CMS Hospital Quality Data: Star Ratings, HCAHPS Scores, and Complication Rates via API

The Centers for Medicare and Medicaid Services publishes quality data for every Medicare-certified hospital in the United States. Our CMS Hospital Quality actor pulls that data into structured JSON: overall star ratings, patient survey scores, complication rates, and readmission statistics for 4,500+ hospitals, filterable by state or city.

What you get per hospital

• facility_name — hospital name
• facility_id — CMS certification number
• city and state — location
• overall_rating — 1 to 5 stars (overall quality rating)
• hcahps_score — patient experience summary score
• hcahps_percentile — national percentile for patient satisfaction
• complication_rate — serious complication rate per 1,000 patients
• readmission_rate — 30-day unplanned readmission rate
• mortality_rate — 30-day mortality rate for key conditions
• patient_volume — estimated annual patient volume where available
• scraped_at — ISO timestamp

Python example

import requests, time

run = requests.post(
    "https://api.apify.com/v2/acts/themineworks~cms-hospital-quality/runs",
    headers={"Authorization": f"Bearer {APIFY_TOKEN}"},
    json={
        "state": "CA",
        "maxResults": 200
    }
)

run_id = run.json()["data"]["id"]

while True:
    status = requests.get(
        f"https://api.apify.com/v2/actor-runs/{run_id}",
        headers={"Authorization": f"Bearer {APIFY_TOKEN}"}
    ).json()["data"]["status"]
    if status in ("SUCCEEDED", "FAILED"):
        break
    time.sleep(5)

hospitals = requests.get(
    f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items",
    headers={"Authorization": f"Bearer {APIFY_TOKEN}"}
).json()

for h in hospitals:
    print(h["facility_name"], h["city"], f"★{h['overall_rating']}")

Rank hospitals by patient satisfaction

import pandas as pd

df = pd.DataFrame(hospitals)
df = df[df["overall_rating"].notna()]
df["overall_rating"] = df["overall_rating"].astype(float)

top = df.nlargest(20, "hcahps_score")[
    ["facility_name", "city", "overall_rating", "hcahps_score", "hcahps_percentile"]
]
print(top.to_string())

Complication rate comparison

# Identify hospitals with both high ratings and low complication rates
df["complication_rate"] = pd.to_numeric(df["complication_rate"], errors="coerce")

high_quality = df[
    (df["overall_rating"] >= 4) &
    (df["complication_rate"] < df["complication_rate"].median())
].sort_values("hcahps_score", ascending=False)

print(f"{len(high_quality)} hospitals with 4+ stars and below-median complication rates")

Parameters

Parameter	Default	Notes
state	—	Two-letter state code (e.g. "TX")
city	—	City name filter
minRating	—	Minimum overall star rating (1-5)
maxResults	500	Maximum hospitals to return

Leave all filters empty to pull the full national dataset.

Use cases

Healthcare market research. Map hospital quality distribution across a metro area or state. Identify where top-performing and underperforming facilities cluster.

Site selection for health systems. Before acquiring or partnering with a facility, pull its quality trajectory over multiple years to assess operational risk.

Healthcare IT sales. Target hospitals with low HCAHPS scores or high readmission rates for solutions that address patient satisfaction or care coordination.

Insurance network design. Build a quality-weighted network by selecting hospitals above a rating threshold within each geography.

Journalism and public reporting. Create interactive hospital quality comparisons for consumer-facing publications without manual CMS portal exports.

Pricing

$0.003 per hospital. Empty queries are not charged.

Available on Apify.