Healthcare Concepts Map
Kendrick Tham
Print Date: 2024-08-19
About This Report
*Right click for option to zoom in and view the map.
Exploring Concept Maps (aka Cmaps) help represent and clarify the relationships between various key components. It helps to identify the most important areas to focus on for improvement and make informed decisions. It helps to to engage stakeholders and facilitate collaboration among different teams. Helps to pinpoint necessary data sources and understand how they can be integrated for analysis.
The Cmap in this post present general relationships between concepts of a healthcare system.
Table of Concepts
This table the ones below it make up the Concepts Map with use of CmapTools and Excel. Due to the large number of columns, the table is transposed to avoid the end columns from pressing out the page width. Column 1 serves as the Parent for each of the Child values on the right. Each Parent Concept and their Child Concept are representative of each other in the same row, and therefore share a similar color.
Show Code
# This project provides opportunity to post images and convert one file type to another using R.
# This table is from a .csv file, converted from .csv to .cxl for import into CmapTools.
# Load necessary libraries
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("knitr")) install.packages("knitr")
if (!require("kableExtra")) install.packages("kableExtra")
library(tidyverse)
library(knitr)
library(kableExtra)
# Read the CSV file and save as `concepts_df`
file_path <- "C:\\Users\\kendr\\OneDrive\\Documents\\Data Analytics\\Solving A Healthcare Challenge\\General\\Concepts.csv"
concepts_df <- read_csv(file_path, show_col_types = FALSE)
# colnames(concepts_df)
View(concepts_df)
# Transpose the data frame
concepts_df_t <- as.data.frame(t(concepts_df))
# Replace NA values with empty strings
concepts_df_t[is.na(concepts_df_t)] <- ""
# Append ":" to the end of the first column
rownames(concepts_df_t) <- paste0(rownames(concepts_df_t), ":")
# Display the transposed Data Frame with the first column in bold and without column names
# kable(concepts_df_t, col.names = NULL, align = "l") %>%
# kable_styling(full_width = TRUE) %>%
# column_spec(1, bold = TRUE) %>%
# row_spec(0, bold = TRUE, background = "#D3D3D3")| Patient Demographics: | Age | Gender | Ethnicity | Socio-economic Status | |
| Healthcare Utilization: | Hospital Admissions | Emergency Department Visits | Outpatient Visits | Readmissions | Length of Stay |
| Care Coordination: | Care Transitions | Referral Management | Follow-up Appointments | Care Plans | |
| Healthcare Providers: | Primary Care Physicians | Specialists | Nurses | Care Coordinators | |
| Health Outcomes: | Patient Satisfaction | Quality of Life | Mortality Rates | Disease Management | |
| Medical Terminologies: | ICD Codes (International Classification of Diseases) | CPT Codes (Current Procedural Terminology) | SNOMED Codes (Systematized Nomenclature of Medicine) | ||
| Data Sources: | Electronic Health Records (EHRs) | Public Health Databases | Insurance Claims | Patient-Generated Health Data | Social Determinants of Health (SDOH) Data |
| Barriers to Care: | Access to Care | Health Literacy | Language Barriers | Transportation Issues | |
| Interventions: | Preventive Care Programs | Chronic Disease Management | Mental Health Support | Vaccination Campaigns | Community Health Initiatives |
| Technology and Tools: | Health Information Exchange (HIE) | Telehealth Services | Mobile Health Apps | Data Analytics Platforms | |
| Performance Metrics: | Readmission Rates | Emergency Department Utilization | Patient Satisfaction Scores | Adherence to Care Plans | |
| Policies and Regulations: | Health Insurance Portability and Accountability Act (HIPAA) | Affordable Care Act (ACA) | Medicare and Medicaid Policies | ||
| Disparities: | Health Disparities | Access to Care Disparities | Socio-economic Disparities |
Table of Concepts-Categories
The Grandparents. The Parents of the Parents. “The OG’s”.
Show Code
# This table is from a .csv file, converted from .csv to .cxl for import into CmapTools.
# Load necessary libraries
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("knitr")) install.packages("knitr")
if (!require("kableExtra")) install.packages("kableExtra")
library(tidyverse)
library(knitr)
library(kableExtra)
# Read the CSV file and save as `concepts_categories_df`
file_path <- "C:\\Users\\kendr\\OneDrive\\Documents\\Data Analytics\\Solving A Healthcare Challenge\\General\\Concepts Categories.csv"
concepts_categories_df <- read_csv(file_path, show_col_types = FALSE)
# colnames(concepts_categories_df)
View(concepts_categories_df)
# Transpose the data frame
concepts_categories_df_t <- as.data.frame(t(concepts_categories_df))
# Replace NA values with empty strings
concepts_categories_df_t[is.na(concepts_categories_df_t)] <- ""
# Append ":" to the end of the first column
rownames(concepts_categories_df_t) <- paste0(rownames(concepts_categories_df_t), ":")
# OUTPUT CHUNK
# Display the transposed Data Frame with the first column in bold and without column names
# kable(concepts_categories_df_t, col.names = NULL, align = "l") %>%
# kable_styling(full_width = TRUE) %>%
# column_spec(1, bold = TRUE) %>%
# row_spec(0, bold = TRUE, background = "#D3D3D3")| Patient Information: | Healthcare Providers | Patient Demographics | ||
| Healthcare Processes: | Care Coordination | Healthcare Utilization | Interventions | Technology and Tools |
| Medical Data: | Data Sources | Medical Terminologies | ||
| Challenges and Barriers: | Barriers to Care | Disparities | ||
| Governance and Policy: | Policies and Regulations | |||
| Health Metrics: | Health Outcomes | Performance Metrics |
Table of Relationships
Some relationships between all the concepts above. A small number of relationships are shown for a simple view by avoiding multiple lines. Additional relationships between these concepts are readily available thanks to the large, complex, and diverse healthcare system.
Show Code
# The two .csv files are combined, imported into CmapTools, arranged for mapping, and then exported. This code uses another .csv file via then adds relationships, concepts, and other as an update towards the final concept map. A dependent drop-down menu and data validations were used in Excel to help keep the concepts and linking phrases in check by selected values and avoiding having to resolve extra, misspelled concepts that required needless--but needful--error checking and time consumption.
# Load necessary libraries
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("knitr")) install.packages("knitr")
if (!require("kableExtra")) install.packages("kableExtra")
library(tidyverse)
library(knitr)
library(kableExtra)
# Read the CSV file and save as `relationships_df`
file_path <- "C:\\Users\\kendr\\OneDrive\\Documents\\Data Analytics\\Solving A Healthcare Challenge\\General\\Add Relationships.csv"
relationships_df <- read_csv(file_path, show_col_types = TRUE)
# colnames(relationships_df)
View(relationships_df)
# OUTPUT CHUNK
# kable(relationships_df, align = "l") %>% # col.names = NULL
# kable_styling(full_width = TRUE) %>%
# column_spec(1) %>% # bold = TRUE
# row_spec(0) # bold = TRUE, background = "#D3D3D3" | Concept-1 | Linking Phrase | Concept-2 |
|---|---|---|
| Emergency Department Visits | reduced by effective | Care Coordination |
| Hospital Admissions | reduced by effective | Care Coordination |
| Care Coordination | reduces | Healthcare Utilization |
| Healthcare Providers | implement programs to mitigate | Socio-economic Disparities |
| Healthcare Providers | provide targeted interventions for | Health Disparities |
| Patient Satisfaction Scores | reflect the quality of | Care Transitions |
| Adherence to Care Plans | reduces | Readmissions |
| Adherence to Care Plans | is ensured by | Outpatient Visits |
| Affordable Care Act (ACA) | enhances | Health Outcomes |
| Affordable Care Act (ACA) | expands | Access to Care |