醫療保險和醫療補助服務中心提供了來自公共數據的兩個數據集,https://data.cms.gov。
-
cms_patient_experience
包含一些來自“臨終關懷 - 提供者數據”的經過輕微清理的數據,該數據提供了臨終關懷機構的列表以及有關患者護理質量的一些數據,https://data.cms.gov/provider-data/dataset/252m-zfp9 。 -
cms_patient_care
“2020 年醫生和臨床醫生質量支付計劃虛擬小組公開報告”,https://data.cms.gov/provider-data/dataset/8c70-d353
格式
cms_patient_experience
是一個包含 500 個觀察值和五個變量的 DataFrame :
- org_pac_id,org_nm
-
組織 ID 和名稱
- measure_cd,measure_title
-
測量代碼和標題
- prf_rate
-
衡量績效率
cms_patient_care
是一個包含 252 個觀測值和 5 個變量的 DataFrame :
- ccn,facility_name
-
設施 ID 和名稱
- measure_abbr
-
縮寫的測量標題,適合用作變量名稱
- score
-
測量分數
- type
-
分數是指滿分 100 分 ("observed"),還是原始分數的最大可能值 ("denominator")
例子
cms_patient_experience %>%
dplyr::distinct(measure_cd, measure_title)
#> # A tibble: 6 × 2
#> measure_cd measure_title
#> <chr> <chr>
#> 1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and…
#> 2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate
#> 3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider
#> 4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education
#> 5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff
#> 6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources
cms_patient_experience %>%
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)
#> # A tibble: 95 × 8
#> org_pac_id org_nm CAHPS…¹ CAHPS…² CAHPS…³ CAHPS…⁴ CAHPS…⁵ CAHPS…⁶
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0446157747 USC CARE ME… 63 87 86 57 85 24
#> 2 0446162697 ASSOCIATION… 59 85 83 63 88 22
#> 3 0547164295 BEAVER MEDI… 49 NA 75 44 73 12
#> 4 0749333730 CAPE PHYSIC… 67 84 85 65 82 24
#> 5 0840104360 ALLIANCE PH… 66 87 87 64 87 28
#> 6 0840109864 REX HOSPITA… 73 87 84 67 91 30
#> 7 0840513552 SCL HEALTH … 58 83 76 58 78 26
#> 8 0941545784 GRITMAN MED… 46 86 81 54 NA 25
#> 9 1052612785 COMMUNITY M… 65 84 80 58 87 29
#> 10 1254237779 OUR LADY OF… 61 NA NA 65 NA 17
#> # … with 85 more rows, and abbreviated variable names ¹CAHPS_GRP_1,
#> # ²CAHPS_GRP_2, ³CAHPS_GRP_3, ⁴CAHPS_GRP_5, ⁵CAHPS_GRP_8, ⁶CAHPS_GRP_12
cms_patient_care %>%
pivot_wider(
names_from = type,
values_from = score
)
#> # A tibble: 126 × 5
#> ccn facility_name measure_…¹ denom…² obser…³
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 011500 BAPTIST HOSPICE beliefs_a… 202 100
#> 2 011500 BAPTIST HOSPICE composite… 202 88.1
#> 3 011500 BAPTIST HOSPICE dyspena_t… 110 99.1
#> 4 011500 BAPTIST HOSPICE dyspnea_s… 202 100
#> 5 011500 BAPTIST HOSPICE opioid_bo… 61 100
#> 6 011500 BAPTIST HOSPICE pain_asse… 107 100
#> 7 011500 BAPTIST HOSPICE pain_scre… 202 88.6
#> 8 011500 BAPTIST HOSPICE treat_pref 202 100
#> 9 011500 BAPTIST HOSPICE visits_im… 232 96.1
#> 10 011501 SOUTHERNCARE NEW BEACON N. BIRMINGHAM beliefs_a… 525 100
#> # … with 116 more rows, and abbreviated variable names ¹measure_abbr,
#> # ²denominator, ³observed
cms_patient_care %>%
pivot_wider(
names_from = measure_abbr,
values_from = score
)
#> # A tibble: 28 × 12
#> ccn facility…¹ type belie…² compo…³ dyspe…⁴ dyspn…⁵ opioi…⁶ pain_…⁷
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 011500 BAPTIST H… deno… 202 202 110 202 61 107
#> 2 011500 BAPTIST H… obse… 100 88.1 99.1 100 100 100
#> 3 011501 SOUTHERNC… deno… 525 525 438 525 101 325
#> 4 011501 SOUTHERNC… obse… 100 100 100 100 100 100
#> 5 011502 COMFORT C… deno… 295 295 236 295 38 121
#> 6 011502 COMFORT C… obse… 100 99.3 99.2 100 100 100
#> 7 011503 SAAD HOSP… deno… 694 694 555 694 37 677
#> 8 011503 SAAD HOSP… obse… 99.9 96 99.6 98.3 100 99
#> 9 011505 HOSPICE F… deno… 600 600 308 600 151 308
#> 10 011505 HOSPICE F… obse… 97.8 92 97.7 99.8 98.7 92.5
#> # … with 18 more rows, 3 more variables: pain_screening <dbl>,
#> # treat_pref <dbl>, visits_imminent <dbl>, and abbreviated variable
#> # names ¹facility_name, ²beliefs_addressed, ³composite_process,
#> # ⁴dyspena_treatment, ⁵dyspnea_screening, ⁶opioid_bowel,
#> # ⁷pain_assessment
cms_patient_care %>%
pivot_wider(
names_from = c(measure_abbr, type),
values_from = score
)
#> # A tibble: 14 × 20
#> ccn facili…¹ belie…² belie…³ compo…⁴ compo…⁵ dyspe…⁶ dyspe…⁷ dyspn…⁸
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 011500 BAPTIST… 202 100 202 88.1 110 99.1 202
#> 2 011501 SOUTHER… 525 100 525 100 438 100 525
#> 3 011502 COMFORT… 295 100 295 99.3 236 99.2 295
#> 4 011503 SAAD HO… 694 99.9 694 96 555 99.6 694
#> 5 011505 HOSPICE… 600 97.8 600 92 308 97.7 600
#> 6 011506 SOUTHER… 589 100 589 99.7 477 100 589
#> 7 011508 SOUTHER… 420 100 420 99.5 347 100 420
#> 8 011510 CULLMAN… 54 100 54 90.7 27 100 54
#> 9 011511 HOSPICE… 179 100 179 99.4 123 99.2 179
#> 10 011512 SOUTHER… 396 100 396 99.5 241 100 396
#> 11 011513 SHEPHER… 335 99.1 335 87.2 154 90.9 335
#> 12 011514 ST VINC… 210 100 210 97.6 137 100 210
#> 13 011516 HOSPICE… 103 100 103 97.1 75 98.7 103
#> 14 011517 HOSPICE… 400 99.8 400 95.8 182 99.5 400
#> # … with 11 more variables: dyspnea_screening_observed <dbl>,
#> # opioid_bowel_denominator <dbl>, opioid_bowel_observed <dbl>,
#> # pain_assessment_denominator <dbl>, pain_assessment_observed <dbl>,
#> # pain_screening_denominator <dbl>, pain_screening_observed <dbl>,
#> # treat_pref_denominator <dbl>, treat_pref_observed <dbl>,
#> # visits_imminent_denominator <dbl>, visits_imminent_observed <dbl>,
#> # and abbreviated variable names ¹facility_name, …
相關用法
- R tidyr chop 砍伐和砍伐
- R tidyr complete 完成缺少數據組合的 DataFrame
- R tidyr separate_rows 將折疊的列分成多行
- R tidyr extract 使用正則表達式組將字符列提取為多列
- R tidyr pivot_longer_spec 使用規範將數據從寬轉為長
- R tidyr unnest_longer 將列表列取消嵌套到行中
- R tidyr uncount “計數” DataFrame
- R tidyr pivot_wider_spec 使用規範將數據從長軸轉向寬軸
- R tidyr replace_na 將 NA 替換為指定值
- R tidyr unnest_wider 將列表列取消嵌套到列中
- R tidyr full_seq 在向量中創建完整的值序列
- R tidyr nest 將行嵌套到 DataFrame 的列表列中
- R tidyr separate 使用正則表達式或數字位置將字符列分成多列
- R tidyr pivot_wider 將數據從長軸轉向寬軸
- R tidyr nest_legacy Nest() 和 unnest() 的舊版本
- R tidyr separate_longer_delim 將字符串拆分為行
- R tidyr gather 將列收集到鍵值對中
- R tidyr hoist 將值提升到列表列之外
- R tidyr pivot_longer 將數據從寬轉為長
- R tidyr pack 打包和拆包
- R tidyr separate_wider_delim 將字符串拆分為列
- R tidyr drop_na 刪除包含缺失值的行
- R tidyr fill 用上一個或下一個值填充缺失值
- R tidyr tidyr_legacy 舊名稱修複
- R tidyr expand 擴展 DataFrame 以包含所有可能的值組合
注:本文由純淨天空篩選整理自Hadley Wickham等大神的英文原創作品 Data from the Centers for Medicare & Medicaid Services。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。