Healthcare Trends and Seasonal Patterns Analysis

Comprehensive Analysis of Healthcare Utilization Data (2010-2024)

Author

STA220: Example project

Published

March 10, 2026

Note

This report was generated using synthetic data! It is only used for demonstration purposes and does not reflect real patients or healthcare providers. The contents have not been reviewed or validated by medical professionals.

Executive Summary

This report analyzes healthcare utilization patterns from a comprehensive synthetic healthcare dataset spanning from 1920 to 2024, with detailed focus on the period 2010-2024. The analysis reveals significant temporal trends, seasonal patterns, and variations across healthcare organizations and service types.

Key Findings: - Healthcare encounters show exponential growth, peaking at over 650 encounters per month by 2024 - Spring consistently shows the highest healthcare activity across all metrics - Healthcare organizations exhibit varying degrees of seasonal sensitivity (14-35 point variation) - Follow-up and preventive care show the strongest seasonal patterns - Acute care remains most stable throughout the year

Data Overview

Code

library(tidyverse)
library(data.table)
library(lubridate)
library(knitr)
library(here)
here::i_am("reports/healthcare_trends_analysis.qmd")

purrr::walk2(
  fs::path_ext_remove(dir(here("data-fixed"))),
  dir(here("data-fixed"), ".csv", full.names = TRUE),
  \(name, path) assign(name, data.table::fread(path), envir = .GlobalEnv)
)


# Display dataset summary
cat("Healthcare Dataset Overview:\n")

Healthcare Dataset Overview:

Code

cat("- Patients:", nrow(patients), "records\n")

- Patients: 6851 records

Code

cat("- Healthcare Encounters:", nrow(encounters), "records\n")

- Healthcare Encounters: 958789 records

Code

cat("- Medical Conditions:", nrow(conditions), "records\n")

- Medical Conditions: 569790 records

Code

cat("- Healthcare Organizations:", nrow(organizations), "facilities\n")

- Healthcare Organizations: 5719 facilities

Code

cat("- Healthcare Providers:", nrow(providers), "providers\n")

- Healthcare Providers: 5719 providers

Code

cat("- Insurance Claims:", nrow(claims), "claims\n")

- Insurance Claims: 1504482 claims

The dataset represents a comprehensive synthetic healthcare ecosystem including patient demographics, medical encounters, diagnoses, treatments, and financial transactions across 5719 healthcare organizations in Massachusetts.

Healthcare Trends Over Time (1920-2024)

Long-term Growth Patterns

Code

# Healthcare encounters over time
encounters_by_month <- encounters |>
  mutate(year_month = floor_date(start, "month")) |>
  count(year_month, name = "encounters") |>
  filter(!is.na(year_month))

p1 <- ggplot(encounters_by_month, aes(x = year_month, y = encounters)) +
  geom_line(color = "steelblue", linewidth = 1.2) +
  geom_smooth(method = "loess", se = FALSE, color = "red", alpha = 0.7) +
  labs(
    title = "Healthcare Encounters Over Time",
    subtitle = "Monthly count with trend line",
    x = "Date",
    y = "Number of Encounters"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# New medical conditions over time
conditions_by_month <- conditions |>
  mutate(year_month = floor_date(start, "month")) |>
  count(year_month, name = "new_conditions") |>
  filter(!is.na(year_month))

p2 <- ggplot(conditions_by_month, aes(x = year_month, y = new_conditions)) +
  geom_line(color = "darkgreen", linewidth = 1.2) +
  geom_smooth(method = "loess", se = FALSE, color = "orange", alpha = 0.7) +
  labs(
    title = "New Medical Conditions Diagnosed Over Time",
    subtitle = "Monthly count with trend line",
    x = "Date",
    y = "Number of New Conditions"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Healthcare claims over time
claims_by_month <- claims |>
  mutate(year_month = floor_date(servicedate, "month")) |>
  group_by(year_month) |>
  summarise(total_claims = n(), .groups = "drop") |>
  filter(!is.na(year_month))

p3 <- ggplot(claims_by_month, aes(x = year_month, y = total_claims)) +
  geom_line(color = "purple", linewidth = 1.2) +
  geom_smooth(method = "loess", se = FALSE, color = "gold", alpha = 0.7) +
  labs(
    title = "Healthcare Claims Filed Over Time",
    subtitle = "Monthly count with trend line",
    x = "Date",
    y = "Number of Claims"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display plots
print(p1)

Long-term healthcare trends showing exponential growth in encounters, conditions, and claims

Code

print(p2)

Code

print(p3)

The long-term analysis reveals exponential growth patterns across all healthcare metrics:

Healthcare Encounters: Peak activity around 2024 with over 6292 encounters per month
New Conditions: Similar exponential pattern with steep growth after 2000
Insurance Claims: Most dramatic growth, reaching over 8151 claims per month

This growth pattern likely reflects both population expansion and increased healthcare system utilization over time.

Seasonal Patterns Analysis

Overall Seasonal Trends (2010-2024)

Code

# Analyze seasonal patterns in encounters
encounters_seasonal <- encounters |>
  mutate(
    month = lubridate::month(start, label = TRUE),
    year = lubridate::year(start),
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Winter",
      month %in% c("Mar", "Apr", "May") ~ "Spring",
      month %in% c("Jun", "Jul", "Aug") ~ "Summer",
      month %in% c("Sep", "Oct", "Nov") ~ "Fall"
    )
  ) |>
  filter(year >= 2010) |>
  count(month, season, name = "encounters")

# Analyze seasonal patterns in conditions
conditions_seasonal <- conditions |>
  mutate(
    month = lubridate::month(start, label = TRUE),
    year = lubridate::year(start),
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Winter",
      month %in% c("Mar", "Apr", "May") ~ "Spring",
      month %in% c("Jun", "Jul", "Aug") ~ "Summer",
      month %in% c("Sep", "Oct", "Nov") ~ "Fall"
    )
  ) |>
  filter(year >= 2010) |>
  count(month, season, name = "conditions")

# Combined visualization
monthly_combined <- encounters_seasonal |>
  left_join(
    conditions_seasonal,
    by = c("month", "season"),
    suffix = c("_encounters", "_conditions")
  ) |>
  select(month, encounters, conditions) |>
  pivot_longer(
    cols = c(encounters, conditions),
    names_to = "metric",
    values_to = "count"
  )

p_seasonal <- ggplot(
  monthly_combined,
  aes(x = month, y = count, color = metric, group = metric)
) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3) +
  scale_color_manual(
    values = c("encounters" = "steelblue", "conditions" = "darkgreen"),
    labels = c("Healthcare Encounters", "New Conditions")
  ) +
  labs(
    title = "Seasonal Patterns: Encounters vs. New Conditions (2010-2024)",
    subtitle = "Comparing monthly patterns",
    x = "Month",
    y = "Total Count",
    color = "Metric"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print(p_seasonal)

Seasonal patterns in healthcare encounters and medical conditions showing spring peaks

Code

# Seasonal summary statistics
seasonal_summary <- encounters_seasonal |>
  left_join(
    conditions_seasonal,
    by = c("month", "season"),
    suffix = c("_encounters", "_conditions")
  ) |>
  group_by(season) |>
  summarise(
    total_encounters = sum(encounters),
    total_conditions = sum(conditions),
    avg_encounters_per_month = round(mean(encounters), 0),
    avg_conditions_per_month = round(mean(conditions), 0),
    .groups = "drop"
  ) |>
  arrange(desc(total_encounters))

kable(
  seasonal_summary,
  caption = "Seasonal Healthcare Activity Summary (2010-2024)"
)

Seasonal Healthcare Activity Summary (2010-2024)
season	total_encounters	total_conditions	avg_encounters_per_month	avg_conditions_per_month
Spring	107989	63390	35996	21130
Winter	100221	60192	33407	20064
Summer	98376	57419	32792	19140
Fall	93611	55472	31204	18491

Seasonal patterns in healthcare encounters and medical conditions showing spring peaks

Key Seasonal Findings

Spring dominance is evident across both encounters and conditions:

Spring shows the highest activity with 107989 total encounters
March consistently shows peak monthly activity
Fall represents the lowest seasonal activity period
Both metrics follow nearly identical seasonal patterns, indicating systematic healthcare utilization trends

Organizational Variations in Seasonal Patterns

Healthcare Organization Analysis

Code

# Get top organizations and analyze their seasonal patterns
top_orgs <- encounters |>
  filter(lubridate::year(start) >= 2010) |>
  count(organization, sort = TRUE) |>
  head(8) |>
  pull(organization)

org_details <- organizations |>
  filter(id %in% top_orgs) |>
  select(id, name, city) |>
  mutate(short_name = str_trunc(name, width = 25))

org_seasonal <- encounters |>
  filter(
    lubridate::year(start) >= 2010,
    organization %in% top_orgs
  ) |>
  mutate(
    month = lubridate::month(start, label = TRUE),
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Winter",
      month %in% c("Mar", "Apr", "May") ~ "Spring",
      month %in% c("Jun", "Jul", "Aug") ~ "Summer",
      month %in% c("Sep", "Oct", "Nov") ~ "Fall"
    )
  ) |>
  left_join(org_details, by = c("organization" = "id")) |>
  count(organization, short_name, month, season, name = "encounters")

# Line plot by organization
p_org_seasonal <- ggplot(
  org_seasonal,
  aes(x = month, y = encounters, color = short_name, group = short_name)
) +
  geom_line(linewidth = 1.1, alpha = 0.8) +
  geom_point(size = 2, alpha = 0.7) +
  labs(
    title = "Seasonal Healthcare Patterns by Organization (2010-2024)",
    subtitle = "Monthly encounters across top 8 healthcare organizations",
    x = "Month",
    y = "Number of Encounters",
    color = "Organization"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom"
  ) +
  guides(color = guide_legend(ncol = 2))

print(p_org_seasonal)

Seasonal patterns vary significantly across healthcare organizations

Code

# Seasonal intensity heatmap
org_seasonal_summary <- org_seasonal |>
  group_by(short_name, season) |>
  summarise(
    total_encounters = sum(encounters),
    .groups = "drop"
  ) |>
  group_by(short_name) |>
  mutate(
    org_avg = mean(total_encounters),
    seasonal_index = round((total_encounters / org_avg) * 100, 0)
  ) |>
  ungroup()

p_heatmap <- ggplot(
  org_seasonal_summary,
  aes(x = season, y = reorder(short_name, org_avg), fill = seasonal_index)
) +
  geom_tile(color = "white", size = 0.5) +
  geom_text(
    aes(label = seasonal_index),
    color = "black",
    size = 3,
    fontface = "bold"
  ) +
  scale_fill_gradient2(
    low = "lightblue",
    mid = "white",
    high = "orange",
    midpoint = 100,
    name = "Seasonal\nIndex"
  ) +
  labs(
    title = "Seasonal Intensity Heatmap by Healthcare Organization",
    subtitle = "Index: 100 = average, >100 = above average, <100 = below average",
    x = "Season",
    y = "Healthcare Organization"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5))

print(p_heatmap)

Organizational Seasonal Variation

Code

org_variation <- org_seasonal_summary |>
  group_by(short_name) |>
  summarise(
    max_season_index = max(seasonal_index),
    min_season_index = min(seasonal_index),
    seasonal_variation = max_season_index - min_season_index,
    .groups = "drop"
  ) |>
  arrange(desc(seasonal_variation))

kable(
  org_variation,
  caption = "Healthcare Organizations Ranked by Seasonal Variation"
)

Healthcare Organizations Ranked by Seasonal Variation
short_name	max_season_index	min_season_index	seasonal_variation
MAT-SU REGIONAL MEDICA…	125	83	42
GALEN HOSPITAL ALASKA …	118	84	34
MAT SU VALLEY MEDICAL …	109	86	23
UHS OF PHOENIX LLC	109	91	18
ALASKA SPECIALTY HOSPI…	107	93	14
PROVIDENCE HEALTH & SE…	107	94	13
FAIRBANKS MEMORIAL HOS…	107	96	11
ALASKA PSYCHIATRIC INS…	104	97	7

Cape Cod Hospital shows the highest seasonal variation (42 points), likely due to its tourist location, while Lahey Hospital & Medical Center maintains the most consistent year-round utilization (7 points variation).

Healthcare Service Type Analysis

Service Categories and Seasonal Patterns

Code

# Define care types based on encounter descriptions
care_type_mapping <- list(
  "Preventive Care" = c(
    "Well child visit (procedure)",
    "General examination of patient (procedure)",
    "Encounter for check up (procedure)",
    "Administration of vaccine to produce active immunity (procedure)"
  ),

  "Acute Care" = c(
    "Encounter for problem (procedure)",
    "Encounter for symptom (procedure)",
    "Emergency room admission (procedure)",
    "Urgent care clinic (environment)"
  ),

  "Specialized Care" = c(
    "Prenatal visit (regime/therapy)",
    "Ophthalmic examination and evaluation (procedure)",
    "Outpatient procedure (procedure)",
    "Telemedicine consultation with patient (procedure)"
  ),

  "Follow-up Care" = c(
    "Follow-up encounter (procedure)",
    "Patient encounter procedure (procedure)",
    "Consultation for treatment (procedure)"
  )
)

categorize_encounter <- function(description) {
  for (category in names(care_type_mapping)) {
    if (description %in% care_type_mapping[[category]]) {
      return(category)
    }
  }
  return("Other Care")
}

care_type_seasonal <- encounters |>
  filter(lubridate::year(start) >= 2010) |>
  mutate(
    month = lubridate::month(start, label = TRUE),
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Winter",
      month %in% c("Mar", "Apr", "May") ~ "Spring",
      month %in% c("Jun", "Jul", "Aug") ~ "Summer",
      month %in% c("Sep", "Oct", "Nov") ~ "Fall"
    ),
    care_type = map_chr(description, categorize_encounter)
  ) |>
  filter(care_type != "Other Care") |>
  count(care_type, month, season, name = "encounters")

p_care_types <- ggplot(
  care_type_seasonal,
  aes(x = month, y = encounters, color = care_type, group = care_type)
) +
  geom_line(linewidth = 1.3, alpha = 0.8) +
  geom_point(size = 3, alpha = 0.9) +
  scale_color_manual(
    values = c(
      "Preventive Care" = "darkgreen",
      "Acute Care" = "red",
      "Specialized Care" = "purple",
      "Follow-up Care" = "orange"
    )
  ) +
  labs(
    title = "Seasonal Patterns by Healthcare Service Type (2010-2024)",
    subtitle = "Monthly encounters across different types of medical care",
    x = "Month",
    y = "Number of Encounters",
    color = "Care Type"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom"
  )

print(p_care_types)

Different healthcare service types show distinct seasonal patterns

Medical Condition Categories

Code

seasonal_conditions <- conditions |>
  filter(lubridate::year(start) >= 2010) |>
  mutate(
    month = lubridate::month(start, label = TRUE),
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Winter",
      month %in% c("Mar", "Apr", "May") ~ "Spring",
      month %in% c("Jun", "Jul", "Aug") ~ "Summer",
      month %in% c("Sep", "Oct", "Nov") ~ "Fall"
    ),
    condition_category = case_when(
      str_detect(description, "viral|Viral|bronchitis|pharyngitis|sinusitis") ~
        "Respiratory/Viral",
      str_detect(description, "dental|Dental|gingivitis|Gingivitis|caries") ~
        "Dental",
      str_detect(description, "stress|Stress|anxiety|depression|isolation") ~
        "Mental Health",
      str_detect(description, "employment|Employment|labor force") ~
        "Social/Occupational",
      TRUE ~ "Other"
    )
  ) |>
  filter(condition_category != "Other") |>
  count(condition_category, month, season, name = "conditions")

p_conditions <- ggplot(
  seasonal_conditions,
  aes(
    x = month,
    y = conditions,
    color = condition_category,
    group = condition_category
  )
) +
  geom_line(linewidth = 1.3, alpha = 0.8) +
  geom_point(size = 3, alpha = 0.9) +
  scale_color_manual(
    values = c(
      "Respiratory/Viral" = "blue",
      "Dental" = "brown",
      "Mental Health" = "darkred",
      "Social/Occupational" = "darkgreen"
    )
  ) +
  labs(
    title = "Seasonal Patterns by Medical Condition Category (2010-2024)",
    subtitle = "Monthly new diagnoses by condition type",
    x = "Month",
    y = "Number of New Conditions",
    color = "Condition Category"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom"
  )

print(p_conditions)

Medical condition categories show varying seasonal sensitivities

Service Type Seasonal Variation Summary

Code

care_type_summary <- care_type_seasonal |>
  group_by(care_type, season) |>
  summarise(total = sum(encounters), .groups = "drop") |>
  group_by(care_type) |>
  mutate(
    avg_seasonal = mean(total),
    seasonal_index = round((total / avg_seasonal) * 100, 0)
  ) |>
  select(care_type, season, seasonal_index) |>
  pivot_wider(names_from = season, values_from = seasonal_index)

kable(
  care_type_summary,
  caption = "Seasonal Index by Healthcare Service Type (100 = average)"
)

Seasonal Index by Healthcare Service Type (100 = average)
care_type	Fall	Spring	Summer	Winter
Acute Care	97	101	101	101
Follow-up Care	80	114	90	117
Preventive Care	93	115	97	95
Specialized Care	98	101	101	100

Code

care_variation <- care_type_summary |>
  mutate(
    max_index = pmax(Fall, Spring, Summer, Winter, na.rm = TRUE),
    min_index = pmin(Fall, Spring, Summer, Winter, na.rm = TRUE),
    variation = max_index - min_index
  ) |>
  arrange(desc(variation)) |>
  select(care_type, variation, max_index, min_index)

kable(
  care_variation,
  caption = "Healthcare Service Types Ranked by Seasonal Variation"
)

Healthcare Service Types Ranked by Seasonal Variation
care_type	variation	max_index	min_index
Follow-up Care	37	117	80
Preventive Care	22	115	93
Acute Care	4	101	97
Specialized Care	3	101	98

Key Findings and Strategic Implications

Primary Insights

Exponential Healthcare Growth: All metrics show dramatic increases, particularly after 2000, with peak activity in 2024.
Consistent Spring Peak Pattern: Spring (March-May) consistently shows 10-15% higher activity than the annual average across all healthcare metrics.
Organizational Variation: Healthcare facilities experience 14-35 point variations in seasonal demand, with tourist-area facilities showing the highest variation.
Service-Specific Patterns:
- Follow-up Care shows highest seasonal variation (30-point spread)
- Acute Care remains most stable year-round (5-point spread)
- Preventive Care peaks strongly in spring (22-point spread)
Condition-Specific Seasonality: Social/occupational issues and dental conditions show the strongest spring peaks.

Strategic Recommendations

For Healthcare Administrators: - Plan for 15-20% capacity increases during spring months (March-May) - Schedule maintenance and training during fall low-demand periods - Implement flexible staffing models for high-variation organizations

For Resource Planning: - Follow-up and preventive care services need the most seasonal flexibility - Emergency and acute care services can maintain consistent year-round staffing - Spring represents peak demand for most elective and preventive services

For Financial Planning: - Budget for highest utilization in Q1/Q2 of each year - Consider seasonal pricing models for non-urgent services - Plan cash flow around spring healthcare activity surges

Data Limitations

This analysis is based on synthetic healthcare data and should be interpreted as illustrative of potential patterns rather than definitive healthcare trends. Real-world applications should validate these patterns with actual healthcare utilization data.

Technical Appendix

Data Sources

Dataset: Synthetic healthcare data (Synthea-generated)
Time Period: Primary analysis focus on 2010-2024
Geographic Scope: Massachusetts healthcare facilities
Data Volume: 70,630+ healthcare encounters across 830+ organizations

Methodology

Seasonal Analysis: Monthly aggregation with four-season groupings
Trend Analysis: LOESS smoothing for long-term patterns
Organizational Comparison: Seasonal index calculation (Organization average = 100)
Service Categorization: Rule-based classification of encounter types

Software and Packages

R version: R version 4.5.2 (2025-10-31)
Key packages: tidyverse, data.table, lubridate, ggplot2
Analysis Platform: Positron IDE