I’ll search for KFF resources specifically about cost-sharing by service, like for PCP visits. A search like "Medicare Advantage cost sharing by service 2025 KFF physician" might lead to the right data.
I
I found a 2025/2026 zip file with MOOP and cost-sharing limit data. It might include factors beyond typical copays, but it can still help me find statutory max MOOP and max copays for some services. I’ll also consider using plan examples from providers like Kaiser and Blue Shield for typical structures, citing sources like Oliver Wyman as extra context.
I’m thinking to download the PBP benefits data as a zipped CSV file and use Python for computation. The file might be large, but it’s doable. I can fetch the file using a direct URL, but I need to find the right download link from the CMS PBP benefits page and access it properly.
To
I
The
I’ve got the download link for the file, so now I'll click it to fetch the data. It might be a zipped file, but that should be fine for processing once I get it. Let's see how it goes!
The web tool gave a strange internal error but still provided the URL. The error might be due to file size, so I’ll use the container to download the file directly. What a relief! I’ll save it to the /mnt/data directory and move forward from there.
I’ll unzip the file and examine its contents in Python to compute the average copays for primary care and specialist services. First, I need to identify the relevant variables in the dataset. I’ll start by opening the zip listing in Python to see what’s inside.
The zip contains several .txt files (possibly tab-delimited) and SAS scripts. I need to find the section for cost sharing related to primary care visits, which is likely under filenames like "pbp_b15_b19b_physician_services" or something similar. I'll search for filenames containing "phys" to narrow it down.
I didn’t find any filenames with “phys,” so I’ll broaden the search. Let’s look for filenames containing keywords like “office,” “doctor,” or “pcp.” That might lead me to something relevant for cost sharing on primary care visits. We’ll see what comes up from this search.
I’m considering that physician services might be inside a file like “pbp_b07a,” so I’ll list all relevant filenames to check. I’ll probably want to inspect the table of contents too, in case there is a general mapping. I’ll also view the zip file’s namelist to spot unique prefixes and narrow it down.
There’s a "PBP_Benefits_2025_dictionary" and a readme file. I’ll extract the dictionary to see the variables, especially the ones related to physician services. It seems like a good idea to also check the readme, so I'll go through some lines with Python for better clarity.
The readme file is truncated, but it looks like "pbp_b7_health_prof" could include health professional services, possibly covering both PCP and specialist visits. That seems promising! I’ll open and inspect the first few lines of the "pbp_b7_health_prof.txt" file or dictionary to confirm.
The file seems massive, and the first line appears truncated because of tab delimiters. I’ll try reading it with pandas using the tab delimiter ('\t'). Once the data is extracted, I can identify the columns by reviewing the dictionary file to better understand the structure.
I see the base for the dictionary file exists, but I’m unsure of its extension. It could be in formats like .xlsx or .pdf. I'll list the items that start with "PBP_Benefits" to figure out the exact format and proceed with the appropriate method to open it.
The dictionary file is an .xlsx. I’ll use pandas to extract and read the data from it. This should help me access and work with the content more effectively. I’ll proceed by loading it into pandas and looking for what’s relevant to the task.
I need to focus on the health professional services file and identify the variables related to PCP and specialist copays. I'll filter the dictionary dataframe to look for "pbp_b7_health_prof" and check for fields that include "Primary Care." Time to dig into that data now.
I’ll continue scrolling through the data, as I’m looking for specific service-related codes such as office visits. Let's search within the filtered dataset for any mentions of "primary" in the TITLE or FIELD_TITLE columns to pinpoint relevant information for primary care.
I see that I have variables like pbp_b7a_copay_amt_mc_min/max for primary care—likely an office visit. This could involve both PCP and specialist care. There are categories for primary care (b7a) and possibly specialists (b7b). I'll search for "specialist" to narrow down the relevant data.
It seems that "specialist" could also be labeled as "Specialist (including physician specialists)" or similar. To capture all possibilities, I'll search for terms like "specialty" or "special." That should help me find the exact data related to specialist services and copays.
It looks like there’s another specialty term to consider. I'll check this second term and see if it's relevant. This should give me a better idea of how to refine the data concerning specialist services, focusing on all possible variations of the term.
It looks like the mental health services are separate, not specialist visits. Those might be in different sections like "pbp_b7b" or "pbp_b7c." I should check all the variable titles for the b7 sections quickly and look for distinct prefixes like pbp_b7a and pbp_b7b to identify the relevant ones.
It looks like I need to figure out what each b7 section represents. b7a seems to be about primary care, and b7b might be for specialists. I’ll check the titles in b7b by filtering for variable names that start with "pbp_b7b" and look at the unique TITLE values to confirm.
It seems like b7b is related to chiropractic services, so specialist visits might not be included here. I’ll check b7f or perhaps b7c for the specialist-related information. Let’s take a look at the titles in b7c and see what we find.
b7c seems to cover occupational therapy, and b7d might be speech therapy. b7e looks like it's related to mental health. I know b7a is primary care, but I need to find which section covers specialist and possibly mental health/psychiatry. Maybe b7g has the specialist details. Let me quickly check.
Awesome! I’ve identified that b7d corresponds to physician specialist services, so I'll use the pbp_b7d_copay amounts. Now, I need to compute the distribution of copay amounts for both PCPs and specialists across plans. I'll load the file pbp_b7_health_prof.txt using pandas, selecting the relevant columns for b7a and b7d. I’ll determine the exact column names and read the file using pandas read_csv with '\t' as the separator. Let's extract the file and begin reading.
I can compute the primary care copay amounts using pbp_b7a_copay_amt_mc_min/max, where the copay_yn field shows '1' for yes. The values might be numeric but stored as strings, with many entries being '0'. I'll convert them to floats. Now, I need to check the column names for physician specialist data. Let's do that next.
I’ll summarize the distribution of copays, focusing on the percentage with $0 for PCP and typical amounts (median) for those with copays. Many plans show copay max, so we'll use the median of the max values. We'll also compute inpatient, ER, and outpatient surgery costs from relevant datasets. Let's simplify by computing the main categories, mentioning variations, and adding a few other key costs. I'll use the PBP datasets, being mindful they include nationwide plans, including SNPs.
It seems like Section C deals with out-of-network (OON) costs, but it might not include the MOOP data. I believe there’s a separate file for MOOP in Section A. I'll check the README to confirm which section provides MOOP data, then verify by looking at Section A columns quickly.
It looks like Section A might be using Windows encoding, so I'll read the file with encoding='latin1' to ensure the data comes through correctly. This should help avoid any issues with special characters or misinterpretation while processing the file.
I can use the membership estimates from Section A to weight copays based on enrollment, which will give more accurate results. I'll determine unique keys for merging, making sure the plan and segment IDs are consistent. It looks like there are duplicates in the benefit coverage columns, so I'll pick the right one to merge on and filter for MA plans. We can then apply enrollment weights based on the membership data.
It looks like there are only 1582 non-null membership values, probably just for plans where membership was reported. Still, I can use this as an approximation for now. I'll compute the unweighted baseline first and then merge the data from Section A with the weights.
Around 21% of plans have membership weights, so I'll use both weighted and unweighted calculations, mentioning weights where applicable. To compute the PCP copay, I'll focus on the Medicare-covered primary care, considering both copay and coinsurance options. If both exist, I'll prioritize copay for worst-case scenarios and calculate typical amounts, including those with $0 copay. We'll also compute for specialist care similarly.
It seems the weighted shares calculation is off, as almost all plans show coinsurance. That likely happened because many plans don't have weights, and I just filled those missing with 0. I’ll fix this by computing the weighted shares only using the plans with a weight greater than 0.
It looks like most of the enrollment is shown in coinsurance plans, but that seems odd since many MA plans actually have copays, not coinsurance. Could be related to how "Medicare-cost-sharing" is defined, especially for provider services. Some plans, like PPOs, may apply coinsurance for specialists or PCPs, but many HMO plans likely have fixed copays. It's worth noting that 39% of the plans have a $0 copay.
Alright, so the share_copay includes all plans with a copay, but share_copay0 is just those with a $0 copay. It seems like 44.6% have a copay, many of which are $0, and 27.3% have coinsurance. About 28% might have unspecified cost shares (possibly due to "Not covered"). I’ll now compute the distribution of copay amounts, including zeros for PCP and specialist services, and calculate the most common values for both.
Alright, for the inpatient hospital copay, it looks like the typical copay is per day for the first 1-5 days or a per-stay fee. I'll examine the dictionary for the pbp_b1a_inpat_hosp file to identify relevant columns, like the minimum and maximum copay amounts, plus any intervals. Let's check it out and identify key data points.
There are many variables here, including coinsurance intervals, which might complicate things. For our task, we just need the common copay amounts per day for inpatient hospital stays. I'll filter the data for titles containing "Copay" to narrow things down and get the most relevant information.
Okay, for inpatient hospital costs, I want to focus on the typical day 1 interval amounts (e.g., days 1-5). I'll read the file, zero in on the tier 1 "interval1" amount, and consider both the start and end days. Many plans use a daily copay for the first few days, so I’ll compute the distribution accordingly.
I need to find the copay-related columns in file b1a. I’ll locate those columns to focus on the relevant copay data. Once I have them, I'll be able to compute the necessary statistics and distributions for the inpatient hospital copays.
I’ll compute the distribution for tier1's per-day copay (amt_int1_t1) across the first interval, filtering for Part A/B coverage. I’ll focus on plans with copay_yn marked as 1 and a start day of int1 as 1. Then, I’ll analyze the typical end day, likely around day 5. Let’s proceed.
The median end day looks like 6, which suggests many plans charge a per-day copay for days 1-5 or 1-6. I’m seeing frequent entries like "days 1-5 $XXX/day," which supports end day 5 being common. So, I'll focus on showing the frequency of the typical end days.
The common inpatient per-day copay ranges between $250-$395 for the first 4-7 days, which is good info. Next, I need to look up emergency room copay, likely under outpatient hospital. I’ll start by checking for "emerg" in the available file names like pbp_b4_er_urgent or similar.
Awesome! I'll use "pbp_b4_emerg_urgent.txt" for emergency room and urgent care info. Next, I’ll take a look at the file’s dictionary to better understand the structure and available data before proceeding. That way, I can make sure I’m targeting the right details.
It looks like there's emergency care (ER) and urgent care data in what might be the "b4b" file. I'll go ahead and read through the file to see what it contains and how we can compute the necessary data for those categories. Let’s dive in!
It looks like we’ll be working with "b4a" for emergency and "b4b" for urgent care. My plan is to compute distributions, focusing on things like ER copay medians, and more. Alright, time to get started and crunch those numbers!
ER
I have the KFF analysis for MOOP and cost sharing already, which I'll cite. Now, I need to reference the CMS PBP benefits dataset for our computed copay distributions. I'll search for the CMS PBP benefits page and properly cite the source and the download link. Let's make sure to include those citations correctly.
I’ll draft a response comparing Medicare Advantage (MA) copays with Medigap, highlighting typical copay ranges using our PBP dataset medians. I'll explain cost-sharing for Medigap Plan G/F, including details about Plan G’s Part B deductible and Plan F’s pre-2020 eligibility. I'll also cover Part D premiums and the 2025 OOP cap under IRA. The focus will remain on answering the user's question about typical copay structures.
The median copay is $30, with most plans around $35. I'll mention that typical copays fall between $30 and $40. For primary care provider (PCP) visits, I’ll compute the same to identify the typical copay range, keeping it relevant to the user's needs.
For most plans, the copay is $0. But for the subset of plans that include a positive copay, I’ll focus on computing the median. This will help clarify what the typical cost is for those who do have copays, rather than the general $0 copay scenario.
For plans that charge a PCP copay, typical amounts range from $5 to $10. I want to calculate what portion of plans have a copay of $0 (with possible coinsurance instead). Among those plans with a copay, 86.9% set the copay to $0. Now, I’ll look into the share of plans that have coinsurance instead, like 20%, and calculate the median coinsurance percentage.
Many MA plans use a 20% coinsurance for PCP visits, meaning patients pay 20% of the contracted rate rather than a fixed copay. This can be lower than a typical copay, but users usually expect a copay. I'll clarify that while plans often have flat copays, some use coinsurance (like Part B coinsurance), and I’ll also look into specialist coinsurance distributions.
It looks like many plans have a 20% coinsurance, but now I need to focus on inpatient per-day copays. I’ll calculate the median copay and typical range. Then, I’ll work out the distribution based on the amount and the number of days associated with these copays.
I’ll clarify common copays for inpatient stays (about $300-$400/day for the first 4-7 days). Afterward, some plans may charge $0 until the MOOP is reached.
Then, I’ll confirm user’s theory about MA and Medigap cost-sharing, explaining that Medigap plans cover most costs, but premiums and deductibles still apply. I’ll also mention MA’s integrated Part D with its out-of-pocket cap of $2,000 starting 2025. Lastly, I'll address premiums for Medigap and cost-sharing comparisons between the two.
To clarify:
How MA cost-sharing is built: includes copays for services and an annual Maximum Out-of-Pocket (MOOP).
Typical amounts (nationwide): PCP copay is $0 or $5-$10, specialists range $30-$40, urgent care $45, ER $125, and inpatient $250-$395/day for the first few days (2025 data from CMS PBP).
While ranges can vary by age and state, I’ll stick with typical copay details for 2025.
Medigap Plan F/G covers deductibles and coinsurance, offering near-first-dollar coverage. However, there’s a premium versus utilization tradeoff, and no network restrictions.
MA, as managed care, has point-of-service cost-sharing, while Medigap is prepaying premiums for minimal copays. Some Medigap plans like Plan N may have copays, but Plan F/G is nearly first-dollar.
I’ll check Medicare.gov for Plan G coverage citations.
For Medigap Plan G, I’ll cite Medicare.gov’s plan comparison and the CMS Medigap guide for details on the lack of Part B deductible coverage. I’ll also mention that while Part D premiums can be low, MA often includes drug coverage, sometimes with a $0 premium. I’ll use the CMS PBP dataset for cost-sharing info.
We’ll explain the methodology by citing the CMS PBP Benefits file for 2025, highlighting that our dataset includes all plans, including SNPs. However, for better alignment with KFF style, we’ll exclude SNP and non-employer group plans by using the special_need_flag in section A, filtering to only general enrollment plans.
I need to double-check the codes for the pbp_a_special_need_flag. They’re likely coded as 1/2 or Yes/No, but I’ll have to confirm. Let me look into the dictionary and retrieve the relevant row from the df_dict to clarify the exact values used.
It looks like code 1 corresponds to "yes," so I'll assume code 2 means "no." The same likely applies to pbp_a_eghp_yn. I'll need to double-check the values for this as well to be certain. Let's review the dictionary to get the full picture.
It seems like filtering out EGHP "Yes" and SNP "Yes" flags should help approximate non-SNP individual enrollment. I'll compute unweighted statistics based on those flags, excluding special_need_flag == 1 and eghp_yn == 2. B7 has these columns, but they might be missing for some, so I'll check for them.
It seems the missing flags might be due to a mismatch between section A and the merged data. SecA should include flags for all plans, so the issue could stem from missing combinations of hnumber, plan_identifier, segment_id, and ben_cov. I'll check the missing rate for the flags to confirm this.
Now that the flags are available for all plans, I can proceed to recompute the stats by applying the necessary filters. This will help me get the correct breakdown without the missing data influencing the results. Time to run the updated calculations with those exclusions.
The filtering is working as expected. PCP coinsurance is almost nonexistent, and copay is the main cost, which matches the expectation. Specialist copay distribution is unchanged. Now I'll move on to computing inpatient and ER stats with the same filter. For inpatient, I'll merge with secA, and then merge b1a and b4 files.
Alright, now that the filtering looks good, I’ll compute the inpatient interval1 using the filtered set. This step should help keep everything consistent with the previous stats, so I can get a clearer picture of the data within the filtered parameters. Let’s proceed.
Next, I'm focusing on computing the filtered stats for ER and urgent care. This will help ensure everything remains consistent within the parameters we’ve already set. Once done, I’ll have a clearer picture of the data with these specific filters applied. Let’s proceed with this step!
Now
Done