Approximately 110 million patients visit emergency departments (EDs) in the United States every year, and 0.5% to 8% of these patients leave before they are seen by ED staff (1).
Leaving the ED without treatment (LWOT) can have serious consequences for both patients and hospitals, including:
1. Decrease in patient satisfaction
2. Decrease in patient safety
3. Hospital liability
4. Lost revenue for the hospital (ED facility fees, inpatient admissions, etc.) (2)
Long perceived waiting time is the most common reason for leaving, but other factors also play a part (3).
Various strategies have been proposed for reducing the occurance of LWOT, such as rounding within the ED waiting room to allay waiting time concerns (2).
This project proposes a model that identifies the likeliest patients to leave without treatment so waiting room rounding efforts can be more focused.
The dataset used in this analysis is from Christiana Care Health System, a large regional integrated health care system headquartered in Newark, Delaware. It contains data for all ED visits (approximately 196,000) made to the three emergency rooms run by CCHS for the year 2016. The data has been deidentified with regard to variables that directly identify a patient (name, account number, etc.), but it has not been deidentified with regard to visit dates and times. For that reason the source data should not be shared outside the Value Institute.
A complete data dictionary is provided in the Excel file containing the dataset.
The dataset is approximately 40 megabytes in size.
## Force the definition of data types inorder tp speed up loading of data.
dataclasses <- c("text", "text", "text", "text", "date", "date", "date", "date", "text", "date", "date", "date", "text", "text", "text", "text", "text", "text", "text", "text", "text", "text", "text", "numeric", "text", "text", "text", "text", "text", "text", "text", "text")
## Read the Data tab of the Excel file into a dataframe
sourcedata <- read_excel(sourcepath, sheet = "Data", col_types = dataclasses)The structure of the dataset looks like this:
## Classes 'tbl_df', 'tbl' and 'data.frame': 195726 obs. of 32 variables:
## $ LeftWithoutTreatmentIndicator: chr "0" "0" "0" "0" ...
## $ ODSVisitID : chr "PHI" "PHI" "PHI" "PHI" ...
## $ EDVisitID : chr "PHI" "PHI" "PHI" "PHI" ...
## $ AccountNumber : chr "PHI" "PHI" "PHI" "PHI" ...
## $ EarliestDateTime : POSIXct, format: "2016-04-12 07:18:00" "2016-04-12 07:32:00" ...
## $ ArrivalSigninDateTime : POSIXct, format: "2016-04-12 07:18:00" "2016-04-12 07:32:00" ...
## $ FullyRegisteredDateTime : POSIXct, format: "2016-04-12 08:06:00" "2016-04-12 08:58:00" ...
## $ EnteredEDDateTime : POSIXct, format: "2016-04-12 07:38:50" "2016-04-12 07:46:59" ...
## $ Facility : chr "WED" "CCH" "WED" "WED" ...
## $ DoctorSawDateTime : POSIXct, format: "2016-04-12 07:39:07" "2016-04-12 08:32:15" ...
## $ NurseSawDateTime : POSIXct, format: "2016-04-12 08:02:50" "2016-04-12 08:41:00" ...
## $ LeftEDDateTime : POSIXct, format: "2016-04-12 09:08:15" "2016-04-12 21:57:34" ...
## $ ArrivedVia1 : chr "24" "18" "24" "18" ...
## $ ArrivedVia2 : chr NA NA NA NA ...
## $ TriageClass : chr "7" "6" "8" "7" ...
## $ TriageSeverityID : chr "4" "3" "4" "2" ...
## $ ProcDesc : chr "Right Sided Weakness And Arm Pain X 2 Days" "Slip And Fall, Back Pain, Unk Loc, Hematuria" "Rash To Right Leg X 4 Days" "R Back Pain X 1 Day/Dizziness" ...
## $ PayerType : chr "Unknown" "Medicare" "Medicaid" "Commercial" ...
## $ FamilyDoc : chr NA "12472" "11018" "3264" ...
## $ Phone : chr "PHI" "PHI" "PHI" "PHI" ...
## $ HasPhone : chr "YES" "YES" "YES" "YES" ...
## $ PatientPersonID : chr "PHI" "PHI" "PHI" "PHI" ...
## $ Zip : chr "19801" "19701" "19809" "19703" ...
## $ AgeInYears : num 50 71 25 46 37 28 11 61 58 61 ...
## $ GenderID : chr "2" "1" "1" "1" ...
## $ EthnicCode : chr "NHL" "NHL" "NHL" "NHL" ...
## $ RaceCode : chr "BL" "WH" "AS" "AI" ...
## $ PrimaryLanguageCode : chr "EN" "EN" "EN" "EN" ...
## $ MaritalStatus : chr "S" "S" "M" "M" ...
## $ ReligionCode : chr "BAP" "BAP" "OTH" "NON" ...
## $ EmailAddress : chr "PHI" "PHI" "PHI" "PHI" ...
## $ HasEmailAddress : chr "NONE" "NONE" "YES" "NONE" ...Five of the 32 columns containing only the text “PHI” are all related to variables representing a patient’s deidentified “personal health information”. These variables can all be ignored and will be dropped from further analysis.
The first column, which is labeled LeftWithoutTreatmentIndicator, is the target variable that needs to be predicted. A “1” indicates the patient left before treatment, a “0” indicates they did not.
Below is a summary of the values found for the categorical variables in the dataset:
## $LeftWithoutTreatmentIndicator
## x freq
## 1 0 187018
## 2 1 8708
##
## $ArrivedVia1
## x freq
## 1 1 7411
## 2 18 45468
## 3 19 128
## 4 2 20
## 5 20 162
## 6 21 5955
## 7 22 2173
## 8 23 52358
## 9 24 61956
## 10 3 24
## 11 4 273
## 12 5 8133
## 13 6 9884
## 14 7 791
## 15 <NA> 990
##
## $ArrivedVia2
## x freq
## 1 Ambulance 7411
## 2 Bus 20
## 3 Helicopter 24
## 4 Other 791
## 5 Police 273
## 6 Relative 8133
## 7 Self 9884
## 8 <NA> 169190
##
## $TriageClass
## x freq
## 1 10 6081
## 2 101 216
## 3 102 140
## 4 103 303
## 5 104 14
## 6 105 17
## 7 106 95
## 8 107 18
## 9 108 30
## 10 109 1
## 11 110 3
## 12 6 78453
## 13 7 58017
## 14 8 41686
## 15 9 1477
## 16 <NA> 9175
##
## $TriageSeverityID
## x freq
## 1 1 1610
## 2 2 60948
## 3 3 82894
## 4 4 43823
## 5 5 6332
## 6 <NA> 119
##
## $PayerType
## x freq
## 1 Commercial 63027
## 2 Medicaid 40029
## 3 Medicare 53249
## 4 Other 522
## 5 Others 88
## 6 Self Pay 18542
## 7 Unknown 20269
##
## $FamilyDoc
## x freq
## 1 10199 82
## 2 10200 81
## 3 10201 12
## 4 10204 32
## 5 10224 3
## 6 10372 9
## 7 10373 2
## 8 10375 2
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## 2881 78223 1
## 2882 78251 1
## 2883 78258 1
## 2884 78259 1
## 2885 78381 1
## 2886 78548 1
## 2887 78613 1
## 2888 78634 1
## 2889 78641 1
## 2890 78653 1
## 2891 78660 1
## 2892 78665 1
## 2893 78730 1
## 2894 78731 1
## 2895 78734 1
## 2896 78741 3
## 2897 78745 1
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## 2899 78759 1
## 2900 78935 1
## 2901 78954 1
## 2902 79325 1
## 2903 79424 1
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## 2905 80013 2
## 2906 80015 1
## 2907 80020 1
## 2908 80026 1
## 2909 80046 1
## 2910 80047 2
## 2911 80104 1
## 2912 80108 1
## 2913 80138 1
## 2914 80203 1
## 2915 80205 1
## 2916 80220 1
## 2917 80525 2
## 2918 80538 1
## 2919 80634 1
## 2920 80904 2
## 2921 80906 1
## 2922 80917 1
## 2923 80922 1
## 2924 81122 1
## 2925 82001 1
## 2926 82501 2
## 2927 83539 2
## 2928 83642 1
## 2929 83686 1
## 2930 83713 1
## 2931 84040 1
## 2932 84081 1
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## 2934 85015 1
## 2935 85018 1
## 2936 85044 1
## 2937 85139 1
## 2938 85202 1
## 2939 85250 1
## 2940 85261 1
## 2941 85262 1
## 2942 85296 1
## 2943 85302 1
## 2944 85374 3
## 2945 85379 1
## 2946 85395 1
## 2947 85735 1
## 2948 85742 1
## 2949 85743 2
## 2950 85748 1
## 2951 85755 1
## 2952 86004 1
## 2953 86406 1
## 2954 86505 1
## 2955 87123 3
## 2956 87144 1
## 2957 87301 1
## 2958 88022 1
## 2959 88330 2
## 2960 89031 1
## 2961 89032 1
## 2962 89081 1
## 2963 89134 1
## 2964 89149 1
## 2965 89169 2
## 2966 89505 1
## 2967 90008 1
## 2968 90029 1
## 2969 90043 2
## 2970 90048 1
## 2971 90066 1
## 2972 90262 1
## 2973 90266 1
## 2974 90293 1
## 2975 90501 1
## 2976 90631 1
## 2977 90732 1
## 2978 91104 1
## 2979 91106 1
## 2980 91214 1
## 2981 91401 2
## 2982 91604 1
## 2983 91730 1
## 2984 91759 1
## 2985 91786 1
## 2986 92039 1
## 2987 92067 2
## 2988 92069 1
## 2989 92109 2
## 2990 92115 9
## 2991 92354 1
## 2992 92376 1
## 2993 92377 1
## 2994 92508 1
## 2995 92536 1
## 2996 92571 1
## 2997 92586 1
## 2998 92603 1
## 2999 92610 2
## 3000 92662 1
## 3001 92663 1
## 3002 92683 1
## 3003 92688 3
## 3004 92821 1
## 3005 92886 1
## 3006 93065 1
## 3007 93111 1
## 3008 93212 1
## 3009 93215 1
## 3010 93230 2
## 3011 93308 1
## 3012 93703 1
## 3013 93940 1
## 3014 93950 1
## 3015 94015 1
## 3016 94019 1
## 3017 94115 1
## 3018 94401 2
## 3019 94513 1
## 3020 94539 2
## 3021 94541 1
## 3022 94549 2
## 3023 94550 1
## 3024 94565 2
## 3025 94610 3
## 3026 94705 1
## 3027 94952 1
## 3028 95009 1
## 3029 95046 1
## 3030 95076 2
## 3031 95121 1
## 3032 95125 1
## 3033 95126 1
## 3034 95134 2
## 3035 95204 2
## 3036 95330 2
## 3037 95648 1
## 3038 95747 1
## 3039 95821 1
## 3040 96150 1
## 3041 96377 1
## 3042 96761 1
## 3043 96826 1
## 3044 97023 1
## 3045 97124 1
## 3046 97206 1
## 3047 97239 1
## 3048 97401 1
## 3049 97459 2
## 3050 98001 1
## 3051 98007 1
## 3052 98053 1
## 3053 98102 1
## 3054 98104 3
## 3055 98144 1
## 3056 98258 1
## 3057 98516 1
## 3058 99504 1
## 3059 99506 1
## 3060 A1160 1
## 3061 FL05 1
## 3062 J1D2J5 1
## 3063 J1E0E3 2
## 3064 L4G6P7 1
## 3065 L4M4N6 1
## 3066 M1L0E1 1
## 3067 M6J1B1 1
## 3068 N4W2R4 1
## 3069 OX2 6PE 1
## 3070 RH76JD 3
## 3071 RM16 2DA 1
## 3072 SE182QT 1
## 3073 SN251RH 1
## 3074 SN31HW 1
## 3075 SW64AJ 1
## 3076 UNK 2
## 3077 <NA> 142
##
## $AgeInYears
## x freq
## 1 0 1385
## 2 1 1276
## 3 2 954
## 4 3 754
## 5 4 705
## 6 5 718
## 7 6 687
## 8 7 620
## 9 8 583
## 10 9 613
## 11 10 627
## 12 11 602
## 13 12 648
## 14 13 740
## 15 14 842
## 16 15 1007
## 17 16 1111
## 18 17 1436
## 19 18 2481
## 20 19 2683
## 21 20 2834
## 22 21 3372
## 23 22 3242
## 24 23 3305
## 25 24 3436
## 26 25 3481
## 27 26 3550
## 28 27 3525
## 29 28 3484
## 30 29 3207
## 31 30 3319
## 32 31 3194
## 33 32 3260
## 34 33 2993
## 35 34 3178
## 36 35 3060
## 37 36 3004
## 38 37 2851
## 39 38 2791
## 40 39 2616
## 41 40 2576
## 42 41 2516
## 43 42 2342
## 44 43 2288
## 45 44 2694
## 46 45 2969
## 47 46 2911
## 48 47 2746
## 49 48 2828
## 50 49 2894
## 51 50 2897
## 52 51 3199
## 53 52 3470
## 54 53 3328
## 55 54 3490
## 56 55 3403
## 57 56 3086
## 58 57 2926
## 59 58 2983
## 60 59 2672
## 61 60 2699
## 62 61 2452
## 63 62 2444
## 64 63 2505
## 65 64 2078
## 66 65 1997
## 67 66 2067
## 68 67 2123
## 69 68 2010
## 70 69 2178
## 71 70 1638
## 72 71 1705
## 73 72 1724
## 74 73 1801
## 75 74 1659
## 76 75 1593
## 77 76 1445
## 78 77 1480
## 79 78 1405
## 80 79 1368
## 81 80 1283
## 82 81 1303
## 83 82 1180
## 84 83 1235
## 85 84 1273
## 86 85 1220
## 87 86 1144
## 88 87 986
## 89 88 861
## 90 89 845
## 91 90 642
## 92 91 669
## 93 92 533
## 94 93 462
## 95 94 365
## 96 95 251
## 97 96 188
## 98 97 133
## 99 98 89
## 100 99 48
## 101 100 46
## 102 101 39
## 103 102 11
## 104 103 8
## 105 104 7
## 106 105 138
## 107 107 1
## 108 109 1
## 109 112 1
## 110 116 1
##
## $GenderID
## x freq
## 1 0 609
## 2 1 87580
## 3 2 107537
##
## $EthnicCode
## x freq
## 1 DEC 421
## 2 HL 12689
## 3 NHL 166226
## 4 UN 16390
##
## $RaceCode
## x freq
## 1 AI 433
## 2 AS 2584
## 3 BL 74621
## 4 DE 218
## 5 OT 7912
## 6 PI 94
## 7 U 4122
## 8 UN 770
## 9 WH 104972
##
## $PrimaryLanguageCode
## x freq
## 1 AR 218
## 2 BE 37
## 3 BP 19
## 4 CN 11
## 5 EL 22
## 6 EN 182580
## 7 ES 4264
## 8 FR 96
## 9 GU 45
## 10 HC 21
## 11 HI 66
## 12 IT 52
## 13 KO 86
## 14 MA 104
## 15 OT 202
## 16 PL 23
## 17 PU 37
## 18 RU 50
## 19 SL 110
## 20 SW 10
## 21 TL 16
## 22 TU 62
## 23 UN 7515
## 24 UR 23
## 25 VI 57
##
## $MaritalStatus
## x freq
## 1 D 15105
## 2 M 58213
## 3 P 177
## 4 S 100418
## 5 U 2953
## 6 W 15670
## 7 X 3190
##
## $ReligionCode
## x freq
## 1 AMI 17
## 2 BAP 18383
## 3 BUD 137
## 4 CAT 31915
## 5 CHR 11885
## 6 CSC 583
## 7 DEC 15893
## 8 EPI 2058
## 9 FRI 30
## 10 GRE 243
## 11 HIN 310
## 12 JEH 1306
## 13 JEW 1039
## 14 LUT 1456
## 15 MEN 8
## 16 MET 11362
## 17 MOR 330
## 18 MUS 2454
## 19 NON 39984
## 20 OTH 7881
## 21 PRE 2601
## 22 PRO 7866
## 23 RUS 26
## 24 SDA 359
## 25 UKR 132
## 26 UNI 117
## 27 UNK 16781
## 28 <NA> 20570
##
## $HasEmailAddress
## x freq
## 1 - 2
## 2 " I DONT KNOW IT " 4
## 3 ? 1
## 4 1 15
## 5 213609036 1
## 6 3025978382 2
## 7 BESTMOR_FAR 1
## 8 BIBE 2
## 9 CANT REMENBER EMAIL ADDRESS 2
## 10 CECLINED 2
## 11 CELL 1
## 12 CHILD 23
## 13 CODYBUCHANAN 1
## 14 DCELINE 1
## 15 DCELINED 7
## 16 DCLINED 3
## 17 DE3CLINED 1
## 18 DEC 26
## 19 DEC LINE 2
## 20 DEC LINED 2
## 21 DEC;NED 1
## 22 DEC;OME 1
## 23 DECELINED 1
## 24 DECFLINED 1
## 25 DECINE 1
## 26 DECINED 3
## 27 DECKINED 2
## 28 DECL 6
## 29 DECL INED 1
## 30 DECL;INED 1
## 31 DECLEIN 1
## 32 DECLI NE 1
## 33 DECLIEND 3
## 34 DECLIINED 4
## 35 DECLILNED 1
## 36 DECLIMED 1
## 37 DECLIN ED 1
## 38 DECLINCED 2
## 39 DECLIND 1
## 40 DECLINDED 1
## 41 DECLINE 5754
## 42 DECLINE - NONE 2
## 43 DECLINE NONE 4
## 44 DECLINE NONE 3
## 45 DECLINE (CAN'T REMEMBER) 12
## 46 DECLINE AT THIS TIME 2125 1
## 47 DECLINE N O 1
## 48 DECLINE PER PT 1
## 49 DECLINE PER WIFE 1
## 50 DECLINE TO GIVE 1
## 51 DECLINE/NONE 1
## 52 DECLINEC 1
## 53 DECLINED 23040
## 54 DECLINED AT THIS TIME 1
## 55 DECLINED AT THIS TIME 12/18 1752 3
## 56 DECLINED EMAIL 2
## 57 DECLINED OUT OF STATE 1
## 58 DECLINED PER MOM 1
## 59 DECLINED PER PT 1
## 60 DECLINED TO GIVE 1
## 61 DECLINED/ NONE 2
## 62 DECLINED` 1
## 63 DECLINEDN 1
## 64 DECLINES 39
## 65 DECLINEWD 1
## 66 DECLINIED 3
## 67 DECLLINED 13
## 68 DECLNE 1
## 69 DECLNED 6
## 70 DECLNIED 1
## 71 DEECLINE 1
## 72 DEFCLINED 1
## 73 DELCINE 5
## 74 DELCINED 56
## 75 DELCLINED 2
## 76 DELINE 3
## 77 DELINED 7
## 78 DENIED 45
## 79 DENIES 1
## 80 DFECLINED 1
## 81 DFES 8
## 82 DNIED 1
## 83 DONE 6
## 84 EDIGGY6691 1
## 85 FAMILY STATES: NONE 1
## 86 GASGHHJ 1
## 87 HAS NONE 1
## 88 IML 1
## 89 KELLIEJMORRIS 2
## 90 LNONE 2
## 91 MASBEAUPLAN 1
## 92 MOM DECLINED 2
## 93 MOTHER DECLINED 1
## 94 N 1
## 95 N/A 92
## 96 N0NE 1
## 97 NA 15
## 98 NADA 1
## 99 NIONE 6
## 100 NNOE 1
## 101 NO 346
## 102 NO DECLINE 1
## 103 NO DX 1
## 104 NO E-MAIL 5
## 105 NO EMAIL 92
## 106 NO EMAIL ADDRESS 1
## 107 NO EMAIL GIVEN 1
## 108 NO EMAIL PER PT 10
## 109 NO REASON 1
## 110 NOE 3
## 111 NOEMAIL 2
## 112 NOEN GIVEN 1
## 113 NOINE 2
## 114 NOME 4
## 115 NON 15
## 116 NON E 2
## 117 NON GIVEN 1
## 118 NON PER PT 1
## 119 NONE 68647
## 120 NONE- 2
## 121 NONE- PER PT 8
## 122 NONE - DECLINED 1
## 123 NONE / DECLINE 3
## 124 NONE 5/6/69 1
## 125 NONE 5881982 4
## 126 NONE AVAILABLE 1
## 127 NONE GIVE 1
## 128 NONE GIVEN 340
## 129 NONE GIVEN (CHILD) 1
## 130 NONE GIVEN (MINOR CHILD) 1
## 131 NONE NONE 1
## 132 NONE PER FATHER 1
## 133 NONE PER GUARDIAN 3
## 134 NONE PER HORIZON HOUSE REP 1
## 135 NONE PER MOTHER 7
## 136 NONE PER MOTHER OF PT 3
## 137 NONE PER PARENT 11
## 138 NONE PER PATIENT 203
## 139 NONE PER PATIENT AT THIS TIME 1
## 140 NONE PER PATIENTS FAMILY 1
## 141 NONE PER PATIENTS FATHER 2
## 142 NONE PER PATIENTS MOTHER 6
## 143 NONE PER PATIENTS PARENT 1
## 144 NONE PER PT 547
## 145 NONE PER PT CHILD 1
## 146 NONE PER PT GRANDMOTHER 2
## 147 NONE PER PT GUARDIAN 7
## 148 NONE PER PT MOTHER 4
## 149 NONE PER PT STEP MOTHER 1
## 150 NONE PER PTS MOTHER 2
## 151 NONE PER PTS PARENT 2
## 152 NONE PER PTS POA 1
## 153 NONE PER PTS SON 1
## 154 NONE PT IS CHILD 1
## 155 NONE; DECLINED 1
## 156 NONEE 1
## 157 NONFE 1
## 158 NONME 1
## 159 NONOE 1
## 160 NONW 1
## 161 NOT AVAILABLE 11
## 162 NOT AVAILBLE 1
## 163 NOT GIVEN 2
## 164 NOT REQUIRED 1
## 165 O 1
## 166 PATIENT DECLINED 4
## 167 PATIENT IS A CHILD 4
## 168 PATIENT STATES NONE 4
## 169 PATIENTS MOTHER STATES NONE 1
## 170 PER DECLINED 1
## 171 PER NONE 3
## 172 PER PR DECLINE 3
## 173 PER PT DECLINED 1
## 174 PER PT DECLINE 62
## 175 PER PT DECLINED 37
## 176 PER PT DECLINED EMAIL 2
## 177 PER PT DELCINE 2
## 178 PER PT NO EMAIL 4
## 179 PER PT NONE 238
## 180 PREFERS NOT 1
## 181 PT'S DAUGHTER STATES NONE 3
## 182 PT'S FAMILY STATES: NONE 1
## 183 PT'S GRANDMOM STATES: NONE 1
## 184 PT'S MOM STATES NONE 5
## 185 PT'S MOM STATES: NONE 7
## 186 PT CAN NOT REMEMBER 1
## 187 PT CANNOT RECALL 3
## 188 PT DECLINE 2
## 189 PT DECLINED 7
## 190 PT DECLINES 6
## 191 PT DOES NOT HAVE AN EMAIL ADDRESS 1
## 192 PT REFUSED 1
## 193 PT STATED NONE 1
## 194 PT STATES NO EMAIL 4
## 195 PT STATES NO EMAIL ADDRESS 2
## 196 PT STATES NONE 64
## 197 PT STATES: NONE 1
## 198 PT STATES: NON 1
## 199 PT STATES: NONE 149
## 200 PT UNABLE TO ANSWER 2
## 201 PT WAS A TRAUMA DID NOT GET EMAIL ADDRESS; 3
## 202 PT: CHILD 1
## 203 REFUSE 12
## 204 REFUSED 247
## 205 REFUSED PER PT 3
## 206 REFUSED TO PROVIDE 1
## 207 RETIRED 1
## 208 RUFUSED 1
## 209 RUNVUS41773 1
## 210 SCORRMAMM1 2
## 211 THERE WAS NONE LISTED 2
## 212 THERE WAS NOTHING LISTED HERE 1
## 213 UAVAILABLE 7
## 214 UKN 6
## 215 UKNOWN 1
## 216 UNABAILABLE 1
## 217 UNAVAIALBLE 1
## 218 UNAVAIBLE 1
## 219 UNAVAIILABLE 1
## 220 UNAVAIL 1
## 221 UNAVAILABE 1
## 222 UNAVAILABLE 396
## 223 UNAVAILBLE 10
## 224 UNAVILABLE 2
## 225 UNEMPLOYED 3
## 226 UNIK 1
## 227 UNINOWN 1
## 228 UNK 108
## 229 UNKJNOWN 1
## 230 UNKN 15
## 231 UNKNIOWN 3
## 232 UNKNONW 1
## 233 UNKNOWM 1
## 234 UNKNOWN 824
## 235 UNKNOWN DECLINE 3
## 236 UNKNOWN AT THIS TIME 1
## 237 UNKNOWN PER PT 1
## 238 UNKNWON 5
## 239 UNKOWN 1
## 240 X 2
## 241 YES 70744
## 242 <NA> 23048Datetime variables have the following characteristics:
## EarliestDateTime ArrivalSigninDateTime
## Min. :2016-01-01 00:07:00 Min. :2016-01-01 00:07:00
## 1st Qu.:2016-03-31 19:23:15 1st Qu.:2016-03-31 21:31:15
## Median :2016-06-30 18:54:00 Median :2016-06-30 19:58:30
## Mean :2016-07-01 12:44:13 Mean :2016-07-01 13:53:05
## 3rd Qu.:2016-09-29 19:40:45 3rd Qu.:2016-09-29 20:19:00
## Max. :2016-12-31 23:52:00 Max. :2016-12-31 23:52:00
## NA's :64
## FullyRegisteredDateTime EnteredEDDateTime
## Min. :2016-01-01 00:37:00 Min. :2016-01-01 00:11:37
## 1st Qu.:2016-03-31 13:29:30 1st Qu.:2016-04-01 09:57:44
## Median :2016-06-29 21:03:00 Median :2016-06-30 04:40:56
## Mean :2016-06-30 19:45:04 Mean :2016-07-01 08:41:35
## 3rd Qu.:2016-09-28 14:04:00 3rd Qu.:2016-09-29 13:45:10
## Max. :2017-01-01 07:48:00 Max. :2017-01-01 05:55:12
## NA's :999 NA's :14217
## DoctorSawDateTime NurseSawDateTime
## Min. :2016-01-01 00:33:17 Min. :2016-01-01 00:25:36
## 1st Qu.:2016-04-01 21:33:42 1st Qu.:2016-04-01 00:48:11
## Median :2016-07-01 05:00:10 Median :2016-06-30 02:01:02
## Mean :2016-07-01 22:32:47 Mean :2016-07-01 00:38:10
## 3rd Qu.:2016-09-30 10:48:09 3rd Qu.:2016-09-28 16:31:01
## Max. :2017-01-01 05:51:09 Max. :2017-01-01 06:34:45
## NA's :10977 NA's :42265
## LeftEDDateTime
## Min. :2016-01-01 01:18:48
## 1st Qu.:2016-04-01 00:37:18
## Median :2016-06-30 23:03:59
## Mean :2016-07-01 18:08:13
## 3rd Qu.:2016-09-30 00:34:15
## Max. :2017-01-03 07:28:00
## NA's :14There are nearly 196 thousand observations. About 8,700 have a positive value for the target variable, meaning about 4% of the ED visits ended with the patient leaving without treatment. There are no missing target variable values.
An examination of the timestamp values shows all of the observations are within the expected data range, January 1 through December 31, 2016.
There are numerous instances of missing time values throughout the dataset. One possible reason for being missing is that an event did not occur. Or, the event may have occurred, but the the time was never recorded. Either way, it does not seem reasonable to try to impute a missing time value, so they will be left blank.
Other variables also have missing values. For the most part these will be left as “Unknown” and no attempt will be made to impute. If there are enough missing values for a variable it will be excluded from the modeling process. Hopefully the size of the dataset will make imputting unneccessary.
For the variable AgeInYears, there are some instances where age equals 0. These could be patients under the age of 1. I’ll assume that is the case. There appear to be a disporportionate number (138) of patients who were 105 years old. It could be some kind of data entry error but I will include them as is.
Zip Codes are a mixture of 5 and 9 digit codes. The 9 digit codes will be truncated to the first 5 digits. There a couple of dozen Zip Codes containing letters (eg., J1D2J5). These will be converted to “Unknown”. Zip Codes that are blank, all zeros or all 9’s will also be converted to “Unknown”. Many of the non-Delaware Zip Codes have only one 1 or 2 cases. In my preprocessing I will consolidate these into a single “Out of State” factor.
ArrivedVia1 is missing about 900 values. These will be converted to “Unknown”.
ArrivedVia2 is predominantely empty, missing over 169 thousand values. The variable will be excluded from further analysis.
TriageClass is missing about 9 thousand values. These will be converted to “Unknown”.
TriageSeverity is missing 119 values. These will be converted to “Unknown”.
PayorType is missing about 20 thousand values. They are already coded as “Unknown”. These will be changed to “Self Pay”.
FamilyDoc is missing about 106 thousand values. These will be converted to “Unknown”.
HasEmailAddress has about 77 thousand “Yes” values. There are about 23 thousand missing values which will be converted to “Unknown”. The remainder of the values are various ways of saying “declined” or “none”. These will all be converted to “No”.
There is a variable called ProcDesc that is a brief free text description of why the patient came to the ED. It contains a mixture of plain English, abbreviations, ICD diagnosis codes and other forms of medical shorthand and is full of misspellings. For this field I will attempt to create a binary classification or probability variable that can be added to the model.
## Zip Code
sourcedata$Zip <- substring(sourcedata$Zip, 1, 5)