Legacy function to create a vector of all possible IDs for mi2b2 workbench

Description

Legacy function to gather all possible MGH and BWH IDs from mrn.txt and con.txt input sources to provide a vector of all possible MGH or BWH IDs to be used as a data request for mi2b2 workbench.

Usage

all_ids_mi2b2(type = "MGH", d_mrn, d_con)

Arguments

Value

vector, with all MGH or BWH IDs that occur in the con and mrn datasources for all patients. Previously this was required to for mi2b2 workbenches allowing access to all possible images of the patients, even if the MGH or BWH changed over time.

Examples

## Not run: 
all_MGH_mrn <- all_ids_mi2b2(type = "MGH", d_mrn = data_mrn, d_con = data_con)

## End(Not run)

Searches diagnosis columns for given diseases.

Description

Analyzes diagnosis data loaded using load_dia. Searches diagnosis columns for a specified set of diseases. By default, the data.table is returned with new columns corresponding to boolean values, whether given group of diagnoses are present among the diagnoses. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given diagnosis is provided.

Usage

convert_dia(
  d,
  code = "dia_code",
  code_type = "dia_code_type",
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_dia",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with indicator columns whether the any of the given diagnoses are reported. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Search for Hypertension and Stroke ICD codes
diseases <- list(HT = c("ICD10:I10"), Stroke = c("ICD9:434.91", "ICD9:I63.50"))
data_dia_parse <- convert_dia(d = data_dia, codes_to_find = diseases, nThread = 2)

#Search for Hypertension and Stroke ICD codes and summarize per patient providing earliest time
diseases <- list(HT = c("ICD10:I10"), Stroke = c("ICD9:434.91", "ICD9:I63.50"))
data_dia_disease <-  convert_dia(d = data_dia, codes_to_find = diseases, nThread = 2,
collapse = "ID_MERGE", aggr_type = "earliest")

## End(Not run)

Searches columns for given diseases defined by ICD codes.

Description

Analyzes encounter data loaded using load_enc. Converts columns with ICD codes and text to simple ICD codes. If requested, the data.table is returned with new columns corresponding to boolean values, whether given group of diagnoses are present in the given columns. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given diagnosis is provided.

Usage

convert_enc(
  d,
  code = c("enc_diag_admit", "enc_diag_princ", paste0("enc_diag_", 1:10)),
  keep = FALSE,
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_enc_admit",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with formatted ICD code columns and possibly indicator columns if provided. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Parse encounter ICD columns and keep original ones as well
data_enc_parse <- convert_enc(d = data_enc, keep = TRUE, nThread = 2)

#Parse encounter ICD columns and discard original ones,
#and create indicator variable for the following diseases
diseases <- list(HT = c("I10"), Stroke = c("434.91", "I63.50"))
data_enc_disease <-  convert_enc(d = data_enc, keep = FALSE,
codes_to_find = diseases, nThread = 2)

#Parse encounter ICD columns and discard original ones
#and create indicator variables for the following diseases and summarize per patient,
#whether there are any encounters where the given diseases were registered
diseases <- list(HT = c("I10"), Stroke = c("434.91", "I63.50"))
data_enc_disease <-  convert_enc(d = data_enc, keep = FALSE,
codes_to_find = diseases, nThread = 2, collapse = "ID_MERGE")

## End(Not run)

Converts lab results to normal/abnormal based-on reference values.

Description

Analyzes laboratory data loaded using load_lab. Converts laboratory results to values without ">" or "<" by creating a column where these characters are removed. Furthermore, adds two indicator columns where based-on the reference ranges or the Abnormal_Flag column in RPDR (lab_result_abn using load_lab), the value is considered normal or abnormal.

Usage

convert_lab(
  d,
  code_results = "lab_result",
  code_reference = "lab_result_range",
  code_flag = "lab_result_abn"
)

Arguments

Value

data.table, with three additional columns: "lab_result_pretty" containing numerical results. In case of ">" or "<" notation, the numeric value is returned, as we only have information that it is at least as much or not larger than a given value. The other column: "lab_result_abn_pretty" can take values: NORMAL/ABNORMAL, depending on whether the value is within the reference range. Please be aware that there can be very different representations of values, and in some cases this will result in misclassification of values. The third column: "lab_result_abn_flag_pretty" gives abnormal if the original Abnormal_Flag column contains any information. Borderline values are considered NORMAL.

Examples

## Not run: 
#Convert loaded lab results
data_lab_pretty <- convert_lab(d = data_lab)
data_lab_pretty[, c("lab_result", "lab_result_pretty", "lab_result_range",
"lab_result_abn_pretty", "lab_result_abn_flag_pretty")]

## End(Not run)

Adds boolean columns corresponding to a group of medications whether it is present in the given row.

Description

Analyzes medication data loaded using load_med. By default, the data.table is returned with new columns corresponding to boolean values, whether given group of medications are present. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given medication is provided.

Usage

convert_med(
  d,
  code = "med",
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_med",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with indicator columns whether given group of codes_to_find is present or not. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Define medication group and add an indicator column whether
#the given medication group was administered
meds <- list(statin = c("Simvastatin", "Atorvastatin"),
             NSAID  = c("Acetaminophen", "Paracetamol"))

data_med_indic <- convert_med(d = data_med, codes_to_find = meds, nThread = 1)

#Summarize per patient if they ever had the given medication groups registered
data_med_indic_any <- convert_med(d = data_med,
codes_to_find = meds, collapse = "ID_MERGE", nThread = 2)

## End(Not run)

Extracts information from notes free text.

Description

Analyzes notes loaded using load_notes or load_lno. Extracts information from the free text present in abc_rep_txt, where abc stands for the three letter abbreviation of the given type of note. An array of string is provided using the anchors argument. The function will return as many columns as there are anchor points. Each column will contain the text between the given anchor point and the next following anchor point. This way the free text report is split into corresponding smaller texts. By default, these are the common standard elements of given note types. Here are provided potential anchor points for the given types of notes:

Cardiology:

c("Report Number:", "Report Status:", "Type:", "Date:", "Ordering Provider:", "SYSTOLIC BLOOD PRESSURE", "DIASTOLIC BLOOD PRESSURE", "VENTRICULAR RATE EKG/MIN", "ATRIAL RATE", "PR INTERVAL", "QRS DURATION", "QT INTERVAL", "QTC INTERVAL", "P AXIS", "R AXIS", "T WAVE AXIS", "LOC", "DX:", "REF:", "Electronically Signed", "report_end")

Discharge:

c("***This text report", "Patient Information", "Physician Discharge Summary", "Surgeries this Admission", "Items for Post-Hospitalization Follow-Up:", "Pending Results", "Hospital Course", "ED Course:", "Diagnosis", "Prescriptions prior to admission", "Family History:", "Physical Exam on Admission:", "Discharge Exam", "report_end")

Endoscopy:

c("NAME:", "DATE:", "Patient Information", "report_end")

History & Physical:

c("***This text report", "Patient Information", "H&P by", "Author:", "Service:", "Author Type:", "Filed:", "Note Time:", "Status:", "Editor:", "report_end")

Operative:

c("NAME:", "UNIT NO:, "DATE:", "SURGEON:", "ASST:", "PREOPERATIVE DIAGNOSIS:", "POSTOPERATIVE DIAGNOSIS:", "NAME OF OPERATION:", "ANESTHESIA:", "INDICATIONS", "OPERATIVE FINDINGS:", "DESCRIPTION OF PROCEDURE:", "Electronically Signed", "report_end")

Pathology:

c("Accession Number:", "Report Status:", "Type:", "Report:", "CASE:", "PATIENT:", "Date", "Source Care Unit:", "Path Subspecialty Service:", "Results To:", "Signed Out by:", "CLINICAL DATA:", "FINAL DIAGNOSIS:", "GROSS DESCRIPTION:", "report_end")

Progress:

c("***This text report", "Patient Information", "History", "Overview", "Progress Notes", "Medications", "Relevant Orders", "Level of Service", "report_end")

Pulmonary:

c("The Pulmonary document", "Name:", "Unit #:", "Date:", "Location:", "Smoking Status:", "Pack Years:", "SPIROMETRY:", "LUNG VOLUMES:", "DIFFUSION:", "PLETHYSMOGRAPHY:" "Pulmonary Function Test Interpretation", "Spirometry", "report_end")

Radiology:

c("Exam Code", "Ordering Provider", "HISTORY", "Associated Reports", "Report Below", "REASON", "REPORT", "TECHNIQUE", "COMPARISON", "FINDINGS", "IMPRESSION", "RECOMMENDATION", "SIGNATURES", "report_end")

Visit:

c("***This text report", "Reason for Visit", "Reason for Visit", "Vital Signs", "Chief Complaint", "History", "Overview", "Medications", "Relevant Orders", "Level of Service", "report_end"

LMR:

c("Subject", "Patient Name:", "Reason for visit", "report_end"

However, these may be modified and extended to include sections of interest, i.e. if a given score is reported in a standard fashion, then adding this phrase (i.e. "CAD-RADS") would create a column where the text following this statement is returned. After this the resulting columns can be easily cleaned up if needed. Be aware to always include "report_end" in the anchors array, to provide the function of the last occurring statement in the report.

Usage

convert_notes(
  d,
  code = NULL,
  anchors = NULL,
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with new columns corresponding to elements in anchors.

Examples

## Not run: 
#Create columns with specific parts of the radiological report defined by anchors
data_rad_parsed <- convert_notes(d = data_rad, code = "rad_rep_txt",
anchors = c("Exam Code", "Ordering Provider", "HISTORY", "Associated Reports",
"Report Below", "REASON", "REPORT", "TECHNIQUE", "COMPARISON", "FINDINGS",
"IMPRESSION", "RECOMMENDATION", "SIGNATURES", "report_end"), nThread = 2)

## End(Not run)

Searches health history data for given codes

Description

Analyzes health history data loaded using load_phy. Searches health history columns for a specified set of codes. By default, the data.table is returned with new columns corresponding to boolean values, whether given group of health history data are present within the respective columns. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given diagnosis is provided.

Usage

convert_phy(
  d,
  code = "phy_code",
  code_type = "phy_code_type",
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_phy",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with indicator columns whether the any of the given health histories are reported. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Search for Height and Weight codes
anthropometrics <- list(Weight = c("LMR:3688", "EPIC:WGT"), Height = c("LMR:3771", "EPIC:HGT"))
data_phy_parse <- convert_phy(d = data_phy, codes_to_find = anthropometrics, nThread = 2)

#Search for for Height and Weight codes and summarize per patient providing earliest time
anthropometrics <- list(Weight = c("LMR:3688", "EPIC:WGT"), Height = c("LMR:3771", "EPIC:HGT"))
data_phy_parse <- convert_phy(d = data_phy, codes_to_find = anthropometrics, nThread = 2,
collapse = "ID_MERGE", aggr_type = "earliest")

## End(Not run)

Searches procedures columns for given procedures.

Description

Analyzes procedure data loaded using load_prc. Searches procedures columns for a specified set of procedures. By default, the data.table is returned with new columns corresponding to boolean values, whether given group of procedures are present in the given procedure. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given procedure is provided.

Usage

convert_prc(
  d,
  code = "prc_code",
  code_type = "prc_code_type",
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_prc",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with indicator columns whether the any of the given procedures are reported. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Search for Anesthesia CPT codes
procedures <- list(Anesthesia = c("CTP:00410", "CPT:00104"))
data_prc_parse <- convert_prc(d = data_prc, codes_to_find = procedures, nThread = 2)

#Search for Anesthesia CPT codes
procedures <- list(Anesthesia = c("CTP:00410", "CPT:00104"))
data_prc_procedures <- convert_prc(d = data_prc, codes_to_find = procedures,
nThread = 2, collapse = "ID_MERGE", aggr_type = "earliest")

## End(Not run)

Searches columns for given reason for visit defined by ERFV codes.

Description

Analyzes reason for visit data loaded using load_rfv. If requested, the data.table is returned with new columns corresponding to boolean values, whether given group of ERFV are present in the given columns. If collapse is given, then the information is aggregated based-on the collapse column and the earliest of latest time of the given reason for visit is provided.

Usage

convert_rfv(
  d,
  code = "rfv_concept_id",
  codes_to_find = NULL,
  collapse = NULL,
  code_time = "time_rfv_start",
  aggr_type = "earliest",
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data.table, with indicator columns if provided. If collapse is present, then only unique ID and the summary columns are returned.

Examples

## Not run: 
#Parse reason for visit columns
#and create indicator variables for the following reasons and summarize per patient,
#whether there are any encounters where the given reasons were registered
reasons <- list(Pain = c("ERFV:160357", "ERFV:140012"), Visit = c("ERFV:501"))
data_rfv_disease <-  convert_rfv(d = data_rfv, keep = FALSE,
codes_to_find = reasons, nThread = 2, collapse = "ID_MERGE")

## End(Not run)

Create a database of DICOM headers.

Description

The function creates a database of DICOM headers present in a folder structure. Each series should be in its own folder, but they can be in a nested folder structure. Files where there are also folder present next to them at the same level will not be parsed. That is the folder structure needs to comply with the DICOM standard. Be aware that the function requires python and pydicom to be installed! The function cycles through all folders present in the provided path and recursively goes through them, every subfolder, and extracts the DICOM header information from the files using the dcmread function of the pydicom package. The extension of the files can be provided by the ext argument, as DICOM files may have different extensions then that of .dcm. Also, using the all boolean argument, you can specify whether the function provides output for each file, or only for the first file, which is beneficial if you are analyzing multi-slice series, as all instances have almost all the same header information. Furthermore, using the keywords argument you can manually specify which DICOM keywords you wish to extract. These need to be a valid keyword specified in the DICOM standard.

Usage

create_img_db(
  path,
  ext = c(".dcm", ".dicom", ".ima", ".tmp", ""),
  all = TRUE,
  keywords = c("StudyDate", "StudyTime", "SeriesDate", "SeriesTime", "AcquisitionDate",
    "AcquisitionTime", "ConversionType", "Manufacturer", "InstitutionName",
    "InstitutionalDepartmentName", "ReferringPhysicianName", "Modality",
    "ManufacturerModelName", "StudyDescription", "SeriesDescription", "StudyComments",
    "ProtocolName", "RequestedProcedureID", "ViewPosition", "StudyInstanceUID",
    "SeriesInstanceUID", "SOPInstanceUID", "AccessionNumber", "PatientName", "PatientID",
    "IssuerOfPatientID", "PatientBirthDate", 
     "PatientSex", "PatientAge",
    "PatientSize", "PatientWeight", "StudyID", "SeriesNumber", "AcquisitionNumber",
    "InstanceNumber", "BodyPartExamined", "SliceThickness", "SpacingBetweenSlices",
    "PixelSpacing", "PixelAspectRatio", "Rows", "Columns", "FieldOfViewDimensions",
    "RescaleIntercept", "RescaleSlope", "WindowCenter", "WindowWidth", "BitsAllocated",
    "BitsStored", "PhotometricInterpretation", "KVP", "ExposureTime", "XRayTubeCurrent",
    "ExposureInuAs", "ImageAndFluoroscopyAreaDoseProduct", "FilterType", 
    
    "ConvolutionKernel", "CTDIvol", "ReconstructionFieldOfView"),
  nThread = parallel::detectCores() - 1,
  na = TRUE,
  identical = TRUE
)

Arguments

Value

data.table, with DICOM header information return unchanged. However, the function also provides additional new columns which help further data manipulations, these are:

time_study

POSIXct, StudyDate and StudyTime concatentated together to POSIXct.

time_series

POSIXct, SeriesDate and SeriesTime concatentated together to POSIXct.

time_acquisition

POSIXct, AcquisitionDate and AcquisitionTime concatentated together to POSIXct.

name_img

string, PatientName with special characters removed.

time_date_of_birth_img

POSIXct, PatientBirthDate as POSIXct.

img_pixel_spacing

numeric, PixelSpacing value of the first element in the array returned as numerical value.

Examples

## Not run: 
#Create a database with DICOM header information
all_dicom_headers <- create_img_db(path = "/Users/Test/Data/DICOM/")
all_dicom_headers <- create_img_db(path = "/Users/Test/Data/DICOM/", ext = c(".dcm", ".DICOM"))
#Create a database with DICOM header information for only IDs and accession numbers
all_dicom_headers <- create_img_db(path = "/Users/Test/Data/DICOM/",
keywords = c("PatientID", "AccessionNumber"))

## End(Not run)

Internal function to create a database of DICOM headers.

Description

The function creates a database of DICOM headers present in a folder structure. Each series should be in its own folder, but they can be in a nested folder structure. Files where there are also folder present next to them at the same level will not be parsed. That is the folder structure needs to comply with the DICOM standard. Be aware that the function requires python and pydicom to be installed! The function cycles through all folders present in the provided path and recursively goes through them, every subfolder, and extracts the DICOM header information from the files using the dcmread function of the pydicom package. The extension of the files can be provided by the ext argument, as DICOM files may have different extensions then that of .dcm. Also, using the all boolean argument, you can specify whether the function provides output for each file, or only for the first file, which is beneficial if you are analyzing multi-slice series, as all instances have almost all the same header information. Furthermore, using the keywords argument you can manually specify which DICOM keywords you wish to extract. These need to be a valid keyword specified in the DICOM standard.

Usage

dcm_db(path, ext, all, keywords, nThread, pydicom)

Arguments

Value

data.table, with DICOM header information. This is then used by create_img_db which formats the output.

Exports free text notes to individual text files.

Description

Exports out the contents of a given cell per row into individual text files. Can be used to export out reports into individual text files for further analyses.

Usage

export_notes(d, folder, code, name1 = "ID_MERGE", name2)

Arguments

Value

NULL, files are exported to given folder.

Examples

## Not run: 
#Output all cardiology notes to given folder
d <- load_notes("Car.txt", type = "car", nThread = 2, format_orig = TRUE)
export_notes(d, folder = "/Users/Test/Notes/", code = "car_rep_txt",
name1 = "ID_MERGE", name2 = "car_rep_num")

## End(Not run)

Find exam data within a given timeframe using parallel CPU computing.

Description

Finds all, earliest or closest examination to a given timepoints using parallel computing. A progress bar is also reported in the terminal to show the progress of the computation.

Usage

find_exam(
  d_from,
  d_to,
  d_from_ID = "ID_MERGE",
  d_to_ID = "ID_MERGE",
  d_from_time = "time_rad_exam",
  d_to_time = "time_enc_admit",
  time_diff_name = "timediff_exam_to_db",
  before = TRUE,
  after = TRUE,
  time = 1,
  time_unit = "days",
  multiple = "closest",
  add_column = NULL,
  keep_data = FALSE,
  nThread = parallel::detectCores() - 1,
  shared_RAM = FALSE
)

Arguments

Value

data table, with d_from filtered to ones only within the timeframe. The columns of d_from are returned with the corresponding time column in data_to where the rows are instances which comply with the time constraints specified by the function. An additional column specified in time_diff_name is also returned, which shows the time difference between the time column in d_from and d_to for that given case. Also the time column from d_to specified by d_to_time is returned under the name of time_to_db. An additional column specified in add_column may be added from data_to to the data table.

Examples

## Not run: 
#Filter encounters for first emergency visits at one of MGH's ED departments
data_enc_ED <- data_enc[enc_clinic == "MGH EMERGENCY 10020010608"]
data_enc_ED <- data_enc_ED[!duplicated(data_enc_ED$ID_MERGE)]

#Find all radiological examinations within 3 day of the ED registration
rdt_ED <- find_exam(d_from = data_rdt, d_to = data_enc_ED,
d_from_ID = "ID_MERGE", d_to_ID = "ID_MERGE",
d_from_time = "time_rdt_exam", d_to_time = "time_enc_admit", time_diff_name = "time_diff_ED_rdt",
before = TRUE, after = TRUE, time = 3, time_unit = "days", multiple = "all",
nThread = 2)

#Find earliest radiological examinations within 3 day of the ED registration
rdt_ED <- find_exam(d_from = data_rdt, d_to = data_enc_ED,
d_from_ID = "ID_MERGE", d_to_ID = "ID_MERGE",
d_from_time = "time_rdt_exam", d_to_time = "time_enc_admit", time_diff_name = "time_diff_ED_rdt",
before = TRUE, after = TRUE, time = 3, time_unit = "days", multiple = "earliest",
nThread = 2)

#Find closest radiological examinations on or after 1 day of the ED registration
#and add primary diagnosis column from encounters
rdt_ED <- find_exam(d_from = data_rdt, d_to = data_enc_ED,
d_from_ID = "ID_MERGE", d_to_ID = "ID_MERGE",
d_from_time = "time_rdt_exam", d_to_time = "time_enc_admit", time_diff_name = "time_diff_ED_rdt",
before = FALSE, after = TRUE, time = 1, time_unit = "days", multiple = "earliest",
add_column = "enc_diag_princ", nThread = 2)

#Find closest radiological examinations on or after 1 day of the ED registration
#but also provide empty rows for patients with exam data but not within the timeframe
rdt_ED <- find_exam(d_from = data_rdt, d_to = data_enc_ED,
d_from_ID = "ID_MERGE", d_to_ID = "ID_MERGE",
d_from_time = "time_rdt_exam", d_to_time = "time_enc_admit", time_diff_name = "time_diff_ED_rdt",
before = FALSE, after = TRUE, time = 1, time_unit = "days", multiple = "earliest",
add_column = "enc_diag_princ", keep_data = TRUE nThread = 2)

## End(Not run)

Find exam data within a given timeframe using parallel CPU computing without shared RAM management.

Description

Finds all, earliest or closest examination to a given timepoints using parallel computing. A progress bar is also reported in the terminal to show the progress of the computation.

Usage

find_exam_ram(
  d_from,
  d_to,
  d_from_ID = "ID_MERGE",
  d_to_ID = "ID_MERGE",
  d_from_time = "time_rad_exam",
  d_to_time = "time_enc_admit",
  time_diff_name = "timediff_exam_to_db",
  before = TRUE,
  after = TRUE,
  time = 1,
  time_unit = "days",
  multiple = "closest",
  add_column = NULL,
  keep_data = FALSE,
  nThread = parallel::detectCores() - 1
)

Loads allergy data information into R.

Description

Loads allergy information into the R environment.

Usage

load_all(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with allergy information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_all_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from all datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_all_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from all datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_all_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_all

POSIXct, Date when the allergy was first noted, corresponds to Noted_Date in RPDR. Converted to POSIXct format.

all_all

string, Name of the allergen, corresponds to Allergen in RPDR.

all_all_code

string, Epic internal identifier for the specific allergen, corresponds to Allergen_Code in RPDR.

all_all_type

string, Hierarchy for the type of allergy noted. Denotes known level of specificity of allergen, corresponds to Allergen_Type in RPDR.

all_reac

string, Noted reactions to the allergen, corresponds to Reactions in RPDR.

all_reac_type

string, Category of reaction to the allergen, corresponds to Reaction_Type in RPDR.

all_severity

string, Degree of severity of noted reactions, corresponds to Severity in RPDR.

all_status

string, Last known status of allergen, either active or deleted from the patient's allergy record, corresponds to Status in RPDR.

all_system

string, The source system where the data was collected, corresponds to System in RPDR.

all_comment

string, Free-text information about the allergen, corresponds to Comments in RPDR.

all_del_reason

string, Free-text information about why the allergen was removed from the patient's allergy list, corresponds to Deleted_Reason in RPDR.

Examples

## Not run: 
#Using defaults
d_all <- load_all(file = "test_All.txt")

#Use sequential processing
d_all <- load_all(file = "test_All.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_all <- load_all(file = "test_All.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads all RPDR text outputs into R.

Description

Loads all RPDR text outputs into R and returns a list of data tables processed. If multiple text files of the same type are available (if the query is larger than 25000 patients), then add a "_" and a number to merge the same data sources into a single output in the order of the provided number.

Usage

load_all_data(
  folder,
  which_data = c("mrn", "con", "dem", "all", "bib", "dia", "enc", "lab", "lno", "mcm",
    "med", "mic", "phy", "prc", "prv", "ptd", "rdt", "rfv", "trn", "car", "dis", "end",
    "hnp", "opn", "pat", "prg", "pul", "rad", "vis"),
  old_dem = FALSE,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  many_sources = TRUE,
  load_report = TRUE,
  format_orig = FALSE
)

Arguments

Value

list of parsed data tables containing the information.

Examples

## Not run: 
#Load all Con, Dem and Mrn datasets processing all files within given datasource in parallel
load_all_data(folder = folder_rpdr, which_data = c("con", "dem", "mrn"),
nThread = 2, many_sources = FALSE)

#Load all supported file types parallelizing on the level of datasources
load_all_data(folder = folder_rpdr, nThread = 2, many_sources = TRUE,
format_orig = TRUE)

## End(Not run)

Helper function for loading RPDR data into R.

Description

Helper function to load different datasources from RPDR. Should not be used on its own.

Usage

load_base(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE,
  src = "mrn",
  fill = FALSE,
  sep_load = "|"
)

Arguments

Value

data table, with minimally parsed data and the raw data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_src_EMPI

string, EMPI IDs from src datasource, if the datasource is not mrn. Data is formatted using pretty_mrn().

ID_src_PMRN

string, PMRN IDs from src datasource, if the datasource is not mrn. Data is formatted using pretty_mrn().

ID_scr_loc

string, from datasource src, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

Loads BiobankFile data into R.

Description

Loads Biobank file data into the R environment.

Usage

load_bib(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with BiobankFile data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_bib_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information, corresponds to Enterprise_Master_Patient_Index in RPDR. Data is formatted using pretty_mrn().

ID_bib_MGH

string, Unique Medical Record Number for Mass General Hospital, corresponds to MGH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_BWH

string, Unique Medical Record Number for Brigham and Women's Hospital, corresponds to BWH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_FH

string, Unique Medical Record Number for Faulkner Hospital, corresponds to FH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_SRH

string, Unique Medical Record Number for Spaulding Rehabilitation Hospital, corresponds to SRH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_NWH

string, Unique Medical Record Number for Newton-Wellesley Hospital, corresponds to NWH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_NSMC

string, Unique Medical Record Number for North Shore Medical Center, corresponds to NSMC_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_MCL

string, Unique Medical Record Number for McLean Hospital, corresponds to MCL_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_MEE

string, Unique Medical Record Number for Mass Eye and Ear, corresponds to MEE_MRN in RPDR. Data is formatted using pretty_mrn().

ID_bib_DFC

string, Unique Medical Record Number for Dana Farber Cancer center, corresponds to DFC_MRN in RPDR. Data is formatted using pretty_mrn(). Legacy data.

ID_bib_WDH

string, Unique Medical Record Number for Wentworth-Douglass Hospital, corresponds to WDH_MRN in RPDR. Data is formatted using pretty_mrn(). Legacy data.

bib_subject_ID

string, Biobank unique patient identifier, corresponds to Subject_ID in RPDR. ID is not formatted.

bib_subject_ID

string, This will always default to Biobank, corresponds to Registry Name in RPDR.

Examples

## Not run: 
#Using defaults
d_bib <- load_bib(file = "test_Bib.txt")

#Use sequential processing
d_bib <- load_bib(file = "test_Bib.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_bib <- load_bib(file = "test_Bib.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads contact information into R.

Description

Loads patient contact, insurance, and PCP information into the R environment.

Usage

load_con(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = TRUE
)

Arguments

Value

data table, with contact information data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_con_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from con datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_con_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from condatasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_con_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

ID_con_loc_list

string, if prevalence of IDs in Patient_ID_List > perc, then they are included in the output. Data is formatted using pretty_mrn().

name_last

string, Patient's last name, corresponds to Last_Name in RPDR.

name_first

string, Patient's first name, corresponds to First_Name in RPDR.

name_middle

string, Patient's middle name or initial, corresponds to Middle_Name in RPDR.

name_previous

string, Any alternate names on record for this patient, corresponds to Previous_Name in RPDR.

SSN

string, Social Security Number, corresponds to SSN in RPDR.

VIP

character, Special patient statuses as defined by the EMPI group, corresponds to VIP in RPDR.

address1

string, Patient's current address, corresponds to address1 in RPDR.

address2

string, Additional address information, corresponds to address2 in RPDR.

city

string, City of residence, corresponds to City in RPDR.

state

string, State of residence, corresponds to State in RPDR.

country_con

string, Country of residence from con datasource, corresponds to Country in RPDR.

zip_con

numeric, Mailing zip code of primary residence from con datasource, corresponds to Zip in RPDR. Formatted to 5 character zip codes using pretty_numbers().

direct_contact_consent

boolean, Indicates whether the patient has given permission to contact them directly through the RODY program, corresponds to Direct_Contact_Consent in RPDR. Legacy variable.

research_invitations

boolean, Indicates if a patient can be invited to participate in research, corresponds to Research_Invitations in RPDR.

phone_home

number, Patient's home phone number, corresponds to Home_Phone in RPDR. Formatted to 10 digit phone numbers using pretty_numbers().

phone_day

number, Phone number where the patient can be reached during the day, corresponds to Day_Phone in RPDR. Formatted to 10 digit phone numbers using pretty_numbers().

insurance1

string, Patient's primary health insurance carrier and subscriber ID information, corresponds to Insurance_1 in RPDR.

insurance2

string, Patient's secondary health insurance carrier and subscriber ID information, if any, corresponds to Insurance_2 in RPDR.

insurance3

string, Patient's tertiary health insurance carrier and subscriber ID information, if any, corresponds to Insurance_3 in RPDR.

primary_care_physician

string, Comma-delimited list of all primary care providers on record for this patient per institution, along with contact information (if available), corresponds to Primary_Care_Physician in RPDR.

primary_care_physician_resident

string, Comma-delimited list of any Resident primary care providers on record for this patient per institution, along with contact information (if available), corresponds to Resident _Primary_Care_Physician in RPDR.

Examples

## Not run: 
#Using defaults
d_con <- load_con(file = "test_Con.txt")

#Use sequential processing
d_con <- load_con(file = "test_Con.txt", nThread = 1)

#Use parallel processing and parse data in
#MRN_Type and MRN columns (default in load_con) and keep all IDs
d_con <- load_con(file = "test_Con.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads demographic information into R for new demographic tables following changes in the beginning of 2022.

Description

Loads patient demographic and vital status information into the R environment. Since version 0.2.2 of the software this function supports the new demographics table data definitions.

Usage

load_dem(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with demographic information data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_dem_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information. from dem datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_dem_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network. from dem datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_dem_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

gender_legal_sex

string, Patient's legal sex, corresponds to Gender_Legal_Sex in RPDR.

sex_at_birth

string, Patient’s sex at time of birth, corresponds to Sex_at_Birth in RPDR.

gender_identity

string, Patient's personal conception of their gender, corresponds to Gender_Identity in RPDR.

time_date_of_birth

POSIXct, Patient's date of birth, corresponds to Date_of_Birth. Converted to POSIXct format.

age

string, Patient's current age (or age at death), corresponds to Age in RPDR.

language

string, Patient's preferred spoken language, corresponds to Language in RPDR.

language_group

string, Patient's preferred language: English or Non-English, corresponds to Language_Group in RPDR.

race_1

string, Patient's primary race, corresponds to Race1 in RPDR.

race_2

string, Patient's primary race if more than one race, corresponds to Race2 in RPDR.

race_group

string, Patient's Race Group as determined by Race1 and Race2, corresponds to Race_Group in RPDR.

ethnic_group

string, Patient's Ethnicity: Hispanic or Non Hispanic, corresponds to Ethnic_Group in RPDR.

marital

string, Patient's current marital status, corresponds to Marital_Status in RPDR.

religion

string, Patient-identified religious preference, corresponds to Religion in RPDR.

veteran

string, Patient's current military veteran status, corresponds to Is_a_veteran in RPDR.

country_dem

string, Patient's current country of residence from dem datasource, corresponds to Country in RPDR.

zip_dem

string, Mailing zip code of patient's primary residence from dem datasource, corresponds to Zip_code in RPDR.Formatted to 5 character zip codes.

vital_status

string, Identifies if the patient is living or deceased. This data is updated monthly from the Partners registration system and the Social Security Death Master Index, corresponds to Vital_Status in RPDR. Punctuation marks are removed.

time_date_of_death

POSIXct, Recorded date of death from source in 'Vital_Status'. Date of death information obtained solely from the Social Security Death Index will not be reported until 3 years after death due to privacy concerns. If the value is independently documented by a Partners entity within the 3 year window then the date will be displayed. corresponds to Date_of_Death in RPDR. Converted to POSIXct format.

Examples

## Not run: 
#Using defaults
d_dem <- load_dem(file = "test_Dem.txt")

#Use sequential processing
d_dem <- load_dem(file = "test_Dem.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_dem <- load_dem(file = "test_Dem.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads demographic information into R for demographics tables before 2022.

Description

Loads patient demographic and vital status information into the R environment. Since version 0.2.2 of the software, this function supports the old demographics table data definitions and is identical to the load_dem function of previous versions of the software.

Usage

load_dem_old(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with demographic information data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_dem_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information. from dem datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_dem_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network. from dem datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_dem_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

gender

string, Patient's legal sex, corresponds to Gender in RPDR.

time_date_of_birth

POSIXct, Patient's date of birth, corresponds to Date_of_Birth in RPDR. Converted to POSIXct format.

age

string, Patient's current age (or age at death), corresponds to Age in RPDR.

language

string, Patient's preferred spoken language, corresponds to Language in RPDR.

race

string, Patient's primary race, corresponds to Race in RPDR.

marital

string, Patient's current marital status, corresponds to Marital_Status in RPDR.

religion

string, Patient-identified religious preference, corresponds to Religion in RPDR.

veteran

string, Patient's current military veteran status, corresponds to Is_a_veteran in RPDR.

country_dem

string, Patient's current country of residence from dem datasource, corresponds to Country in RPDR.

zip_dem

string, Mailing zip code of patient's primary residence from dem datasource, corresponds to Zip_code in RPDR.Formatted to 5 character zip codes.

vital_status

string, Identifies if the patient is living or deceased. This data is updated monthly from the Partners registration system and the Social Security Death Master Index, corresponds to Vital_Status in RPDR. Punctuation marks are removed.

time_date_of_death

POSIXct, Recorded date of death from source in 'Vital_Status'. Date of death information obtained solely from the Social Security Death Index will not be reported until 3 years after death due to privacy concerns. If the value is independently documented by a Partners entity within the 3 year window then the date will be displayed. corresponds to Date_of_Death in RPDR. Converted to POSIXct format.

Examples

## Not run: 
#Using defaults
d_dem <- load_dem_old(file = "test_Dem.txt")

#Use sequential processing
d_dem <- load_dem_old(file = "test_Dem.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_dem <- load_dem_old(file = "test_Dem.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads diagnoses into R.

Description

Loads diagnoses information into the R environment, both Dia and Dea files.

Usage

load_dia(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with diagnoses information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_dia_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from dia datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_dia_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from dia datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_dia_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_dia

POSIXct, Date when the diagnosis was noted, corresponds to Date in RPDR. Converted to POSIXct format.

dia_name

string, Name of the diagnosis, diagnosis-related group, or phenotype. For more information on available Phenotypes visit https://phenotypes.partners.org/phenotype_list.html, corresponds to Diagnosis_Name in RPDR.

dia_code

string, Diagnosis, diagnosis-related group, or phenotype code, corresponds to Code in RPDR.

dia_code_type

string, Standardized classification system or custom grouping associated with the diagnosis code, corresponds to Code_type in RPDR.

dia_flag

string, Qualifier for the diagnosis, if any, corresponds to Diagnosis_flag in RPDR.

dia_enc_num

string, Unique identifier of the record/visit. This values includes the source system, hospital, and a unique identifier within the source system, corresponds to Encounter_number in RPDR.

dia_provider

string, Provider of record for the encounter where the diagnosis was entered, corresponds to Provider in RPDR.

dia_clinic

string, Specific department/location where the patient encounter took place, corresponds to Clinic in RPDR.

dia_hosp

string, Facility where the encounter occurred, corresponds to Hospital in RPDR.

dia_inpatient

string, Identifies whether the diagnosis was noted during an inpatient or outpatient encounter, corresponds to Inpatient_Outpatient in RPDR. Punctuation marks removed.

Examples

## Not run: 
#Using defaults
d_dia <- load_dia(file = "test_Dia.txt")

#Use sequential processing
d_dia <- load_dia(file = "test_Dia.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_dea <- load_dia(file = "test_Dea.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads encounter information into R.

Description

Loads encounter-level detail information into the R environment, both Enc and Exc files.

Usage

load_enc(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with encounter information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_enc_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from enc datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_enc_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from enc datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_enc_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

enc_numb

string, Unique identifier of the record/visit. This values includes the source system, hospital, and a unique identifier within the source system, corresponds to Encounter_number in RPDR.

time_enc_admit

POSIXct, Date when the patient was admitted or entered the facility, corresponds to Admit_Date in RPDR. Converted to POSIXct format.

time_enc_disch

POSIXct, Date when the patient was discharged or left the facility, corresponds to Discharge_Date in RPDR. Converted to POSIXct format.

enc_status

string, Billing account-related notes about the encounter. This will not be populated for all encounters, corresponds to Encounter_Status in RPDR.

enc_hosp

string, Facility where the encounter occurred, corresponds to Hospital in RPDR.

enc_inpatient

string, Classifies the type of encounter as either Inpatient or Outpatient. ED visits are currently classified under the 'Outpatient' label, corresponds to Inpatient_or_Outpatient in RPDR.

enc_service

string, Hospital service line assigned to the encounter, corresponds to Service_Line in RPDR.

enc_attending

string, The attending provider associated with the encounter. For Epic professional billing, this is the billing provider, corresponds to Attending_MD in RPDR.

enc_length

numeric, Length of stay for the encounter, corresponds to LOS_days in RPDR.

enc_clinic

string, Specific department/location where the encounter occured, corresponds to Clinic_Name in RPDR.

enc_admit_src

string, Location where the patient was admitted when entering the hospital/clinic, corresponds to Admit_Source in RPDR.

enc_pat_type

string, Provides information regarding the specific patient classifications and status of the patient visit. This field is only populated for McLean Hospital encounters, corresponds to Patient_Type in RPDR.

enc_ref_disp

string, Location where the patient has been directed for treatment or follow-up by a staff member. This field is only populated for McLean Hospital encounters, corresponds to Referrer_Discipline in RPDR.

enc_disch_disp

string, Patient's anticipated location or status following the encounter, corresponds to Discharge_Disposition in RPDR.

enc_pay

string, Payors responsible for the hospital account. Multiple payors (primary, secondary, etc.) may be listed, corresponds to Payor in RPDR.

enc_diag_admit

string, Initial working diagnosis documented by the admitting or attending physician, corresponds to Admitting_Diagnosis in RPDR.

enc_diag_princ

string, Condition established, after study, to be chiefly responsible for occasioning the admission of the patient to the hospital for care, corresponds to Principle_Diagnosis in RPDR.

enc_diag_1

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_1 in RPDR.

enc_diag_2

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_2 in RPDR.

enc_diag_3

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_3 in RPDR.

enc_diag_4

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_4 in RPDR.

enc_diag_5

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_5 in RPDR.

enc_diag_6

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_6 in RPDR.

enc_diag_7

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_7 in RPDR.

enc_diag_8

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_8 in RPDR.

enc_diag_9

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_9 in RPDR.

enc_diag_10

string, Additional diagnoses associated with this encounter or visit, corresponds to Diagnosis_10 in RPDR.

enc_diag_group

string, Diagnosis-Related Group for the encounter, in the following format: SYSTEM:CODE - Description, corresponds to DRG in RPDR.

Examples

## Not run: 
#Using defaults
d_enc <- load_enc(file = "test_Enc.txt")

#Use sequential processing
d_enc <- load_enc(file = "test_Enc.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_exc <- load_enc(file = "test_Exc.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads laboratory results into R.

Description

Loads laboratory results into the R environment, both Lab and Clb files.

Usage

load_lab(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with laboratory exam information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_lab_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from lab datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_lab_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from lab datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_lab_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_lab_result

POSIXct, Date when the specimen was collected, corresponds to Seq_Date_Time in RPDR. Converted to POSIXct format.

lab_group

string, Higher-level grouping concept used to consolidate similar tests across hospitals, corresponds to Group_ID in RPDR.

lab_loinc

string, Standardized LOINC code for the laboratory test, corresponds to Loinc_Code in RPDR.

lab_testID

string, Internal identifier for the test used by the source system, corresponds to Test_ID in RPDR.

lab_descript

string, Name of the lab test, corresponds to Test_Description in RPDR.

lab_result

string, Result value for the test, corresponds to Result in RPDR.

lab_result_txt

string, Additional information included with the result. This can include instructions for interpretation or comments from the laboratory, corresponds to Result_Text in RPDR.

lab_result_abn

string, Flag for identifying if values are outside of normal ranges or represent a significant deviation from previous values, corresponds to Abnormal_Flag in RPDR.

lab_result_unit

string, Units associated with the result value, corresponds to Reference_Unit in RPDR.

lab_result_range

string, Normal or therapeutic range for this value, corresponds to Reference_Range in RPDR.

lab_result_toxic

string, Reference range of values defined as being toxic to the patient, corresponds to Toxic_Range in RPDR.

lab_spec

string, Type of specimen collected to perform the test, corresponds to Specimen_Type in RPDR.

lab_spec_txt

string, Free-text information about the specimen, its collection or its integrity, corresponds to Specimen_Text in RPDR.

lab_correction

string, Free-text information about any changes made to the results, corresponds to Correction_Flag in RPDR.

lab_status

string, Flag which indicates whether the procedure is pending or complete, corresponds to Test_Status in RPDR.

lab_ord_pys

string, Name of the ordering physician, corresponds to Ordering_Doc in RPDR.

lab_accession

string, Internal tracking number assigned to the specimen for identification in the lab, corresponds to Accession in RPDR.

lab_source

string, Database source, either CDR (Clinical Data Repository) or RPDR (internal RPDR database), corresponds to Source in RPDR.

Examples

## Not run: 
#Using defaults
d_lab <- load_lab(file = "test_Lab.txt")

#Use sequential processing
d_lab <- load_lab(file = "test_Lab.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_clb <- load_lab(file = "test_Clb.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads LMR note documents into R.

Description

Loads notes from the LMR legacy EHR system.

Usage

load_lno(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with LMR notes information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_lno_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from lno datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_lno_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from lno datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_lno_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_lno

POSIXct, Date when the report was filed, corresponds to LMRNote_Date in RPDR. Converted to POSIXct format.

lno_rec_id

string, Internal identifier for this report within the LMR system, corresponds to Record_Id in RPDR.

lno_status

string, Completion status of the note, corresponds to Status in RPDR.

lno_author

string, Name of user who created the note, corresponds to Author in RPDR.

lno_author_mrn

string, Author's user identifier within the LMR system, corresponds to Author_MRN in RPDR.

lno_COD

string, Hospital-specific user code of the note author. The first character is a hospital-specific prefix, corresponds to COD in RPDR.

lno_hosp

string, Facility where the encounter occurred, corresponds to Institution in RPDR.

lno_subject

string, Type of note. This value is derived from the "Subject" line of the narrative text, corresponds to Subject in RPDR.

lno_rep_txt

string, Full narrative text of the note, corresponds to Comments in RPDR.

Examples

## Not run: 
#Using defaults
d_lno <- load_lno(file = "test_Lno.txt")

#Use sequential processing
d_lno <- load_lno(file = "test_Lno.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_lno <- load_lno(file = "test_Lno.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads match control data into R.

Description

Loads match control tables into the R environment.

Usage

load_mcm(
  file,
  sep = ":",
  id_length = "standard",
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data table, with matching data.

ID_case_PMRN

string, Epic PMRN value for a patient in the index cohort, corresponds to Case_Patient_EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_case_EMPI

string, EMPI value for a patient in the index cohort, corresponds to Case_Patient_EMPI in RPDR. Data is formatted using pretty_mrn().

ID_control_PMRN

string, Epic PMRN value for a patient matched to a case in the index cohort, corresponds to Control_Patient_EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_control_EMPI

string, EMPI value for a control patient matched to a case in the index cohort, corresponds to Control_Patient_EMPI in RPDR. Data is formatted using pretty_mrn().

match_strength

string, Number of similar data points between the index patient and the control patient. This number corresponds to the number of controls (Age, Gender, etc.) chosen during the match control query creation process, corresponds to Match_Strength in RPDR.

Examples

## Not run: 
#Using defaults
d_mcm <- load_mcm(file = "test_Mcm.txt")

#Use sequential processing
d_mcm <- load_mcm(file = "test_Mcm.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_mcm <- load_mcm(file = "test_Mcm.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads medication order detail into R.

Description

Loads medication order detail information into the R environment, both Med and Mee files.

Usage

load_med(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with medication order information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_med_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from enc datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_med_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from enc datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_med_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

med_enc_numb

string, Unique identifier of the record/visit, displayed in the following format: Source System - Institution Number, corresponds to Encounter_number in RPDR.

time_med

POSIXct, Completion status of the requested test/transfusion. Converted to POSIXct format, corresponds to Medication_Date in RPDR.

time_med_detail

string, To clarify when patients may have stopped taking a medication, this column provides the statuses of 'Listed' or 'Removed'. This is provided on pre-Epic (LMR) medication dates (1997-2017). The 'Listed' value denotes that a medication was on the patient's medication list on the date indicated. The 'Removed' value denotes that a medication was removed from a patient's medication list on the date indicated. Corresponds to Medication_Date_Detail in RPDR.

med

string, Name of the medication. This may be appended with the source system in the case of OnCall and LMR medications, corresponds to Medication in RPDR.

med_code

string, Medication code associated with the "Code_type" value, corresponds to Code in RPDR.

med_code_type

string, Standardized classification system or custom source value used to identify the medication, corresponds to Code_Type in RPDR.

med_quant

string, Number of units of the medication ordered, corresponds to Quantity in RPDR.

med_prov

string, Ordering provider for the medication, corresponds to Provider in RPDR.

med_clinic

string, Specific department/location where the medication was ordered or administered, corresponds to Clinic in RPDR.

med_hosp

string, Facility where the medication was ordered or administered, corresponds to Hospital in RPDR.

med_inpatient

string, Identifies whether the medication was ordered with an Inpatient or Outpatient indication, corresponds to Inpatient_Outpatient in RPDR.

med_add_info

string, Additional administration information about the medication, corresponds to Additional_Info in RPDR.

Examples

## Not run: 
#Using defaults
d_med <- load_med(file = "test_Med.txt")

#Use sequential processing
d_med <- load_med(file = "test_Med.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_mee <- load_med(file = "test_Mee.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads microbiology results into R.

Description

Loads microbiology results into the R environment.

Usage

load_mic(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE,
  format_orig = FALSE
)

Arguments

Value

data table, with microbiology information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_mic_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from mic datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_mic_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from mic datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_mic_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_mic

POSIXct, Date when the specimen was received by the laboratory, corresponds to Microbiology_Date_Time in RPDR. Converted to POSIXct format.

mic_org_code

string, Internal identifier for the organism used by the source system, corresponds to Organism_Code in RPDR.

mic_org_name

string, Name of the organism identified or tested, corresponds to Organism_Name in RPDR.

mic_org_text

string, Full narrative text of the test and results, including sensitivities, corresponds to Organism_Text in RPDR.

mic_org_comment

string, Free-text information about the organism or result, corresponds to Organism_Comment in RPDR.

mic_test_code

string, Internal identifier for the test used by the source system, corresponds to Test_Code in RPDR.

mic_test_name

string, Name of the assay to be performed, or the results of a culture, corresponds to Test_Name in RPDR.

mic_test_status

string, Status of the results, i.e. preliminary or final, corresponds to Test_Status in RPDR.

mic_test_comment

string, Free-text information about the test and results, corresponds to Test_Comments in RPDR.

mic_spec

string, Type of specimen collected to perform the test, corresponds to Specimen_Type in RPDR.

mic_spec_txt

string, Free-text information about the specimen, its collection or its integrity, corresponds to Specimen_Comments in RPDR.

mic_accession

string, Internal tracking number assigned to the specimen for identification in the microbiology lab, corresponds to Microbiology_Number in RPDR.

Examples

## Not run: 
#Using defaults
d_mic <- load_mic(file = "test_Mic.txt")

#Use sequential processing
d_mic <- load_mic(file = "test_Mic.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_mic <- load_mic(file = "test_Mic.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads MRN data into R.

Description

Loads patient identifiers for Partners institutions, including hospital-specific MRNs into the R environment.

Usage

load_mrn(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with MRN data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_mrn_INCOMING

string, Patient identifier, usually the EMPI, corresponds to IncomingId in RPDR. Data is formatted using pretty_mrn().

ID_mrn_INCOMING_SITE

string, Source of identifier, e.g. EMP for Enterprise Master Patient Index, MGH for Mass General Hospital, corresponds to IncomingSite in RPDR.

ID_mrn_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information, corresponds to Enterprise_Master_Patient_Index in RPDR. Data is formatted using pretty_mrn().

ID_mrn_MGH

string, Unique Medical Record Number for Mass General Hospital, corresponds to MGH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_BWH

string, Unique Medical Record Number for Brigham and Women's Hospital, corresponds to BWH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_FH

string, Unique Medical Record Number for Faulkner Hospital, corresponds to FH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_SRH

string, Unique Medical Record Number for Spaulding Rehabilitation Hospital, corresponds to SRH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_NWH

string, Unique Medical Record Number for Newton-Wellesley Hospital, corresponds to NWH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_NSMC

string, Unique Medical Record Number for North Shore Medical Center, corresponds to NSMC_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_MCL

string, Unique Medical Record Number for McLean Hospital, corresponds to MCL_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_MEE

string, Unique Medical Record Number for Mass Eye and Ear, corresponds to MEE_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_DFC

string, Unique Medical Record Number for Dana Farber Cancer center, corresponds to DFC_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_WDH

string, Unique Medical Record Number for Wentworth-Douglass Hospital, corresponds to WDH_MRN in RPDR. Data is formatted using pretty_mrn().

ID_mrn_STATUS

string, Status of the record, corresponds to Status in RPDR.

Examples

## Not run: 
#Using defaults
d_mrn <- load_mrn(file = "test_Mrn.txt")

#Use sequential processing
d_mrn <- load_mrn(file = "test_Mrn.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_mrn <- load_mrn(file = "test_Mrn.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads note documents into R.

Description

Loads documents information into the R environment, which are:

Cardiology:

"car"

Discharge:

"dis"

Endoscopy:

"end"

History & Physical:

"hnp"

Operative:

"opn"

Pathology:

"pat"

Progress:

"prg"

Pulmonary:

"pul"

Radiology:

"rad"

Visit:

"vis"

Usage

load_notes(
  file,
  type,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE,
  load_report = TRUE,
  format_orig = FALSE
)

Arguments

Value

data table, with notes information. abc stands for the three letter abbreviation of the given type of note.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_abc_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from abc datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_abc_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from abc datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_abc_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

abc_rep_num

string, Source-specific identifier used to reference the report, corresponds to Report_Number in RPDR.

time_abc

POSIXct, Date when the report was filed, corresponds to Report_Date_Time in RPDR. Converted to POSIXct format.

abc_rep_desc

string, Type of report or procedure documented in the report, corresponds to Report_Description in RPDR.

abc_rep_status

string, Completion status of the note/report, corresponds to Report_Status in RPDR.

abc_rep_type

string, See specification in RPDR data dictionary, corresponds to Report_Type in RPDR.

abc_rep_txt

string, Full narrative text contained in the note/report, corresponds to Report_Text in RPDR. Only provided if load_report is TRUE.

Examples

## Not run: 
#Using defaults
d_hnp <- load_notes(file = "test_Hnp.txt", type = "hnp")

#Use sequential processing
d_hnp <- load_notes(file = "test_Hnp.txt", type = "hnp", nThread = 1, format_orig = TRUE)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_hnp <- load_notes(file = "test_Hnp.txt", type = "hnp", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads helath history information into R.

Description

Loads vital signs, social history, immunizations, and various other health history details into the R environment.

Usage

load_phy(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with health history information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_phy_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from phy datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_phy_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from phy datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_phy_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_phy

POSIXct, Date when the diagnosis was noted, corresponds to Date in RPDR. Converted to POSIXct format.

phy_name

string, Type of clinical value/observation recorded, corresponds to Concept_Name in RPDR.

phy_code

string, Source-specific identifier for the specific type of clinical observation, corresponds to Code in RPDR.

phy_code_type

string, Source system for the value, corresponds to Code_type in RPDR.

phy_result

string, Value associated with the clinical observation. Note: BMI results are calculated internally in the RPDR, corresponds to Results in RPDR.

phy_unit

string, Units associated with the clinical observation, corresponds to Units in RPDR.

phy_provider

string, Provider of record for the encounter where the observation was recorded, corresponds to Providers in RPDR.

phy_clinic

string, Specific department/location where the patient observation was recorded, corresponds to Clinic in RPDR.

phy_hosp

string, Facility where the observation was recorded, corresponds to Hospital in RPDR.

phy_inpatient

string, Classifies the type of encounter where the observation was entered, corresponds to Inpatient_Outpatient in RPDR.

phy_enc_num

string, Unique identifier of the record/visit. This values includes the source system and a unique identifier within the source system, corresponds to Encounter_number in RPDR.

Examples

## Not run: 
#Using defaults
d_phy <- load_phy(file = "test_Phy.txt")

#Use sequential processing
d_phy <- load_phy(file = "test_Phy.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_phy <- load_phy(file = "test_Phy.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads procedures into R.

Description

Loads Clinical procedure information into the R environment, both Prc and Pec files.

Usage

load_prc(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with procedural information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_prc_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from prc datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_prc_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from prc datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_prc_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_prc

POSIXct, Date when the procedure was performed, corresponds to Date in RPDR. Converted to POSIXct format.

prc_name

string, Name of the procedure or operation performed, corresponds to Procedure_Name in RPDR.

prc_code

string, Procedure code associated with the "Code_type" value, corresponds to Code in RPDR.

prc_code_type

string, Standardized classification system or custom source value associated with the procedure code, corresponds to Code_type in RPDR.

prc_flag

string, Qualifier for the diagnosis, corresponds to Procedure_Flag in RPDR.

prc_quantity

string, Number of the procedures that were ordered for this record, corresponds to Quantity in RPDR.

prc_provider

string, Provider identifies the health care clinician performing the procedure, corresponds to Provider in RPDR.

prc_clinic

string, Specific department/location where the procedure was ordered or performed, corresponds to Clinic in RPDR.

prc_hosp

string, Facility where the procedure was ordered or performed, corresponds to Hospital in RPDR.

prc_inpatient

string, classifies the type of encounter where the procedure was performed or ordered.

prc_enc_num

string, Unique identifier of the record/visit, displayed in the following format: Source System - Institution Number, corresponds to Encounter_number in RPDR.

Examples

## Not run: 
#Using defaults
d_prc <- load_prc(file = "test_Prc.txt")

#Use sequential processing
d_prc <- load_prc(file = "test_Prc.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_pec <- load_prc(file = "test_Pec.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads providers information into R.

Description

Loads providers information into the R environment.

Usage

load_prv(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = TRUE
)

Arguments

Value

data table, with provider information data.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_con_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from con datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_con_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from condatasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_con_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_prv_last_seen

POSIXct, Date when the patient was last seen by the provider, corresponds to Last_Seen_Date in RPDR.

prv_name

string, Full name of the provider, corresponds to Provider_Name in RPDR.

prv_rank

string, Provides a quantitative value of provider's level of interaction with the patient. This is calculated using the number of CPT codes for face-to-face visits that the provider has billed for in relation to the patient, corresponds to Provider_Rank in RPDR.

prv_ID

string, Identification code for the provider, including the source institution, corresponds to Provider_ID in RPDR.

prv_ID_CMP

string, Corporate Provider Master ID. This is the unique identifier for a provider across the MGB network, corresponds to CPM_Id in RPDR.

prv_spec

string, Comma-delimited list of the provider's specialties, corresponds to Specialties in RPDR.

prv_pcp

string, Available for BWH and MGH PCPs only. Flag indicating whether the provider is listed as the patient's Primary Care Physician, corresponds to Is_PCP in RPDR.

prv_dep

string, Provider's department, corresponds to Enterprise_service in RPDR.

prv_address1

string, Address of the provider's primary practice, corresponds to Address_1 in RPDR.

prv_address2

string, Additional address information, corresponds to Address_2 in RPDR.

prv_city

string, City of the provider's primary practice, corresponds to City in RPDR.

prv_state

string, State of the provider's primary practice, corresponds to State in RPDR.

prv_zip

string, Mailing zip code of provider's primary practice, corresponds to Zip in RPDR.

prv_phone

string, Telephone number of the provider's primary practice, corresponds to Phone_Ext in RPDR.

prv_fax

string, Fax number of the provider's primary practice, corresponds to Fax in RPDR.

prv_email

string, Primary e-mail address for the provider, corresponds to Email in RPDR.

Examples

## Not run: 
#Using defaults
d_prv <- load_prv(file = "test_Prv.txt")

#Use sequential processing
d_prv <- load_prv(file = "test_Prv.txt", nThread = 1)

#Use parallel processing and parse data in
#MRN_Type and MRN columns (default in load_con) and keep all IDs
d_prv <- load_prv(file = "test_Prv.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads patient data information into R.

Description

Loads patient data information into the R environment.

Usage

load_ptd(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with patient data information information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_ptd_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from ptd datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_ptd_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from ptd datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_ptd_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_ptd_start

POSIXct, Date item was initiated in the record, corresponds to Start_Date in RPDR. Converted to POSIXct format.

time_ptd_end

POSIXct, Date item was finalized in the record, corresponds to End_Date in RPDR. Converted to POSIXct format.

ptd_desc

string, Name of the item being reported, corresponds to Description in RPDR.

ptd_result

string, Result of the item being reported, corresponds to Result in RPDR.

ptd_type

string, Describes the type of data being reported, corresponds to Patient_Data_Type in RPDR.

ptd_enc_num

string, Unique identifier of the record/visit. This values includes the source system and a unique identifier within the source system, corresponds to Encounter_number in RPDR.

Examples

## Not run: 
#Using defaults
d_ptd <- load_ptd(file = "test_Phy.txt")

#Use sequential processing
d_ptd <- load_ptd(file = "test_Phy.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_ptd <- load_ptd(file = "test_Phy.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads radiology procedures data into R.

Description

Loads radiology procedures information into the R environment.

Usage

load_rdt(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with radiological exam information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_rdt_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from rdt datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_rdt_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from rdt datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_rdt_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_rdt_exam

POSIXct, Date of the radiology exam, corresponds to Date in RPDR. Converted to POSIXct format.

rdt_mode

string, Modality of the exam, corresponds to Mode in RPDR.

rdt_group

string, Higher-level grouping concept used to consolidate similar procedures across hospitals, corresponds to Group in RPDR.

rdt_test_code

string, Internal identifier for the procedure used by the source system, corresponds to Test_Code in RPDR.

rdt_test_desc

string, Full name of the exam/study performed, corresponds to Test_Description in RPDR.

rdt_accession

string, Identifier assigned to the report or procedure for Radiology tracking purposes, corresponds to Accession_Number in RPDR.

rdt_provider

string, Ordering or authorizing provider for the study, corresponds to Provider in RPDR.

rdt_clinic

string, Specific department/location where the procedure was ordered or performed, corresponds to Clinic in RPDR.

rdt_hosp

string, Facility where the order was entered, corresponds to Hospital in RPDR.

rdt_inpatient

string, Classifies the type of encounter where the procedure was performed, corresponds to Inpatient_Outpatient in RPDR.

Examples

## Not run: 
#Using defaults
d_rdt <- load_rdt(file = "test_Rdt.txt")

#Use sequential processing
d_rdt <- load_rdt(file = "test_Rdt.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_rdt <- load_rdt(file = "test_Rdt.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads reason for visit data into R.

Description

Loads reason for visit information into the R environment.

Usage

load_rfv(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with reason for visit information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_rfv_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from dia datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_rfv_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from rfv datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_rfv_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_rfv_start

POSIXct, Start date of the encounter, corresponds to Start_Date in RPDR. Converted to POSIXct format.

time_rfv_end

POSIXct, End date of the encounter, corresponds to End_Date in RPDR. Converted to POSIXct format.

rfv_provider

string, Primary provider for the encounter, corresponds to Provider in RPDR.

rfv_hosp

string, Facility where the encounter occurred, corresponds to Hospital in RPDR.

rfv_clinic

string, Specific department/location where the patient encounter took place, corresponds to Clinic in RPDR.

rfv_chief_complaint

string, Description of the chief complaint/reason for visit, corresponds to Chief_Complaint in RPDR.

rfv_concept_id

string, Epic identifier for the chief complaint/reason for visit, corresponds to Concept_id in RPDR.

rfv_comment

string, Free-text comments regarding the chief complain/reason for visit, corresponds to Comments in RPDR.

rfv_enc_numb

string, Unique identifier of the record/visit. This values includes the source system, hospital, and a unique identifier within the source system, corresponds to Encounter_number in RPDR.

Examples

## Not run: 
#Using defaults
d_rfv <- load_rfv(file = "test_Rfv.txt")

#Use sequential processing
d_rfv <- load_rfv(file = "test_Rfv.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_rfv <- load_rfv(file = "test_Rfv.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Loads transfusion results into R.

Description

Loads transfusion results into the R environment.

Usage

load_trn(
  file,
  merge_id = "EMPI",
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  na = TRUE,
  identical = TRUE,
  nThread = parallel::detectCores() - 1,
  mrn_type = FALSE
)

Arguments

Value

data table, with transfusion information.

ID_MERGE

numeric, defined IDs by merge_id, used for merging later.

ID_trn_EMPI

string, Unique Partners-wide identifier assigned to the patient used to consolidate patient information from trn datasource, corresponds to EMPI in RPDR. Data is formatted using pretty_mrn().

ID_trn_PMRN

string, Epic medical record number. This value is unique across Epic instances within the Partners network from trn datasource, corresponds to EPIC_PMRN in RPDR. Data is formatted using pretty_mrn().

ID_trn_loc

string, if mrn_type == TRUE, then the data in MRN_Type and MRN are parsed into IDs corresponding to locations (loc). Data is formatted using pretty_mrn().

time_trn

POSIXct, Date when the transfusion was administered or test was performed, corresponds to Transaction_Date_Time in RPDR. Converted to POSIXct format.

trn_descript

string, The type of procedure or product administered, corresponds to Test_Description in RPDR.

trn_result

string, Results of the test or transaction/lot number of transfusion, corresponds to Results in RPDR.

trn_result_abn

string, Denotes an abnormal finding or value, corresponds to Abnormal_Flag in RPDR.

trn_comment

string, Free-text comments about the status of the test/transfusion, corresponds to Comments in RPDR.

trn_status

string, Completion status of the requested test/transfusion, corresponds to Status_Flag in RPDR.

trn_accession

string, Identifier assigned to the test/transfusion for tracking purposes by the blood bank, corresponds to Accession in RPDR.

Examples

## Not run: 
#Using defaults
d_trn <- load_trn(file = "test_Trn.txt")

#Use sequential processing
d_trn <- load_trn(file = "test_Trn.txt", nThread = 1)

#Use parallel processing and parse data in MRN_Type and MRN columns and keep all IDs
d_trn <- load_trn(file = "test_Trn.txt", nThread = 20, mrn_type = TRUE, perc = 1)

## End(Not run)

Parse IDs from a string of delimited list of values.

Description

Creates columns corresponding to MRNs in the string of delimited list of values. If the string and the numeric part of the MRN are present in the same column, then supply the column to str. If the string portion and the numeric portion is in different columns, then supply the string part to str and the numeric part to num.

Usage

parse_ids(
  str,
  num = NULL,
  sep = ":",
  id_length = "standard",
  perc = 0.6,
  nThread = parallel::detectCores() - 1
)

Arguments

Value

data table, with columns corresponding to MRNs in the string of delimited list of values.

Converts MRN integer to string compatible with RPDR.

Description

Adds or removes zeros from integers to comply with MRN code standards for given institution and adds institution prefix.

Usage

pretty_mrn(v, prefix = "MGH", sep = ":", id_length = "standard", nThread = 1)

Arguments

Value

vector, with characters formatted to specified lengths. If length of the ID does not match the required length, then leading zeros are added to the ID. If the ID is longer then the required length, then numerals from the beginning of the ID are cut off until it is the required length.

Examples

## Not run: 
mrns <- sample(1e4:1e7, size = 10) #Simulate MRNs

#MGH format
pretty_mrn(v = mrns, prefix = "MGH")

#BWH format
pretty_mrn(v = mrns, prefix = "BWH")

#Multiple sources using space as a separator
pretty_mrn(v = mrns[1:3], prefix = c("MGH", "BWH", "EMPI"), sep = " ")

#Keeping the length of the IDs despite not adhering to the requirements
pretty_mrn(v = mrns, prefix = "EMPI", id_length = "asis")

## End(Not run)

Converts numerical codes to universal format specified by length.

Description

Creates numerical strings with given lengths by removing additional characters from the back and adding leading zeros if necessary.

Usage

pretty_numbers(v, length_final = 5, remove_from_back = 4)

Arguments

Value

vector, with characters formatted accordingly.

Removes spaces, special characters and capitals from string vector.

Description

Removes paces, special characters and capitals from string vector and converts unknowns to NA.

Usage

pretty_text(
  v,
  remove_after = FALSE,
  remove_punc = FALSE,
  remove_white = FALSE,
  add_na = TRUE
)

Arguments

Value

vector, with characters formatted accordingly.

Delete columns with all NA or all identical data.

Description

Delete columns where all data elements are NA or the same.

Usage

remove_column(dt, na = TRUE, identical = TRUE)

Arguments

Value

data table, with data.