data trove cms hospital database

This column is a fixed-length, zero-padded 6-character alphanumeric facility identifier — likely a government or regulatory facility code (e.g., CMS Facility ID or similar). Every one of the 5,421 rows has a distinct value with zero nulls and zero duplicates, confirming it functions as a primary key. All values are exactly 6 characters long (min, mean, max = 6) and fully uppercase, consistent with a structured code scheme. The top visible values follow a numeric sequence starting with '010001', suggesting geographic or administrative ordering. Treatment: Retain as a primary key for row identification and use as a join key to link facility-level metadata tables. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

5,421

len_min

6

len_max

6

len_mean

6

len_median

6

len_p95

6

word_mean

1

word_median

1

n_empty

0

n_duplicates

0

duplicate_rate

0

vocab_size

5,421

readability_flesch_mean

121.2

emoji_rate

0

url_rate

0

one_word_rate

1

allcaps_rate

1

boilerplate_rate

0

This column contains healthcare facility names — overwhelmingly hospitals and medical centers, as evidenced by 'hospital' appearing 2,740 times and 'medical'/'health'/'center' dominating the top-word list across 5,421 rows. Nearly all values are ALL-CAPS (99.3%), suggesting a standardized administrative data entry convention. The near-unique alert is noteworthy: with 5,286 unique values out of 5,421 rows (duplicate_rate 2.5%, 135 duplicates), some facilities appear more than once, which could indicate multiple records per facility rather than a clean one-row-per-facility structure. Average name length of ~29 characters and ~4 words per name is consistent with formal institutional naming patterns. Treatment: Use as a human-readable label or grouping key; normalize case before display; investigate 135 duplicate entries for potential record linkage issues before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

5,286

len_min

3

len_max

74

len_mean

29.21

len_median

28

len_p95

45

word_mean

3.995

word_median

4

n_empty

0

n_duplicates

135

duplicate_rate

0.0249

vocab_size

3,942

readability_flesch_mean

6.842

emoji_rate

0

url_rate

0

one_word_rate

0.001845

allcaps_rate

0.9932

boilerplate_rate

0

This column contains physical street addresses, confirmed by dominant top words: 'street' (1,036), 'avenue' (580), 'drive' (511), 'road' (507), and directional prefixes like 'north', 'east', 'west', 'south'. Nearly all values are uppercased (99.2% allcaps_rate), consistent with address data sourced from a government registry or standardised mailing system. With 5,387 unique values out of 5,421 rows and only 34 duplicates (0.6% duplicate rate), the column is near-unique — the small number of duplicates may indicate shared addresses such as apartment buildings or data entry issues worth investigating. Treatment: Do not use as a predictive feature directly; parse into structured components (number, street, direction, suffix) or geocode for spatial modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

5,387

len_min

7

len_max

50

len_mean

19.37

len_median

19

len_p95

29

word_mean

3.754

word_median

4

n_empty

0

n_duplicates

34

duplicate_rate

0.006272

vocab_size

4,996

readability_flesch_mean

79.27

emoji_rate

0

url_rate

0

one_word_rate

0

allcaps_rate

0.9921

boilerplate_rate

0

This column contains US city and town names, stored in ALL-CAPS format (99.4% allcaps rate), consistent with a standardized postal or administrative data source. With 3,049 unique values across 5,421 rows, the duplicate rate of 43.8% (2,372 duplicates) is expected for a geographic field — major cities like CHICAGO (34) and HOUSTON (31) naturally repeat. The near-complete one-word rate (77.1%) alongside multi-word entries like 'OKLAHOMA CITY' and 'LOS ANGELES' is normal for city names. No nulls are present, indicating good completeness. Treatment: Standardize casing, then use as a grouping/aggregation key or encode as a high-cardinality categorical feature. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

3,049

len_min

3

len_max

24

len_mean

8.611

len_median

8

len_p95

13

word_mean

1.241

word_median

1

n_empty

0

n_duplicates

2,372

duplicate_rate

0.4376

vocab_size

2,890

readability_flesch_mean

18.29

emoji_rate

0

url_rate

0

one_word_rate

0.7709

allcaps_rate

0.9943

boilerplate_rate

0

This column contains US state abbreviations, covering 56 distinct values (50 states plus likely DC and US territories). The distribution is notably spread — entropy ratio of 0.917 indicates near-uniform coverage — but TX leads with 462 occurrences (8.5% of 5,421 rows), followed by CA (378) and FL (221), reflecting population-weighted representation. The cardinality of 56 slightly exceeding 50 states warrants a quick audit to confirm no invalid or duplicate codes exist. Treatment: One-hot encode or use target encoding depending on model type; verify the 6 non-standard codes (beyond 50 states) are valid territories or DC. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

56

top_value

TX

top_rate

0.08522

cardinality

56

entropy

5.328

entropy_ratio

0.9174

This column contains US postal (ZIP) codes stored as integers rather than strings, covering the full national range from 603 (Puerto Rico/Northeast) to 99929 (Alaska), with 4721 distinct values across 5421 rows. The numeric treatment is problematic: leading zeros are silently dropped (the minimum of 603 almost certainly represents 00603), making string-based joins or lookups unreliable. The near-flat kurtosis (−0.99) and low skew (−0.16) indicate fairly broad geographic spread with no dominant regional cluster, which is notable given the wide IQR of 43333. Treatment: Cast to zero-padded 5-character string before any join, geocoding, or feature engineering to restore lost leading zeros. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

4,721

min

603

max

99,929

mean

5.378e+04

median

55,066

std

2.706e+04

q1

32,771

q3

76,104

iqr

43,333

skew

-0.1646

kurtosis

-0.9879

n_outliers

0

outlier_rate

0

zero_rate

0

This column contains US county (or parish) names, stored entirely in uppercase (allcaps_rate 1.0), consistent with a standardised geographic reference field. With 1,555 unique values across 5,421 rows and a duplicate_rate of 0.7132, the same county names recur frequently — expected given that many records share common counties like LOS ANGELES (88), JEFFERSON (59), and COOK (59). The high one_word_rate (0.873) reflects that most US county names are single tokens, while multi-word entries like 'LOS ANGELES' and 'SAN ...' account for the remainder. Treatment: Encode as a categorical geographic grouping variable; consider joining to a FIPS code lookup for spatial analysis. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

1,555

len_min

3

len_max

25

len_mean

7.34

len_median

7

len_p95

11

word_mean

1.135

word_median

1

n_empty

0

n_duplicates

3,866

duplicate_rate

0.7132

vocab_size

1,591

readability_flesch_mean

34.44

emoji_rate

0

url_rate

0

one_word_rate

0.8733

allcaps_rate

1

boilerplate_rate

0

This column contains North American telephone numbers, all formatted uniformly at exactly 14 characters (consistent with the '(NXX) NXX-XXXX' pattern) with zero nulls across 5,421 records. The allcaps_rate of 1.0 is an artifact of digit/punctuation-only strings rather than alphabetic content. Surprisingly, 38 duplicate phone numbers exist (duplicate_rate ≈ 0.007), meaning distinct records share the same telephone number, which warrants investigation for data quality issues. Top area codes such as (406), (605), and (402) suggest a predominantly rural US geographic distribution. Treatment: Flag 38 duplicate values for deduplication review; treat as a categorical or string identifier — do not embed numerically. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

5,383

len_min

14

len_max

14

len_mean

14

len_median

14

len_p95

14

word_mean

2

word_median

2

n_empty

0

n_duplicates

38

duplicate_rate

0.00701

vocab_size

5,550

readability_flesch_mean

120.2

emoji_rate

0

url_rate

0

one_word_rate

0

allcaps_rate

1

boilerplate_rate

0

This column classifies each hospital record into one of 8 facility-type categories, making it a standard categorical label for hospital segmentation. 'Acute Care Hospitals' dominates at 57.6% of records (3,120 of 5,421), creating notable class imbalance. The extreme tail is striking: 'Long-term' hospitals appear only 4 times, and 'Acute Care - Department of Defense' just 32 times, meaning minority classes may be statistically unreliable for subgroup analysis. There are no nulls and no unexpected values. Treatment: One-hot encode for modelling; be cautious with rare classes ('Long-term' n=4, 'DoD' n=32) which may need grouping or exclusion. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

8

top_value

Acute Care Hospitals

top_rate

0.5755

cardinality

8

entropy

1.654

entropy_ratio

0.5513

This column captures the legal/operational ownership classification of hospitals, with 12 distinct categories across 5,421 records and no nulls. The dominant category is 'Voluntary non-profit - Private' at 42.3% (2,291 records), creating notable imbalance — the top value alone accounts for more than twice the second-largest category ('Proprietary' at 1,067). The entropy ratio of 0.72 indicates moderate but uneven spread across categories, with minority classes like 'Government - Federal' (44) and 'Physician' (74) being quite sparse. Treatment: One-hot encode or target-encode for modelling, but consider grouping sparse categories (e.g., 'Government - Federal' with n=44, 'Physician' with n=74) to avoid instability. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

12

top_value

Voluntary non-profit - Private

top_rate

0.4226

cardinality

12

entropy

2.586

entropy_ratio

0.7215

This column is a binary flag indicating whether emergency services are present or available for a given record. The dominant value is 'Yes' at 83.1% (4505 of 5421 rows), making the distribution notably imbalanced — 'No' accounts for only 916 records (roughly 1-in-6). No nulls exist, and entropy of 0.655 reflects the skew away from a balanced 50/50 split. Treatment: Encode as binary (1/0); note class imbalance (83/17 split) and apply appropriate weighting or stratified sampling if used as a target or predictor. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

Yes

top_rate

0.831

cardinality

2

entropy

0.6553

entropy_ratio

0.6553

This column flags whether a facility meets criteria for a 'birthing friendly' designation, and every non-null value is 'Y' (top_rate = 1.0, cardinality = 1). That means it carries zero discriminative information among observed records — there are no 'N' values at all. More striking, 58.24% of rows are null, leaving only 2,264 usable observations out of 5,421; the nulls likely represent facilities that were not assessed or do not offer birthing services. Treatment: Treat non-null as a binary indicator of assessed-and-qualifying facilities; nulls should be coded as a distinct category (e.g. 'not assessed') rather than imputed, as they almost certainly reflect structural missingness rather than random dropout. high · anthropic:default

n

5,421

nulls

3,157 (58.2%)

unique

1

top_value

Y

top_rate

1

cardinality

1

entropy

0

entropy_ratio

0

This column represents a CMS-style 1–5 star overall hospital rating, stored as a categorical field with 6 distinct values. The most striking finding is that 47.1% of all 5,421 rows carry the value 'Not Available', making missing ratings the single largest 'category'—outnumbering any numeric rating tier. Among hospitals that do have a rating, scores cluster around 3 and 4 (937 and 765 occurrences respectively), with the extremes (1 and 5) being relatively rare. Treatment: Convert numeric strings to ordinal integers; treat 'Not Available' as a distinct missing indicator (do not encode as a numeric level) and consider imputation or a separate missingness flag before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

6

top_value

Not Available

top_rate

0.4708

cardinality

6

entropy

2.133

entropy_ratio

0.8252

This column contains footnote codes attached to hospital overall ratings, where each numeric code references a specific explanatory note (e.g., data suppression reason, insufficient data, or special methodology). The 52.7% null rate is expected since most hospitals with valid ratings require no footnote. However, among non-null rows, value '16' dominates at 65.4% of present values (1,676 occurrences), suggesting a single suppression or caveat reason is overwhelmingly common; the presence of a compound value '16, 23' indicates some records carry multiple footnote codes, which could complicate downstream parsing. Treatment: Treat nulls as 'no footnote'; split multi-code entries on comma and one-hot encode each footnote code separately before modelling. high · anthropic:default

n

5,421

nulls

2,857 (52.7%)

unique

7

top_value

16

top_rate

0.6537

cardinality

7

entropy

1.158

entropy_ratio

0.4126

This column records the count of MORT (mortality) group measures evaluated per entity, with only two distinct values across 5,421 rows: '7' (dominant, 84.1%) and 'Not Available' (15.9%). The cardinality of 2 and the presence of 'Not Available' as the only alternative to '7' suggests this is effectively a binary flag — entities either have a full complement of 7 mortality measures or are excluded from measurement entirely. The 'Not Available' string rather than a true null (null_rate is 0.0) means missingness is encoded as a sentinel value and must be handled explicitly. Treatment: Recode 'Not Available' to null or a binary indicator before modelling; if '7' is constant for valid records, consider dropping or using only the missingness flag. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

7

top_rate

0.8408

cardinality

2

entropy

0.6324

entropy_ratio

0.6324

This column records how many mortality quality measures are reported for a given facility, taking integer values 1–7 plus a sentinel string 'Not Available'. Despite being numeric in nature, it was parsed as categorical, likely because of that mixed-type sentinel. Surprisingly, 'Not Available' is the single most frequent value at 32.8% (1,777 of 5,421 rows), meaning roughly a third of facilities have no mortality measure count on record. The remaining values are fairly evenly spread across 1–7, suggesting the missingness is not random but tied to whether a facility participates in mortality reporting at all. Treatment: Separate 'Not Available' into a boolean missingness indicator, then cast the remaining values to integer for use as an ordinal or numeric feature. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

8

top_value

Not Available

top_rate

0.3278

cardinality

8

entropy

2.765

entropy_ratio

0.9217

This column encodes how many mortality (MORT) measures a hospital performs better than the national benchmark, stored as a categorical string despite being an ordinal count ranging from 0 to 7. The dominant value is '0' (3,136 rows, 57.8%), meaning most hospitals beat no mortality benchmarks, while 1,777 rows (32.8%) are 'Not Available', leaving only ~9.4% of hospitals showing any above-average mortality performance. The absence of value '6' except for a single record and the jump from '5' to '7' (skipping '6' effectively) signals a highly right-skewed, near-zero distribution with a substantial missingness class masquerading as a category. Treatment: Split 'Not Available' into a binary missing indicator, then cast remaining values to integer for ordinal or numeric modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

9

top_value

0

top_rate

0.5785

cardinality

9

entropy

1.453

entropy_ratio

0.4583

This column represents a count of mortality (MORT) measures where a hospital's performance was rated 'No Different' from the national benchmark, stored as a categorical string rather than a numeric type. The most striking signal is that 32.8% of rows (1,777 of 5,421) carry 'Not Available', effectively a masked null despite the reported null_rate of 0.0. The numeric values range from 0 to 7, with '0' being extremely rare (only 12 occurrences), suggesting nearly all reporting hospitals have at least one mortality measure in this category. Treatment: Recode 'Not Available' as a true null or a separate indicator flag, then cast remaining values to integer for modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

9

top_value

Not Available

top_rate

0.3278

cardinality

9

entropy

2.806

entropy_ratio

0.8852

This column counts how many mortality (MORT) quality measures a hospital scored 'worse than expected' on, stored as a categorical string rather than an integer. The dominant value is '0' (3,266 rows, 60.2%), indicating most hospitals have no worse-than-expected mortality flags, but 1,777 rows (32.8%) carry 'Not Available', which is a substantial missingness proxy masked by a zero null_rate. Only 378 hospitals have one or more worse measures, and the counts are right-skewed, topping out at 5. Treatment: Convert numeric strings to integers, recode 'Not Available' as NaN, then treat as an ordinal count feature; consider a binary flag for any worse measure given extreme skew toward 0. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

7

top_value

0

top_rate

0.6025

cardinality

7

entropy

1.294

entropy_ratio

0.4608

This column contains footnote codes associated with a mortality (MORT) group, encoded as numeric values despite functioning as a categorical label. With only 4 unique values (5.0, and likely 19.0, 23.0, and one other near the mean) drawn from a set bounded by 5–23, this is clearly a small enumeration of footnote categories rather than a true numeric measure. The 67.2% null rate is the dominant signal — meaning footnotes apply to fewer than a third of rows, suggesting they flag exceptional or conditional cases. The near-flat kurtosis (−1.96) and bimodal-like IQR of 14.0 confirm the values are widely spread across only a few discrete codes. Treatment: Cast to categorical/string; treat nulls as 'no footnote' class; one-hot encode if used as a feature. high · anthropic:default

n

5,421

nulls

3,643 (67.2%)

unique

4

min

5

max

23

mean

11.58

median

5

std

7.057

q1

5

q3

19

iqr

14

skew

0.1488

kurtosis

-1.959

n_outliers

0

outlier_rate

0

zero_rate

0

This column represents a count of safety group measures, but is typed as categorical with only two distinct values: '8' (a fixed numeric string) and 'Not Available'. The dominance of a single value '8' across 84.1% of 5,421 rows (4,558 occurrences) suggests this may be a schema-constrained field or a denormalized count that rarely varies. The presence of 'Not Available' as the only alternative (863 rows, ~15.9%) rather than a true numeric null is surprising and indicates missing data was encoded as a string sentinel rather than left null. Treatment: Binarize into a flag (count_available vs. not_available); treat 'Not Available' as missing before any numeric use. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

8

top_rate

0.8408

cardinality

2

entropy

0.6324

entropy_ratio

0.6324

This column represents an ordinal count of safety measures implemented at a facility, stored as a categorical/string type with values ranging from 1 to 8. The most striking signal is that 38.1% of the 5,421 rows (2,065 records) carry the value 'Not Available', which is the single largest category and likely reflects missing or unreported data rather than a meaningful category. The numeric values show a non-uniform distribution, with '7' being the most common true count (733 occurrences) and '4' the rarest (223), suggesting a rough bimodal tendency toward higher counts. The column should be treated as ordinal numeric after separating out 'Not Available' as a distinct missingness indicator. Treatment: Cast numeric strings to integer, encode 'Not Available' as a separate missingness flag or impute, then use as ordinal feature. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

9

top_value

Not Available

top_rate

0.3809

cardinality

9

entropy

2.753

entropy_ratio

0.8684

This column represents a count of safety measures rated 'better' for a given entity (likely a healthcare facility or similar regulated site), stored as a categorical rather than numeric type. The dominant value is 'Not Available' at 38.1% of rows (2,065 of 5,421), which masks the underlying numeric distribution; among records with actual counts, the distribution is strongly right-skewed, with 1,548 zeros and only 3 records reaching the maximum value of 6. Analysts should be aware that the high 'Not Available' rate introduces substantial missingness risk if this field is coerced to numeric. Treatment: Separate 'Not Available' into a missingness indicator, then cast remaining values to integer for ordinal or numeric modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

8

top_value

Not Available

top_rate

0.3809

cardinality

8

entropy

2.11

entropy_ratio

0.7033

This column represents a count of safety measures rated as 'no different' (presumably compared to a benchmark), stored as a categorical type despite containing numeric-looking values 0–8. The dominant value is 'Not Available' at 38.1% of 5,421 rows (2,065 records), which is a significant missingness proxy masking as a category rather than a true null — notable since the null_rate is 0.0. The remaining values follow a rough bell-curve peaking at 5, with very sparse representation at the extremes (8 appears only 10 times, 0 only 20 times). Treatment: Recode 'Not Available' as NaN, cast remaining values to integer, then treat as ordinal or numeric feature. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

10

top_value

Not Available

top_rate

0.3809

cardinality

10

entropy

2.685

entropy_ratio

0.8083

This column represents a count of safety measures that have deteriorated, stored as a categorical field with only 5 distinct values (0, 1, 2, 3, and 'Not Available'). The dominant value is '0' at 54.3% of rows (2,941 records), indicating most entities have no worsening safety measures, but 38.1% of rows (2,065) carry 'Not Available' rather than a numeric zero, suggesting a meaningful distinction between 'measured and none found' vs. 'not assessed'. The actual worsening counts (1–3) are rare and sharply drop off, with only 6 records reaching a count of 3. Treatment: Encode 'Not Available' as a separate indicator flag, then cast numeric strings to integer for ordinal or count-based modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

5

top_value

0

top_rate

0.5425

cardinality

5

entropy

1.338

entropy_ratio

0.5764

This column encodes footnote reference numbers attached to safety groups, with only 4 distinct integer values (5, 19, 23, and at least one other given the IQR and mean) acting as categorical codes rather than true continuous quantities. The 61.8% null rate is a major signal: most records carry no footnote, making non-null values the exception. The platykurtic distribution (kurtosis ≈ −1.81) and near-zero skew confirm a flat, discrete distribution across a handful of categories, not a numeric measurement. Despite being stored as numeric, this should be treated as a sparse categorical indicator. Treatment: Cast to nullable categorical; one-hot or ordinal encode the 4 distinct values, and treat nulls as a fifth 'no footnote' category. high · anthropic:default

n

5,421

nulls

3,350 (61.8%)

unique

4

min

5

max

23

mean

10.69

median

5

std

6.95

q1

5

q3

19

iqr

14

skew

0.4116

kurtosis

-1.809

n_outliers

0

outlier_rate

0

zero_rate

0

This column represents the count of readmission group measures associated with a hospital record, stored as a categorical type despite being a numeric concept. It has only two distinct values across 5,421 rows: '11' (84.1% of records) and 'Not Available' (15.9%), with zero nulls. The near-total dominance of a single value '11' and the use of 'Not Available' as a sentinel string (rather than a true null) are both worth flagging — the column carries almost no discriminative power as a feature. Treatment: Convert 'Not Available' to null, cast to integer, then assess whether near-zero variance warrants dropping before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

11

top_rate

0.8408

cardinality

2

entropy

0.6324

entropy_ratio

0.6324

This column represents the count of readmission measures available for a healthcare facility, stored as a categorical/string type despite containing numeric values. The most striking signal is that 'Not Available' is the dominant value at 21.2% of all 5,421 rows (1,150 records), meaning roughly one-in-five facilities have no reportable readmission measure count. The remaining values span a narrow integer range (at least 2–11 visible), with near-uniform distribution across them given the high entropy ratio of 0.965, suggesting facilities tend to report varying but substantive numbers of measures. Treatment: Cast numeric strings to integer, encode 'Not Available' as NaN or a separate missingness indicator flag before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

12

top_value

Not Available

top_rate

0.2121

cardinality

12

entropy

3.459

entropy_ratio

0.965

This column represents a small integer count (0–5) of how many readmission measures a hospital performed 'better' than a national benchmark, stored as a categorical/string field. The dominant value is '0' at 61.5% (3,332 rows), meaning most hospitals beat no readmission benchmarks at all. Notably, 1,150 rows (21.2%) carry 'Not Available' rather than a numeric zero, signalling a data-availability distinction that must be resolved before any numeric analysis. Treatment: Separate 'Not Available' into a missingness indicator flag, then cast remaining values to integer for ordinal or numeric modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

7

top_value

0

top_rate

0.6146

cardinality

7

entropy

1.51

entropy_ratio

0.5379

This column represents the count of readmission measures rated 'No Different' from the national rate for a given hospital, with valid integer values ranging from 1 to at least 9 (cardinality 13 suggests values up to ~12). The most striking signal is that 'Not Available' is the single most frequent value at 21.2% (1,150 of 5,421 rows), meaning roughly one in five hospitals has no reportable count — this non-numeric sentinel is stored as a string, making the column categorical despite its inherently ordinal numeric nature. Among reportable hospitals, the distribution across counts 1–9 is remarkably flat (372–497 each), suggesting no strong clustering around a typical score. The high entropy ratio (0.92) reflects this near-uniform spread across the 13 distinct values. Treatment: Separate 'Not Available' into a missingness indicator flag, then cast remaining values to integer for ordinal/numeric modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

13

top_value

Not Available

top_rate

0.2121

cardinality

13

entropy

3.408

entropy_ratio

0.9211

This column records the number of readmission quality measures on which a hospital performed worse than the national benchmark, stored as a categorical/string field despite being fundamentally ordinal-numeric. The dominant value is '0' (2,988 of 5,421 rows, 55.1%), indicating most facilities had no worse-than-average readmission measures, while 'Not Available' accounts for 1,150 rows (21.2%) — a substantial missingness masked by a zero null_rate. The numeric range spans 0–7, but the distribution is heavily right-skewed with only 3 hospitals scoring 6 or higher. Treatment: Recode 'Not Available' as NaN, cast remaining values to integer, then treat as an ordinal feature or apply count-based encoding before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

9

top_value

0

top_rate

0.5512

cardinality

9

entropy

1.758

entropy_ratio

0.5545

This column is a coded footnote indicator for a readmission (READM) metric group, taking only 3 distinct numeric values (5, 19, 22) despite 5,421 rows. The 78.79% null rate is the dominant signal — nulls almost certainly mean 'no footnote applies', making non-null values the exception rather than the rule. The three values are likely lookup codes referencing a footnote legend (e.g., CMS suppression or reliability flags), not a continuous numeric measure, despite being stored as numeric. Treatment: Treat as categorical; map the 3 codes (5, 19, 22) to their footnote definitions and one-hot encode or use as a suppression/reliability flag. high · anthropic:default

n

5,421

nulls

4,271 (78.8%)

unique

3

min

5

max

22

mean

15.15

median

19

std

6.366

q1

5

q3

19

iqr

14

skew

-0.9528

kurtosis

-1.051

n_outliers

0

outlier_rate

0

zero_rate

0

This column records the count of patient experience group measures, but is stored as a categorical with only two distinct values: '8' (the actual measure count) and 'Not Available'. The dominant value '8' appears in 84.1% of rows (4,558 of 5,421), while 'Not Available' accounts for the remaining 15.9% (863 rows) — suggesting a structured survey instrument with a fixed number of measures for eligible facilities, and a meaningful missingness pattern for ineligible or non-reporting ones. Treatment: Treat 'Not Available' as a distinct eligibility/reporting flag; convert '8' to integer if numeric operations are needed, or encode as binary eligible/not-eligible indicator. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

8

top_rate

0.8408

cardinality

2

entropy

0.6324

entropy_ratio

0.6324

This column represents a count of facility patient experience measures, but despite its numeric-sounding name it is stored as a categorical with only 2 distinct values: '8' (58.2% of rows, n=3,154) and 'Not Available' (41.8%, n=2,267). The near-uniform presence of a single numeric value ('8') suggests this count is fixed or standardized across facilities that report it, making the column effectively a binary indicator of data availability rather than a meaningful numeric measure. No nulls exist; missingness is encoded as the string 'Not Available'. Treatment: Re-encode as binary flag (1 = '8', 0 = 'Not Available') since the numeric value is invariant; drop or use as availability indicator. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

8

top_rate

0.5818

cardinality

2

entropy

0.9806

entropy_ratio

0.9806

This column appears to store numeric footnote codes attached to a patient experience group field, with only 3 distinct values (5, 19, and 22 inferred from min/Q1/Q3/max) acting as categorical tags rather than true measurements. The 58.18% null rate is flagged as an alert, meaning footnotes are absent for the majority of records — consistent with footnotes being exception annotations rather than universal attributes. The platykurtic distribution (kurtosis −1.66) and the fact that Q1 equals the median and minimum (all 5.0) confirm the values cluster heavily at the lowest code, with the IQR of 14 spanning to 19. Treatment: Treat as a low-cardinality categorical; one-hot encode the 3 footnote codes and model nulls as a fourth explicit category ('no footnote'). medium · anthropic:default

n

5,421

nulls

3,154 (58.2%)

unique

3

min

5

max

22

mean

10.15

median

5

std

6.806

q1

5

q3

19

iqr

14

skew

0.571

kurtosis

-1.658

n_outliers

0

outlier_rate

0

zero_rate

0

This column represents a count of TE (likely Test/Treatment Episode) group measures, stored as a categorical rather than numeric type — a likely encoding artefact. It has only two distinct values across 5,421 rows: '12' (84.1% of rows) and 'Not Available' (15.9%), suggesting it is effectively a binary availability flag masquerading as a count. The dominance of a single numeric value '12' with no other numeric variation is surprising and implies the measure count may be fixed/standardised across records rather than truly variable. Treatment: Convert '12' to numeric and 'Not Available' to null, then treat as a binary missingness indicator or drop if constant after imputation. medium · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

2

top_value

12

top_rate

0.8408

cardinality

2

entropy

0.6324

entropy_ratio

0.6324

This column represents the number of Technical Efficiency (TE) measures recorded per facility, stored as a categorical/string type despite being fundamentally numeric with integer values ranging at least from 4 to 12. The most surprising signal is that 'Not Available' is the single most frequent value at 17.1% (928 of 5421 rows), indicating a meaningful data-availability gap that is structurally encoded as a string sentinel rather than a null — zero nulls are reported. Entropy ratio of 0.93 across only 13 categories suggests a near-uniform distribution among the numeric values, with no strong concentration beyond the 'Not Available' category. Treatment: Convert numeric string values to integer, recode 'Not Available' as NaN, then decide whether to impute or flag as a separate missingness indicator before modelling. high · anthropic:default

n

5,421

nulls

0 (0.0%)

unique

13

top_value

Not Available

top_rate

0.1712

cardinality

13

entropy

3.458

entropy_ratio

0.9343

This column encodes footnote reference numbers attached to a 'TE Group' field, taking only 3 distinct numeric values (5, 19, 22) across the entire dataset. 82.88% of rows are null, meaning footnotes apply to a small minority of records. The IQR of 0 and median/Q1/Q3 all equal to 19 confirm that value 19 dominates non-null entries, while values 5 and 22 appear rarely enough to generate 133 outliers (14.3% of non-null rows) and strong negative skew (−2.43). Treatment: Treat as a sparse categorical flag; convert non-null values to a one-hot or ordinal indicator and consider a separate binary 'has_footnote' feature given the 82.88% null rate. high · anthropic:default

n

5,421

nulls

4,493 (82.9%)

unique

3

min

5

max

22

mean

17.58

median

19

std

4.432

q1

19

q3

19

iqr

0

skew

-2.43

kurtosis

4.12

n_outliers

133

outlier_rate

0.1433

zero_rate

0