Supercharge Your QI Program With Run Charts

.title[
# Supercharge Your QI Program With Run Charts
]
.subtitle[
## R/Medicine 2022 | August 25
]
.author[
### Robert L. Lobato, MD, MS
]
.institute[
### Chief, Division of Multispecialty Anesthesiology<br>University of Nebraska Medical Center
]

---

# The Goal of QI is to Refine a Process

### Three Fundamental Steps

1. **Reduce variation** in how a process is performed by establishing a standardized practice using evidence-based guidelines

2. Deploy the standardized practice in a test environment and **measure improvement**

3. Expand adoption and **sustain improvements** over time

---

# The Goal of QI is to Refine a Process

<br>

--
.center[

## 1. How do you know that your QI program is working?

]

<br>

--
.center[

## 2. How do you communicate your progress to stakeholders?

]

---

.center[
# Run Charts<span style="color:#666666"> are an answer<br>to both these questions</span> 
]
---
background-image: url("images/concept_run_chart.png")
background-position: 50% 90%
background-size: 70%

# What Are Run Charts?

.center[
### A graphical method for visualizing **process variation**<br>and identifying trends or **changes in performance**
]

???

ggpomological theme by Garrick Aden-Buie
---

# What Are Run Charts?

### Developed in the 1920s by Walter Shewhart of Bell Labs

--
- Manufacturing

???

Toyota 1950s. Toyota Production System (TPS)

--
- Business Operations (1990s)

???

Institute for Healthcare Improvement
--

---
background-image: url("images/basic_run_chart.png")
background-position: 50% 100%
background-size: 70%

# How Do We Construct a Run Chart?

--
1. Plot an **outcome of interest** against a **relevant time interval**

--
2. Calculate and plot the **median** for the sample

???

It sounds too simple to be useful, right?

Can use graph paper, Excel, or R!

---
background-image: url("images/basic_run_chart.png")
background-position: 50% 100%
background-size: 70%

# How Can Run Charts Improve QI?

1. Illustrating **variation** in an existing process

2. Detecting **changes in performance** over time

---
background-image: url("images/basic_run_chart_lower_variation.png")
background-position: 50% 100%
background-size: 70%

# Variation

### Difference between an ideal and an observed process

--
- How consistent is a process?

--
- Graphically, **dispersion around a central tendency**

---
background-image: url("images/basic_run_chart_lower_variation_example.png")
background-position: 50% 100%
background-size: 70%

# Why is Variation Important?

--
1. **Variation** can result from differences in any part of a process

--
2. QI seeks to **minimize variation** wherever possible

---
background-image: url("images/basic_run_chart_with_shift.png")
background-position: 50% 100%
background-size: 70%

# Detecting a Change in Performance

- Pre-define number of **consecutive events** above or below the median that constitute a **run**

- When a run is identified, a **new median** is calculated

???

Tremendously simple, but amazingly powerful

---
background-image: url("images/basic_run_chart_with_shift.png")
background-position: 50% 100%
background-size: 70%

# Defining a Run

- Pre-specified number of **consecutive events** above or below the median

--
- Specific to each individual QI program, based on  **"meaningful change"**

???
Depends (in part) on the frequency of observations
---

class:center,middle

# Example from Clinical Practice

---

# University of Nebraska Medical Center<br>Department of Anesthesiology

.pull-left.w65[
- Level 1 trauma center in Omaha

- Nebraska's only anesthesiology training program

- Anesthesiologists, CRNAs, and anesthesiology residents

- ~25,000 adult anesthetics per year
]

.pull-right.w30[
![](images/unmc_dusk.jpg)
]

???

The clinical example I want to use comes from my clinical work at UNMC

---
background-image: url("images/nausea.png")
background-position: 85% 50%
background-size: 20%

class:highlight-last-item

# Post Operative Nausea and Vomiting<br>(PONV)

- Common phenomenon after anesthesia

--
- Important patient-centered outcome

--
- Associated with increased healthcare cost

--
- Well-recognized risk factors

--
- Evidence-based society guidelines for<br>**preventative treatment**

???

Excellent candidate for a QI program

---

# PONV as a CMS MIPS Measure

**CMS:** Center for Medicare and Medicaid Services<br>
**MIPS:** Merit-Based Incentive Payment System (Part of the Quality Payment Program)
---

# UNMC MIPS 430 Performance

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    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">academic_year</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">surgery_month</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">success_rate</th>
    </tr>
  </thead>
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<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-07-01</td>
<td class="gt_row gt_right">89.2%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-08-01</td>
<td class="gt_row gt_right">88.8%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-09-01</td>
<td class="gt_row gt_right">89.6%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-10-01</td>
<td class="gt_row gt_right">92.3%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-11-01</td>
<td class="gt_row gt_right">92.5%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2020-12-01</td>
<td class="gt_row gt_right">89.7%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2021-01-01</td>
<td class="gt_row gt_right">93.1%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2021-02-01</td>
<td class="gt_row gt_right">90.9%</td></tr>
    <tr><td class="gt_row gt_right gt_stub"></td>
<td class="gt_row gt_center">2021</td>
<td class="gt_row gt_center">2021-03-01</td>
<td class="gt_row gt_right">93.0%</td></tr>
    <tr><td class="gt_row gt_right gt_stub gt_grand_summary_row gt_first_grand_summary_row">mean</td>
<td class="gt_row gt_center gt_grand_summary_row gt_first_grand_summary_row">—</td>
<td class="gt_row gt_center gt_grand_summary_row gt_first_grand_summary_row">—</td>
<td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">91.00%</td></tr>
    <tr><td class="gt_row gt_right gt_stub gt_grand_summary_row gt_last_summary_row">median</td>
<td class="gt_row gt_center gt_grand_summary_row gt_last_summary_row">—</td>
<td class="gt_row gt_center gt_grand_summary_row gt_last_summary_row">—</td>
<td class="gt_row gt_right gt_grand_summary_row gt_last_summary_row">90.87%</td></tr>
  </tbody>
  
  
</table>
</div>

???

Outcome of interest. Relevant time interval. Job for a Run Chart.
---

# {runcharter}<span style="color:#666666"> by John MacKintosh</span>

.pull-left.w75[
![](images/mackintosh_talk_screencap.png)

### R/Medicine 2020 Lightning Talk
]

.pull-right.w20[
<br>
<br>
![](images/mackintosh_talk_qr.png)
]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
*   datecol = surgery_month,
    yval = success_rate,
    grpvar = plot_group,
    med_rows = 9,
    runlength = 3,
    direction = "both" 
  )
```

]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
    datecol = surgery_month,
*   yval = success_rate,
    grpvar = plot_group,
    med_rows = 9,
    runlength = 3,
    direction = "both" 
  )
```

]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
    datecol = surgery_month,  
    yval = success_rate,
    grpvar = plot_group,
*   med_rows = 9,
    runlength = 3,
    direction = "both" 
  )
```

]

.pull-right[
<br>
## Variable containing <br>**number of observations**<br>used to calculate<br>initial baseline
]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
    datecol = surgery_month,  
    yval = success_rate, 
    grpvar = plot_group,
    med_rows = 9, 
*   runlength = 3,
    direction = "both" 
  )
```

]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
    datecol = surgery_month,
    yval = success_rate,
    grpvar = plot_group,
    med_rows = 9,
    runlength = 3,
*   direction = "both"
  )
```

]

---

```r
library(runcharter)

runchart_output <- ponv_data_baseline %>% 
  runcharter(
    datecol = surgery_month,  
    yval = success_rate,
*   grpvar = plot_group,
    med_rows = 9,
    runlength = 3,
    direction = "both" 
  )
```

]

---

# <span style="color:black">{runcharter}</span> Output

.pull-left.w65[

```
## $runchart
```

![](index_files/figure-html/unnamed-chunk-13-1.png)

```
## 
## $sustained
##    plot_group    median start_date   end_date  extend_to run_type
## 1:          1 0.9087302 2020-07-01 2021-03-01 2021-04-01 baseline
```
]

.pull-right.w30[
  <br>
  1. ggplot run chart
  <br>
  <br>
  2. Calculation of medians<br>(with rebasing)
]

---

???

Very nice plot. Sensible number of decimal places. Identification of dates. Faceting label.

---

# Baseline Performance on MIPS 430

.pull-left.w60[
![](index_files/figure-html/unnamed-chunk-15-1.png)
]

.pull-right.w35[

- Stable process

- **"Common cause"** variation

]

???

From a QI point of view we see

---

# Adding <span style="color:black">{ggplot}</span> Detail to Run Charts

```r
*runchart_output$runchart + # <- this is a ggplot object
  facet_null() +
  theme_ipsum_rc() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(date_labels = "%b %Y") +
  theme(panel.grid.minor = element_blank(),
        panel.grid.major = element_blank()) +
  labs(title = "MIPS 430 Success Rate by Month",
       subtitle = "Prevention of PONV - Combination Therapy")
```

---

# Adding <span style="color:black">{ggplot}</span> Detail to Run Charts

```r
runchart_output$runchart +
* facet_null() +                                                                # <- remove faceting
* theme_ipsum_rc() +                                                            # <- apply a theme
* scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +           # <- reformat axes
* scale_x_date(date_labels = "%b %Y") +                                         # <- reformat axes
* theme(panel.grid.minor = element_blank(),
*       panel.grid.major = element_blank()) +
* labs(title = "MIPS 430 Success Rate by Month",                                # <- add title & subtitle
*      subtitle = "Prevention of PONV - Combination Therapy")                   # <- add title & subtitle
```

---

---

# Department-Wide QI Program

- Large-group education through departmental **Grand Rounds**

- Included as a quarterly metric in our **Value-Based Quality Incentive** program

---

# First Quarter Performance

![](index_files/figure-html/runchart-initial-1.png)

---

```r
runchart_output_initial$runchart +
  facet_null() +
  theme_ipsum_rc() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(date_labels = "%b %Y") +
  theme(panel.grid.minor = element_blank(),
        panel.grid.major = element_blank()) +
  labs(title = "MIPS 430 Success Rate by Month",
       subtitle = "Prevention of PONV - Combination Therapy") +
* annotate("rect", xmin = ymd("2020-07-01"), xmax = ymd("2021-04-05"),
*          ymin = -Inf, ymax = Inf, alpha = 0.15) +
* geom_vline(xintercept = ymd("2021-04-05")) +
* annotate("text", x = ymd("2020-11-15"), y = 0.89,
*          label = "Historical Data", family = "Roboto Condensed") +
* annotate("text", x = ymd("2021-04-10"), y = 0.89,
*          hjust = 0, color = "#AD122A", parse=TRUE,
*          label = "bold('Start of QI Project')", family = "Roboto Condensed")
```

### _ggplot annotation layer geoms using custom fonts, colors, and parsed formatting._

# Super cool!

---

---

---

# Second Quarter Performance

---

---

???

geom_mark_ellipse from {ggforce}.
Draw attention to a subset of points in a plot

---

class:

# Sustainability Challenge

---

# Department-Wide QI Program <span style="color:black">Part II</span>

# 
--

- Large-group education through departmental **Grand Rounds**

- Included as a quarterly metric in our **Value-Based Quality Incentive** program

- Metric review at **Monthly Quality Conference**

- Individual-clinician level quarterly reports of **Metric Success Rate**

---

# Clinician-Level Performance Report

---

# Third and Fourth Quarter Performance

---

???
Increased intensity of feedback restored our performance gain.

New run identified in spring 2022. New median rebased

---
background-image: url("images/final_runchart_wide.gif")
background-position: 50% 90%
background-size: 45%

# <span style="color:black">Supercharging QI with</span> Run Charts

???
Accessible to diverse audience. No math involved

Algorithmic identification of runs is tremendously persuasive.

Communicate with your audience more effectively.

Themes, fonts, shading, annotation. Animation!

--
- **Intuitive interpretation** of progress and setbacks

--
- Display both **performance change** and **process variation** over time

--
- {runcharter} output customizable to **increase stakeholder engagement**

---

# Thank You!

### R Core Team

### R Foundation

### R Consortium

### Linux Foundation
]

### {ggplot}, {tidyverse}, {gt}

### Bob Rudis {hrbrthemes}

### Thousands of contributors!
]