In qhu75/fhiRclient: SMART FHIR Client

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Introduction

The R package is developed to implement the FHIR client with essential features of authentication, CRUD operations, bundle search, and data structures for the FHIR resources. The package is built based on the python library fhircient and S4 class. Tidy-style functions were developed for the clinial data manipulation more friendly.

Import the package

To get started, first we need to load the package.

library(fhiRclient)
library(dplyr)

The dependent python fhirclient can be installed from the build-in method. The latest version can be installed from its github repository directly for the version of R4 support.

install_fhirclient(version = "latest")

Building connection

ct <- Client(app_id = "my_app",
             api_base = "https://r4.smarthealthit.org")
ct

CRUD functions

Read a Patient record

To read a patient record and convert it to json list.

pt <- Patient(list(id = "326b4675-0bc8-4dbd-b406-a5564c282401"))
ct %>% Read(pt) %>% as_json %>% as_json_tbl

To read a patient and get the birth date.

ct %>% Read(pt) %>% BirthDate

To read a patient and get the full name.

ct %>% Read(pt) %>% getName

Create a new Patient record

pt1 <- Read(ct, pt)
pt1$id <- NULL
newpt <- ct %>% Create(pt1)
newID <- newpt$id
newID

Update a patient record

An example to add middle name and update the record.

pt1 <- ct %>% Read(Patient(list(id = newID)))
pt1 %>% getName
pt1$name[[1]]$given <- list("Bradly", "Middle")
update <- ct %>% Update(pt1)
ct %>% Read(pt1) %>% getName

Delete a patient record

ct %>% Delete(pt1)

Bundle search

The Search function can be used to search for resource bundles with parameters. The page option can be specified to look up results from first page or all pages.

res <- Search(ct, "observation", "Observation",
              list(code = "http://loinc.org|2339-0"), page = "all")
res

The filterBundle function can be used to extract specific elements. For example, here we extract all the unique reference patients.

res %>% filterBundle('subject') %>% unique

We can group the results with indexes and convert it to wide table with patients in different columns.

gres <- res %>% filterBundle("entry.resource") %>% group_index("entry.resource.id")
gres
gres[1:44,] %>% tidyr::pivot_wider(names_from = entry.resource.id)

Data Classes

Here we have two ways to create a patient model, with Patient functon or a general Model constructor.

pt <- Patient(list(id = "pid"))
pt <- Model("patient", "Patient", list(id = "pid"))

The function getModelClass can be used to query all available classes for a model. The general Model function require the resource model name and a data class to build.

getModelClass("humanname")
hn <- Model("humanname", "HumanName", list(family = "A", given = list("B", "C")))
hn
as_json(hn)

Shiny Demo

ShinyDemo()

SessionInfo

sessionInfo()

qhu75/fhiRclient documentation built on Dec. 31, 2020, 2:11 a.m.