Cedars_CRCs.qmd

---
title: "Beyond the Symptoms"
subtitle: "Reducing Emergency Department Overutilization by Broadening Homelessness Services"
author: "Luke Morris"
institute: "Cedars-Sinai Medical Center"
format: 
  revealjs:
    theme:
      - moon
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    logo: images/CSlogo3.png
from: markdown+emoji
editor: source
---

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::: {.column width="40%"}
### [AGENDA]{style="font-weight:500"}
:::

::: {.column width="60%"}
-   Homelessness Figures

-   The ED Care Model

-   Problems at the Intersection

-   A New Role

-   Evidence of Effect
:::
:::

::: footer
This author has no conflicts of interest to disclose
:::

::: {.notes}
Here’s what’s on tap:

A quick look at homelessness figures

The Role Emergency Departments are designed to meet

How those don’t two things don’t always line up

A new role we created to tackle those issues

And how that’s going
:::

## [Homelessness by the Numbers]{style="font-weight:500"} {.smaller}

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::: {.column width="45%"}
-   **United States** (\~pop. 331.5 million)

    -   580,466 People Experiencing Homelessness (PEH)

    -   0.175% of U.S. population

     

-   **California** (\~pop. 40 million)

    -   161,548 PEH

    -   0.408% of state population

    -   California makes up 11.9% of U.S. pop, but hosts 28% of all PEH in U.S.
:::

::: {.column width="55%"}
![](images/hmlsPcts.png)
:::
:::

::: footer
Figures via [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-results.html) and [HUD 2020 Annual Homelessness Assessment Report](https://www.huduser.gov/portal/sites/default/files/pdf/2020-AHAR-Part-1.pdf)
:::

::: {.notes}
I won’t drag you through all of these figures, but here are the highlights:

About 0.2% of the U.S. population was experiencing homelessness in 2020.

Post-COVID that number’s only gone up from the 580 thousand seen here.

Zooming in, 12 percent of Americans live in California

But 28 percent of all Americans experiencing homelessness live in the state
:::

## [Homelessness in LA]{style="font-weight:500"} {.smaller}

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::: {.column width="60%"}
-   **LA County** (\~pop. 10 million)

    -   25% of California's population but 41% of its PEH

    -   3% of U.S. population but 12% of its PEH

 

-   **LA City** (\~pop. 3.9 million)

    -   39% of county population but 62% of PEH

    -   2/3 in first episode of homelessness

    -   Estimated new 20,000 PEH amid COVID
:::

::: {.column width="40%"}
![](images/laFigs.png){fig-align="right"}
:::
:::

::: footer
Figures via [LAHSA 2020 Count](https://www.lahsa.org/news?article=726-2020-greater-los-angeles-homeless-count-results#:~:text=LOS%20ANGELES%2C%20CA%E2%80%94The%20Los,point%2Din%2Dtime%20count.) and [Union Rescue Mission in Skid Row](https://urm.org/)
:::

::: {.notes}
Zooming in even further, Los Angeles County is home to 25% of California’s population

but 41% of Californians experiencing homelessness reside in the county.

2 of every 5 LA County residents is within the City of LA, 

but 3 of every 5 people experiencing homelessness in the county resides in city limits.

And COVID is expected to have left another 20,000 Angelenos homeless.
:::

## [The Emergency Medicine Model]{style="font-weight:500"} {.smaller}

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::: {.column width="60%"}
-   A place for stabilization

    -   Get you stable enough to go back home

    -   or stable enough to transport to an inpatient unit

 

-   Problems with non-urgent utilization

    -   Overburdens department resources

    -   Increases wait for care for all patients

    -   Chair reaction of patients Leaving before they're even seen
:::

::: {.column width="40%"}
![](images/bandage.png){fig-align="right"}
:::
:::

::: {.notes}
With the background of homelessness established, let’s look at what Emergency Departments are designed to do:

It’s a community safety net for anyone needing aid. 

EMTALA ensures that.

But emergency departments are designed as more or less a midway point in your care journey: 

The idea is that you get stabilized enough to go back home or to be admitted.

Recent decades have seen the ED model tested and challenged by growing non-urgent ED utilization.

The results? 

The ED’s resources get spread thin, 

everybody’s wait time balloons with the influx of patients, 

and people leave without even being seen because they’re tired of waiting.
:::

## [Packed EDs]{style="font-weight:500"} {.smaller}

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-   Crowding and overflowing longstanding issues

-   Examples of low-acuity cases coming in

    -   prescription refills

    -   treatment of hypertension

    -   chronic elevated blood sugar

    -   behavioral health

-   Source of patient and staff harm

-   "COVID-19 has laid bare medicine's house of cards." ^\*^
:::

::: {.column width="35%"}
![](images/packedED.png){fig-align="right"}
:::
:::

::: footer
\* [Dr. Sharon Anoush Chekijian in U.S. News and World Report](https://www.usnews.com/news/health-news/articles/2021-10-19/er-crowding-threatens-a-health-care-house-of-cards) \| Graphic via [CDC](https://www.cdc.gov/mmwr/volumes/69/wr/mm6923e1.htm)
:::

::: {.notes}
I’m not telling you anything new here. 

We’ve all seen overflowing waiting rooms and gurneys lining hallways.

Those non-urgent cases are part of the culprit

Folks are trying to get their prescriptions refilled at the ED

Poorly regulated blood pressure or blood sugar cases pack the waiting room

and for many behavioral health patients, stopping by the ED essentially becomes part of their social routine.

All through this, staff are getting spread thin, 

that drain on energy and burnout puts both staff and patients at risk.

And these issues all existed BEFORE COVID.
:::

## [Homeless Care at Cedars-Sinai ED]{style="font-weight:500"} {.smaller}

-   7,956 visits by 3,194 PEH patients Feb 2020-Jan 2021

-   Roughly 2.49 visits per PEH patient

-   Many there for basic needs

    -   Food

    -   Shelter

    -   Primary care-level treatment

::: {.notes}
One of the patient groups often behind those low-acuity cases are those experiencing homelessness

Here’s what the homeless caseload looked like in our ED: 

February 2020 to January 2021, our neighbors experiencing homelessness visited nearly 8,000 times. 

The average here suggests 2-3 times per patient, 

but some were seen dozens or even more than 100 times.

Common chief complaints we see in those visits are food needs, 

seeking shelter and treatment for issues best handled by a PCP or urgent care.
:::

## [California SB1152]{style="font-weight:500"} {.smaller}

-   Passed in 2018, requires hospitals to document info about patients experiencing homelessness before discharging them.

    -   Services offered

        -   Transportation

        -   Meal

        -   Meds

        -   Vaccinations

        -   Weather-appropriate clothing

    -   Resources offered

        -   Clinical/Behavioral

        -   Follow-up with PCP

        -   Help getting health coverage

::: {.notes}
Rewind a little bit before COVID, 

California passed a law requiring hospitals to document the types of services and resources offered to patients experiencing homelessness before discharging them

Were they fed?

Did they get a ride to where they stay?

How about meds? Vaccinations? Help applying for insurance?

Were their clothes appropriate for the conditions outside?

Were they set up with a follow-up appointment with a PCP or behaviorist?

All these must be noted before discharge according to California law.
:::

## [A New Role is Born]{style="font-weight:500"} {.smaller}

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**Community Resource Coordinator**

-   Responsible for SB1152 checklist items

-   Help PEH patients find more sustainable support

-   Provide connections to community resources

    -   Shelter

    -   Staple needs

    -   Insurance

    -   Follow-up & specialty care appointments
:::

::: {.column width="40%"}
[![      Weihao Qu, CRC](images/weihao.png){fig-align="right"}](https://www.cedars-sinai.org/eoc/cedars-sinai-connects-patients-to-services-for-homelessness.html)
:::
:::

::: {.notes}
Obviously the status quo wasn’t meeting our homeless patients’ needs.

ED leadership started wondering if there was something being missed in the initial encounter 

that could limit the need for return visits. 

And somebody was going to need to track those new data points.

A generous donor was able to help the ED create a new role, 

the Community Resource Coordinator was charged with going beyond the symptoms with our patients experiencing homelessness.

If patients welcome a CRC’s help, the CRC works on connecting them with vital staple needs, or shelter, or health coverage, or follow-up 
care.

By the way, this is Weihao, who was one of our OG CRCs. 

A lot of the target variables you’re about to see are his brainchild. I just crunched them.
:::

## [How's It Going?]{style="font-weight:500"}

Are CRCs effective at reducing the use of emergency services for non-urgent matters while ensuring that homeless patients can access their basic needs?

 

Data gathered in fulfilling SB1152 needs used to measure role's impact

::: {.notes}
So let’s tackle this like a research question: 

Are CRCs effective at reducing the use of emergency services for non-urgent matters while ensuring that homeless patients can access their basic needs?

Luckily, we’ve got a dataset to help us figure it out.
:::

## [Studying the Impact]{style="font-weight:500"} {.smaller}

-   Diff-in-diff cohort model

    -   Visit rates of patients experiencing homelessness

    -   Split by whether they were seen by a CRC during their index visit

-   Dependent variables

    -   Time interval between visits (in days)

    -   Cost of stay

    -   Length of stay (LOS)

-   Differences in the populations were tested for statistical significance using a Welch's t-test

::: {.notes}
Nerd goggles on.

We tested a diff-in-diff cohort model 

to compare the visit rate of patients experiencing homelessness. 

One group saw a CRC at their index visit, 

one group didn’t see a CRC on that visit.

Across groups we compared how long it took a patient to return to the ED, the cost of their stay, and the length of their stay and ran 
each through a t-test.
:::

## [What We Found]{style="font-weight:500"} {.smaller}

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-   A two-sample t-test demonstrated statistical significance (p = 0.0014)

-   22.82% increase to \# of days elapsed before a patient returned to the ED after visiting with a CRC

-   95% confidence interval: 2.6-10.9 days larger gap between ED visits
:::

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![](images/overallGap.png){fig-align="right"}
:::
:::

::: {.notes}
When it came to gap between return visits, we indeed found statistical significance. 

Patients who saw a CRC took an extra 3-11 days to return compared with those who didn’t.
:::

## [90-Day Return Rate]{style="font-weight:500"} {.scrollable}

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![](images/90d.png){style="max-width: 95%;" fig-align="right"}
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**Revisit rate:**

:arrow_down:35%

**95% CI:**

0.8-1.4 fewer visits

**p:** \<0.0001
:::
:::

::: {.notes}
We compared visits per next 90 days and next 180 days. 

Patients who met with a CRC had 1-2 fewer visits in that window of time.
:::

## [180-Day Return Rate]{style="font-weight:500"} {.scrollable}

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![](images/180d.png){style="max-width: 95%;;" fig-align="right"}
:::

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**Revisit rate:**

:arrow_down:35%

**95% CI:**

1.1-2.1 fewer visits

**p:** \<0.0001
:::
:::

::: {.notes}
We compared visits per next 90 days and next 180 days. 

Patients who met with a CRC had 1-2 fewer visits in that window of time.
:::

## [Other Results]{style="font-weight:500"} {.smaller}

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-   Difference in cost of care

    -   p = 0.1050

    -   Not statistically significant

-   Difference in length of stay in the ED

    -   p = 0.1053

    -   Not statistically significant

-   Makes sense considering CRC can't really affect those figures
:::

::: {.column width="40%"}
![](images/scott.png){fig-align="right"}
:::
:::

::: {.notes}
Those differences for cost and length of stay didn’t quite work out. 

We couldn’t achieve statistical significance there, 

but upon reflection that made sense. 

A CRC doesn’t have any sway in what treatment you receive in the ED or when.
:::

## [Understanding the Results]{style="font-weight:500"} {.smaller}

-   Increased gap between ED visits

-   Lowered rate of ED visits over 90 and 180 days

-   Implications

    -   Increased utilization of community resources may be decreasing frequency of needs that draw patients experiencing homelessness to ED

    -   Reduced burden by lower-urgency needs on ED resources

-   Possible effects

    -   Shorter wait times

    -   Fewer patients leaving without being seen

    -   Greater focus on other patients with more acute needs

::: {.notes}
So yay, we got a few p-values to go our way. But what does that mean?

The big win comes in the fact we know these patients are getting connected to the community resources they need. 

They’re getting opportunities at follow-up care that goes beyond the symptoms.

Meanwhile, in the ED, the possible gains are cuts in wait time, 

fewer patients heading for the door before being seen, 

and emergency providers getting to do more of what they entered the field to do: Save lives in immediate danger
:::

## [Beyond the First Year ...]{style="font-weight:500"} {.smaller}

-   Slides so far showed Feb 2020-Jan 2021, but the work continues.

-   Results Feb 2020-Apr 2023

    -   20,669 visits by 8,902 PEH patients

::: columns
::: {.column width="30%"}
#### Overall Avg Gap Between Visits {style="font-weight:500"}

![](images/overallGapSince.png)

:arrow_up: by 10.0-23.6 days
:::

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#### 90-Day Visit Rate {style="font-weight:500"}

![](images/90dSince.png)

:arrow_down: by 0.7-1.1 visits
:::

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#### 180-day Visit Rate {style="font-weight:500"}

![](images/180dSince.png)

:arrow_down: by 1.2-1.8 visits
:::
:::

::: {.notes}
We’ve continued monitoring results beyond that first year of the CRC program, 

and things continue to look promising for reducing repeat visits.
:::

## The Dataset {.smaller}

Patient Identifier

Encounter Identifier

Arrival Datetime

Departure Datetime

ED Navigator

Total Charges

Visits last 90 Days

Visits last 180 Days

## The Script {.scrollable .code-overflow-wrap}

```{r part1, eval=FALSE, warning=FALSE, echo=TRUE}
# Make relevant R packages available.

library(dtplyr)
library(tidyverse, warn.conflicts = FALSE)

# Read in Homeless Patient Discharge Checklist dataset from Excel.

patlog <- readxl::read_excel("~/SomeFolder/Homeless.xlsx")

# Convert variables to appropriate data type.

patlog$DOB <- ymd(patlog$DOB)
patlog$`Admission Date` <- ymd_hms(patlog$`Admission Date`)
patlog$`Discharge Date` <- ymd_hms(patlog$`Discharge Date`)
patlog$`Last Seen DATE` <- ymd_hms(patlog$`Last Seen DATE`)

# Ensure we're only looking at emergency visits

patlog <- patlog %>%
  filter(
        (Department == "ASAP EMERGENCY DEPT" 
         | Department == "MDRH EMERGENCY DEPT"))

# Set relevant dates

mindate <- date(min(patlog$`Discharge Date`, na.rm = TRUE))
maxdate <- date(max(patlog$`Discharge Date`, na.rm = TRUE))
lag90days <- date(max(patlog$`Discharge Date`, na.rm = TRUE) - days(90))
lag180days <- date(max(patlog$`Discharge Date`, na.rm = TRUE) - days(180))

# Declare those dates for readers

glue::glue("Before we get into determining whether CSMC\'s and MRDH\'s
           Community Resource Coordinators are having a statistically
           significant effect on the department, let\'s quickly look at
           their caseload from {month(mindate, label = TRUE)} 
           {day(mindate)}, {year(mindate)} to 
           {month(maxdate, label = TRUE)} {day(maxdate)}, 
           {year(maxdate)}.")

# Count the number of cases involving an ED Navigator (aka CRC).

patlog %>%
  group_by(metCRC = !is.na(`ED Navigator`)) %>%
  tally()

# Save those counts to their own variables

CRCtotal <- sum(!is.na(patlog$`ED Navigator`))
noCRCtotal <- sum(is.na(patlog$`ED Navigator`))

# Share results with readers

glue::glue("We can see that the CRCs had {CRCtotal} interactions with
           homeless patients. {noCRCtotal} homeless encounters did not 
           include a CRC interaction.")

# Create three new variables, one calculating days elapsed before 
# the patient was seen again after this visit,
# one counting up the number of times the patient was seen in the
# 90 days following this visit, and
# one counting up the number of times the patient was seen in the
# 180 days following this visit.

patlog <- patlog %>%
  group_by(Name) %>%
  mutate(nextVisit = lead(`Discharge Date`),
         daysUntilNextVisit = round(
           int_length(
             `Discharge Date`%--%nextVisit)/86400),
         visitsNext90Days = map_dbl(
           `Discharge Date`, 
           ~sum(`Discharge Date` %within% interval(., . + days(90)),
                na.rm = TRUE)) -1,
         visitsNext180Days = map_dbl(
           `Discharge Date`, 
           ~sum(`Discharge Date` %within% interval(., . + days(180)), 
                na.rm = TRUE)) -1) %>%
  ungroup()

# For every patient who saw a CRC during their visit, list the number
# of days between the current visit and their next one

sawCRC <- patlog %>%
  filter(!is.na(`ED Navigator`)) %>%
  drop_na(daysUntilNextVisit) %>%
  pull(daysUntilNextVisit)

# For every patient who did not see a CRC during their visit, list
# the number of days between the current visit and their next one

noCRC <- patlog %>%
  filter(is.na(`ED Navigator`)) %>%
  drop_na(daysUntilNextVisit) %>%
  pull(daysUntilNextVisit)

#Declare n-sizes for readers

glue::glue("In this test, for patients who returned after 
           encountering a CRC, n = {length(sawCRC)}. 
           For patients who returned after not
           encountering a CRC in their previous visit, 
           n = {length(noCRC)}.")

# Perform a t-test to determine if the difference in the two
# groups' average number of days between ED visits is 
# statistically significant

dayGapTest <- t.test(sawCRC, noCRC, conf.level = 0.95)

# Display raw test results

dayGapTest

#Calculations for visualization

gapInDays <- patlog %>%
  filter(is.na(daysUntilNextVisit) == FALSE) %>%
  mutate(visitedByCRC = !is.na(`ED Navigator`)) %>%
  select(visitedByCRC, daysUntilNextVisit) %>%
  group_by(visitedByCRC) %>%
  summarize(
    avgVisitGap = mean(daysUntilNextVisit), 
    n = n(), 
    sd = sd(daysUntilNextVisit)
    ) %>%
  mutate(se=sd/sqrt(n))

# Visualize results

ggplot(
  gapInDays, 
  aes(visitedByCRC, avgVisitGap, fill = visitedByCRC
      )
  ) +
  geom_col() +
  geom_errorbar(
    aes(
      x = visitedByCRC, 
      ymin = avgVisitGap - se, 
      ymax = avgVisitGap + se), 
    width = 0.2) +
  geom_text(aes(label = format(signif(avgVisitGap, digits = 3),
                               nsmall = 2)), vjust = 3.9, size = 8) +
  ggtitle("Average Gap Between ED Visits for Homeless Patients") +
  xlab("Patient Group") +
  ylab("Average Gap in Days") +
  scale_x_discrete(labels = c ("Wasn't Seen by CRC", "Saw CRC")) +
  scale_fill_manual(values = alpha(c("#B0B3B2", "#4EC3E0"), .6)) +
  theme_bw() +
  theme(
        legend.position = "none",
        plot.title = element_text(size = 17),
        axis.text = element_text(size = 14),
        axis.title = element_text(size = 16)
        )

# Explain the results

glue::glue("From this output, we can say that we're 95% confident
           that CRC interactions had an impact on the rate of 
           return for repeat homeless visitors seen between
           {month(mindate, label = TRUE)} 
           {day(mindate)}, {year(mindate)} to 
           {month(maxdate, label = TRUE)} {day(maxdate)}, 
           {year(maxdate)}, equal to somewhere between 
           {round(dayGapTest$conf.int[[1]], 1)} and 
           {round(dayGapTest$conf.int[[2]], 1)} extra days 
           between visits.")

# Since the next test requires a 90-day window and we have data for 
# Feb 1 2020 - April 30, 2023, we'll cut off visits at Jan 30 2023 to 
# ensure every observation has the necessary 90-day window

cutoff90days <- patlog %>%
  filter(`Discharge Date` < ymd(lag90days))

# For every patient who saw a CRC during their visit, list the number of
# times they had been seen in the ED over the 90 days after the
# current visit.

sawCRC90days <- cutoff90days %>%
  filter(!is.na(`ED Navigator`)) %>%
  pull(visitsNext90Days)

# For every patient who did not see a CRC during their visit, list the 
# number of times they had been seen in the ED over the 90 days 
# after the current visit.

noCRC90days <- cutoff90days %>%
  filter(is.na(`ED Navigator`)) %>%
  pull(visitsNext90Days)

#Declare n-sizes for readers

glue::glue("To look further, we'll compare the average number of 
           visits in a 90-day period for patients who encountered
           CRCs vs. those who didn't. Data used for this test is
           visits from {month(mindate, label = TRUE)} 
           {day(mindate)}, {year(mindate)} to 
           {month(lag90days, label = TRUE)} 
           {day(lag90days)}, {year(lag90days)}, 
           to allow for the appropriate 90-day measurement window.")

glue::glue("In this test, for ED encounters by patients who met with 
           a CRC, n = {length(sawCRC90days)}. For ED encounters by
           patients who hadn't met with a CRC, 
           n = {length(noCRC90days)}.")

# Perform a t-test to determine if the difference in the two groups'
# average number of visits over 90 days is statistically significant

test90days <- t.test(sawCRC90days, noCRC90days, conf.level = 0.95)

# Display raw test results

test90days

#Calculations for visualization

rate90Days <- cutoff90days %>%
  mutate(visitedByCRC = !is.na(`ED Navigator`)) %>%
  select(visitedByCRC, visitsNext90Days) %>%
  group_by(visitedByCRC) %>%
  summarize(
    rateOfVisits90 = mean(visitsNext90Days), 
    n = n(), 
    sd = sd(visitsNext90Days)) %>%
  mutate(se=sd/sqrt(n))
  
# Visualize results

ggplot(rate90Days, aes(visitedByCRC, rateOfVisits90, 
                       fill = visitedByCRC)) +
  geom_col() +
  geom_errorbar(
    aes(
      x = visitedByCRC,
      ymin = rateOfVisits90 - se,
      ymax = rateOfVisits90 + se),
    width = 0.2) +
  geom_text(
    aes(
      label = format(signif(rateOfVisits90, digits = 3), 
                     nsmall = 2)), 
    vjust = 3.9, 
    size = 8) +
  ggtitle("Rate of ED Visits per 90 Days for Homeless Patients") +
  xlab("Patient Group") +
  ylab("Rate of ED Visits per 90 Days") +
  scale_x_discrete(labels = c ("Wasn't Seen by CRC", "Saw CRC")) +
  scale_fill_manual(values = alpha(c("#B0B3B2", "#4EC3E0"), .6)) +
  theme_bw() +
  theme(
        legend.position = "none",
        plot.title = element_text(size = 18),
        axis.text = element_text(size = 14),
        axis.title = element_text(size = 16)
    )

# Explain the results

glue::glue("From these results, we're 95% confident that CRC interactions
           have a statistically significant impact on homeless patients' 
           rate of visits per 90 days, with patients who saw them being 
           seen somewhere between 
           {round(abs(test90days$conf.int[[2]]), 1)} 
           to {round(abs(test90days$conf.int[[1]]), 1)} 
           fewer times over the next 90 days.")

# For every patient who did not see a CRC during their visit, list the 
# number of times they had been seen in the ED over the 180 days after 
# the current visit. Since the next test requires a 180-day window and 
# we have data for Feb. 1, 2020 - Apr 30, 2023, we'll cut off visits 
# at Nov 1 2022 to ensure every observation has the necessary 180-day
# window

cutoff180days <- patlog %>%
  filter(`Discharge Date` < ymd(lag180days))

# For every patient who saw a CRC during their visit, list the number 
# of times they had been seen in the ED over the 180 days after the 
# current visit.

sawCRC180days <- cutoff180days %>%
  filter(!is.na(`ED Navigator`)) %>%
  drop_na(visitsNext180Days) %>%
  pull(visitsNext180Days)

# For every patient who did not see a CRC during their visit, list the
# number of times they had been seen in the ED over the 180 days after 
# the current visit.

noCRC180days <- cutoff180days %>%
  filter(is.na(`ED Navigator`)) %>%
  drop_na(visitsNext180Days) %>%
  pull(visitsNext180Days)

#Declare n-sizes for readers

glue::glue("Expanding the window to 180 days also demonstrates value in
           the CRC program. Data used for this test is visits from 
           {month(mindate, label = TRUE)} {day(mindate)}, {year(mindate)} 
           to {month(lag180days, label = TRUE)} {day(lag180days)}, 
           {year(lag180days)}, to allow for the appropriate 180-day 
           measurement window.")

glue::glue("In this test, for ED encounters by patients who met with
            a CRC, n = {length(sawCRC180days)}. For ED encounters by
            patients who hadn't met with a CRC, 
            n = {length(noCRC180days)}.")

# Perform a t-test to determine if the difference in the two groups' 
# average number of visits over 180 days is statistically significant

test180days <- t.test(sawCRC180days, noCRC180days, conf.level = 0.95)

# Display raw test results

test180days

#Calculations for visualization

rate180Days <- cutoff180days %>%
  mutate(visitedByCRC = !is.na(`ED Navigator`)) %>%
  select(visitedByCRC, visitsNext180Days) %>%
  group_by(visitedByCRC) %>%
  summarize(
    rateOfVisits180 = mean(visitsNext180Days), 
    n = n(), 
    sd = sd(visitsNext180Days)) %>%
  mutate(se=sd/sqrt(n))
  
# Visualize results

ggplot(rate180Days, 
       aes(visitedByCRC, rateOfVisits180, fill = visitedByCRC)) +
  geom_col() +
  geom_errorbar(aes(
                x = visitedByCRC,
                  ymin = rateOfVisits180 - se,
                  ymax = rateOfVisits180 + se),
                width = 0.2) +
  geom_text(aes(
    label = format(signif(rateOfVisits180, digits = 3), 
                   nsmall = 2)), 
    vjust = 3.9, 
    size = 8) +
  ggtitle("Rate of ED Visits per 180 Days for Homeless Patients") +
  xlab("Patient Group") +
  ylab("Rate of ED Visits per 180 Days") +
  scale_x_discrete(labels = c ("Wasn't Seen by CRC", "Saw CRC")) +
  scale_fill_manual(values = alpha(c("#B0B3B2", "#4EC3E0"), .6)) +
  theme_bw() +
  theme(
        legend.position = "none",
        plot.title = element_text(size = 18),
        axis.text = element_text(size = 14),
        axis.title = element_text(size = 16)
      )

# Explain the results

glue::glue("From these results, we're 95% confident that CRC interactions
           have a statistically significant impact on homeless patients' 
           rate of visits per 180 days, with patients who saw them being
           seen somewhere between 
           {round(abs(test180days$conf.int[[2]]), 1)} to 
           {round(abs(test180days$conf.int[[1]]), 1)} fewer times over
           the next 180 days.")
```

## The Report

[Let's take a look.](https://lukesmorris.com/Cedars_CRCs/demo%20CRC%20Report.html){preview-link="true"}

## . {background-image="images/questions.webp"}

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