# Presentation: 508297

## Model for Improvement

*Kevin Little, Ph.D., IHI*
*Corinna Nyquist, RN, BSN, Indian Health Council *
*Marni Kopenski, NP, Gerald Ignace Indian Health Center*
*Jana Towne, RN, BSN, Whiteriver Service Unit*

## Overview of the Model for Improvement

- Based on a presentation developed by
- Jerry Langley, IHI
- Cindy Hupke, IHI
- for
- Innovations in Planned Care for the Indian Health System

- Kevin Little, IHI
- 25 June 2008

**Notes:**
Diverse audience and this material is relevant to a range of people. 

## Agenda

- Context for IPC work
- Team example 1
- Theory and Concepts-1
- Team example 2
- Theory and Concepts-2
- Team example 3
- Q and A/Discussion
- Next Steps/Summary

## Blood Pressure PDSAPurpose/Objective of Cycle:To determine/assess our blood pressure techniques

*Corinna Nyquist*
*Indian Health Council*

## PLAN

- The Change:
*What are we testing?*  Taking blood pressures after a client has rested for 5 minutes vs. upon entry to clinic.
*Who are testing on?  *The next 5 clients.
*When are we testing?  *Today.
*Where are we testing?  *In the medical clinic.
- Our Prediction:
- We anticipate that BPs taken 5 minutes after the client has rested will be better than those taken when clients first arrive.

## PLAN cont.

- Data
*What data do we need to collect?  *BPs upon entry into the clinic and again 5 minutes after the client is roomed.
*Who will collect the data?  *The team.
*When will the data be collected?**  *Today at 3 pm.
*Where will the data be collected?*  In the medical clinic.

## DO (carry out the test)

*What was actually tested?  *Took BPs on 4 clients upon entering the clinic and repeated 5 minutes later after client had been sitting down.
*What happened?  *4 out of 4 BPs were lower after resting for 5 minutes.
*Observations?*  BPs decreased when clients were sedentary for 5 minutes.
*Problems?* Asked data entry staff what they do if there are 2 BPs – response was that they enter 1st BP taken.

## STUDY

- Blood pressures taken after client was seated for 5 minutes were substantially lower than those taken upon entry to the clinic.  Team realized that we needed to change our current process and complete BPs after client is roomed and has rested.

## ACT

*What changes should we make before the next test cycle?  *Meet with MA’s and nurses tomorrow morning and ask them to room the client and then complete any questionnaires and screens prior to doing vitals.  Also, we need to write protocol for data entry regarding two results for any given test.
*When will the next test cycle be?  *Tomorrow.
*Are we ready to implement the change?  *Yes – by 11-13-07.

## Your Observations

- What was effective?

- Opportunities to do better?

## Theory and Concepts-1

## To Be Successful at Improvement You Need the Following:

**Will** - for improvement
**Ideas** - for changes that will lead to improvement
**Execution** – a framework for action to adapt the changes to achieve improvement

**Notes:**
You need to change the system to get improvement but not all changes lead to improvement.

Execution is the hard part, based on our experience (and perhaps yours, too.)

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

- G. Langley et al. (1996),
*The Improvement Guide: *
*A Practical Approach to Enhancing Organizational Performance*,
- Jossey-Bass Publishers, San Francisco.

**Notes:**
The Model for Improvement provides a practical and effective framework to execution.

## Fundamental Questions for Improvement

- Fundamental Questions for Improvement

- What are we trying to accomplish?
- How will we know that a change is an improvement?
- What changes can we make that will result in an improvement?

**Notes:**
Asking these questions can provoke informed responses that set the stage for improvement.

THE ORDER OF THE 3 QUESTIONS MATTERS!  You gain more and more with each question, in sequence.

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

## An Aim Statement

- Whiteriver Service Unit seeks to fundamentally redesign its delivery of care for all patients in order to promote the health and wellness of the community, and to improve the prevention and treatment of chronic conditions.  We will improve health care processes and outcomes for each patient across the entire population in a cost effective manner.  This commitment is reflected in our strategic goal to “provide compassionate, quality health care that is timely and effective, that maximizes patient safety and that excels at best practices.”

## Some Guidance to Support the Aim

- We will actively partner with the White Mountain Apache community to collaborate in the design of patient-centered care processes in order to have the community take ownership of the changes.
- The initiative will develop care processes which focus on a positive patient and family members’ health care experience.
- These processes will be developed, tested, and packaged in such a manner that they can be spread throughout Whiteriver Hospital and Cibecue Health Center within the next 3 years.
- The team will define patient care process through the use of flowcharts; create a list of possible changes, and prioritize and implement the changes. A specific focus will address how to sustain improvements.
- The initial microsystem that will receive improvement focus is located in Whiteriver Hospital’s Outpatient Clinic A.  Core staff for the care team will include at a minimum, 1.0FTE Med Staff coverage (2 physicians and 2 physician assistants,) 2 RNs, 2 Certified Nursing Assistants, Medical Clerk, Pharmacist, Dietician, Lab Tech, Clinical Applications Coordinator and Patient Registration representative.  A patient panel of 1000 patients who represent all ages will be created.  These patients will participate as members of their own care teams.

## Some Goals to Support the Aim : (within 12 months; evaluated annually thereafter)

- Health improvement for this panel of patients (as measured by chronic condition and preventive services outcomes) by 100%.
- Patient and family experience improvement as evidenced by satisfaction scores to > 95 % and third next available appointment with the care team to < 1 month.
- Efficiency improvement as measured by decreased office visit cycle time to 60 minutes when pharmacy is included (40 minutes without pharmacy) and increased value-added face time to 40 min.
- Decrease in Urgent Care and Emergency visits by 50% per 1000 patients
- Changes will occur within existing funding resources.
- Create a process for identifying waste, and then reduce it in all forms.
- Patients who indicate that they feel confident managing their own conditions will increase to > 95%.

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

## IPC Measures (Fall 2007)

| Measurement Domain | Areas of Focus/Coverage |
| --- | --- |
| Clinical Prevention | Keeping current on intake screenings |
|  | Keeping current on cancer related screenings |
|  | Keeping current on Immunizations |
| Management and Prevention of Chronic Conditions | Control of Blood Pressure (Cardiovascular Disease) |
|  | Control of Lipids (Cardiovascular Disease) |
|  | Diabetes Care |
|  | Obesity |
|  | Asthma |
| Costs | Dollars spent outside primary care |
| Patient Experience | Experience and Efficiency |
|  | Activation |
|  | Satisfaction |
|  | Building relationships for care |
|  | Access |

## Slide 21

| Measurement
Domain |  | Areas of Focus/Coverage | Core Measure | Goal |
| --- | --- | --- | --- | --- |
| Clinical
Prevention | Keeping current on intake screenings |  | Intake Screening Bundle  | 70% |
|  | Keeping current on cancer related screenings |  | Cancer Screening Bundle | 70% |
|  | Keeping current on Immunizations |  | Childhood Immunizations  | 90% |
| Management and
Prevention
of Chronic
Conditions | Control of Blood Pressure (Cardiovascular Disease) |  | BP in Control Bundle | 70% |
|  | Control of Lipids (Cardiovascular Disease) |  | LDL in Control Bundle | 70% |
|  | Diabetes Care |  | Diabetes Comprehensive Care | 70% |
|  | Obesity |  | Nutrition Education for at risk populations | 60% |
|  | Obesity |  | Exercise Education for at risk populations | 60% |
|  | Asthma |  | New measure coming in 2008  |  |
| Costs | Dollars spent outside primary care |  | Number of Hospitalizations | 20% less |
|  | Dollars spent outside primary care |  | Number of ER and Urgent Care Visits | 40% less |
|  | Dollars spent outside primary care |  | Total Costs of Outside Services | 20% less |
| Patient
Experience | Waiting and Efficiency |  | Average Office Visit Cycle Time | 45 minutes |
|  | Activation |  | Percent of Patients with Self Management Goal Set  | 70% |
|  | Activation |  | Patient Confidence | 90% |
|  | Satisfaction |  | Patient Recommending Health Facility | 95% |
|  | Building relationships for care |  | Continuity of Care | 80% |
|  | Access |  | Third Next Available Appointment | Same week |

## Intake Screening Bundle

| Specific Measures inside the Intake Screening Bundle |  |  |
| --- | --- | --- |
| Measure | Numerator | Denominator |
| Alcohol Misuse Screening | Female patients screened for alcohol use, who have alcohol-related diagnoses, or who have received alcohol-related education or counseling during the Report Period, including refusals in the past year | Female Active Clinical patients ages 15 to 44 (child-bearing age). |
| Depression Screening | Patients screened for depression or diagnosed with mood disorder at any time during the Report Period, including documented refusals in past year. | Active Clinical patients ages 18 and older |
| Domestic/IPV Screening | Patients screened for or diagnosed with intimate partner (domestic) violence during the Report Period, including documented refusals in past year. | Female Active Clinical patients ages 15-40. |
| Tobacco Use and Assessment | Patients screened for tobacco use during the Report Period | Active Clinical patients ages 5 and older |
| BMI Assessed (Obesity Assessed) | All patients for whom BMI can be calculated, including refusals in the past year. | Active Clinical patients ages 2 through 74 |
| BP Assessed | Patients with BP values documented within the last year | All Active Clinical patients ages 20 and over, broken down by gender  |

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

## Some Change Ideas for IPC

- Empanelling
- Care team
- Use data to drive improvement
- Optimize use of HIT
- Remove waste
- Plan for every pt
- Segment care
- Reminders system
- Move work to appropriate licensure
- Reliable follow-up
- Max packing

- Proactive care across spectrum
- Self-Management
- Integration into community
- BH integrated into primary care
- Train workforce
- Transportation for pts
- Integration of traditional medicine
- Guidelines of care
- Advanced access
- Adjusting meds to control

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

## Model for Improvement

- What are we trying to

- accomplish?

- How will we know that a

- change is an improvement?

- What change can we make that

- will result in improvement?

- The most effective PDSA’s are those that are linked to your aim, measures, and key change concepts—the arrow from the three questions to the PDSA is not decorative!

## The PDSA Cycle for Learning and Improvement

***Act***

-  What changes
-    are to be made?
-  Next cycle?

***Plan***

-  Objective
-  Questions and
-    predictions (why)
-  Plan to carry out
-    the cycle (who,
-    what, where, when)

***Study***

-  Complete the
-    analysis of the data
- Compare data to
-         predictions
- Summarize what
-    was learned

***Do***

-  Carry out the plan
-  Document problems
-    and unexpected
-    observations
-  Begin analysis
-    of the data

## 			   The PDSA CycleTesting:The Basics

## Why Test?

- Increase the belief that the change will result in improvement
- Predict how much improvement can be expected from the change
- Learn how to adapt the change to conditions in the local environment
- Evaluate costs and side-effects of the change
- Minimize resistance upon implementation

## Testing on a Small Scale

- Have others that have some knowledgeable about the change review and comment on its feasibility.
- Test the change on the members of the team that helped developed it before introducing the change to others.
- Incorporate redundancy in the test by making the change side-by-side with the existing system.
- Conduct the test in one facility or office in the organization, or with one patient.
- Conduct the test over a short time period.
- Test the change on a small group of volunteers.
- Develop a plan to simulate the change in some way.

## Do  Study	

- Do ** **Study

**Reasons for failed tests****	****	****	**
- 1. Change not executed well
- 2. Support processes inadequate
- 3. Hypothesis/hunch wrong:
- 		  - Change executed but did not result in local improvement
- 		  - Local improvement did not impact access or efficiency
- Collect **data** during the Do Phase of the Cycle to help differentiate the these situations.

## Study  Act

- Compare data to prediction
- Summarize what was learned
- ACT - Take action on the new knowledge

## The Sequential Nature of PDSA Cycles: Building upon what we learn

*Marni Kopenski*
*Gerald Ignace Indian Health Center*

## PDSA #5

*Purpose/Objective*:  To determine if patients are bringing their medications or medication cards to their appointments (on each appointment card it says to bring in your medications).

## PLAN

- The Change:
*What are we testing?*  To determine if patients are bringing in their medication cards or medications at their visit.
*Who are testing on?  *5 patients of Marni Kopenski’s (all the diabetic patients on that day).
*When are we testing?  *June 6, 2007.
*Where are we testing?  *In the clinic.
- Our Prediction:
- We predict that at least 3 of the 5 patients will bring in either their current med list or the actual meds.

## PLAN cont.

- Data
*What data do we need to collect?  *If patient has a medication list or their meds with them at the visit.
*Who will collect the data?  *The MA rooming the patient.
*When will the data be collected?**  *Before the provider enters the room; beginning of visit.
*Where will the data be collected?*  On a bright green recording sheet.

## DO (carry out the test)

*What was actually tested?  *Measuring the number of patients who brought in their meds or med list.
*What happened?  *Of the 5 scheduled diabetic patients, only 1 patient showed up.
*Observations?*  We didn’t realize that we have many days when scheduled patients don’t show up.
*Problems?* We couldn’t accurately tell how many have a med list or actual meds due to only 1 patient showing up for visit.

## STUDY

- 1 of 5 diabetic patients showed up for their scheduled appt. That is a 80% no show rate on that day for diabetic patients.  The one patient that did show up had her medications with her and a medication card that she carries with her at all times.

## ACT

*What changes should we make before the next test cycle?  *We have to determine our current no show rate and what we can do to decrease it. Reception staff has not consistently been calling the day before to remind patients of appointments.
*What will the next test cycle be?  *On Monday, June 25, the receptionist will call MK’s patients that are scheduled for Tuesday appointments – will remind them of the appt. date and time, to bring their meds, and to call and cancel if they can’t make it.
*Are we ready to implement the change?  *Yes

## PDSA #6

*Purpose/Objective*:  Our last PDSA showed we had a lot of no shows and we wanted to test if calling patients before their appointments would help decrease no shows. Our no show rate is currently 18-22% and increases slightly in the summer. Per reception, they have not been calling patients because so many phone numbers are inaccurate or disconnected.

## PLAN

- The Change:
*What are we testing?*  To see if calling the patients before their appointment helps decrease no show rate.
*Who are testing on?  *Marni Kopenski’s Tuesday morning patients (9 total)
*When are we testing?  *Reception will call the patients today (Monday) and chart who shows up tomorrow.
*Where are we testing?  *In the clinic.
- Our Predictions:
-     We predict that 1/3 or 3 of the 9 phone numbers will be accurate, the no show rate will be less than 18%, the patients that reception actually talked to will show up, and that calling the patients the day before their appointment decreases no shows.

## PLAN cont.

- Data
*What data do we need to collect?  *Call patients and record if reception:
-  Talked to patient and patient said they would come to the scheduled appointment
- Left a message on a machine or with a family member
- Had a wrong phone number or phone was disconnected
- Didn’t get an answer – couldn’t talk to anyone
- Talked to patient and they rescheduled or cancelled
- On Tuesday, see what patients showed up and if there is a correlation between calling and showing up
*Who will collect the data?  *Reception
*When will the data be collected?**  *Tuesday afternoon after MK’s patients have been seen.

## DO (carry out the test)

*What was actually tested?  *If calling patients to remind them of their appointments will decrease  no show rate.
*What happened?  *Reception called patients and were surprised to reach most of them.
*Observations?*  Didn’t take long to call patients.
*Problems?* We still have some patients without accurate phone numbers or phones.

## STUDY

- Monday Calling: 9 calls made, patients were reminded of appointments, and encouraged to cancel if unable to come in.
- 1 wrong number
- 2 messages left
- 6 patients were talked to directly and confirmed their appointments
- Tuesday Patients:
- 2 people cancelled before their scheduled time
- 5 showed up
- 2 no showed – 1 was called day before and said she would be at appointment. The other no show had a disconnected phone.

## STUDY cont. 

- Only 2 of the 9 patients had a disconnected phone or wrong number. We had predicted 3/9 so we overestimated.
- No show rate was 22%. This was higher than expected but in normal range. We really thought this would be lower with the phone calls the night before.
- 6 patients were actually talked to and 5 of the 6 showed up. Therefore, 83% of the people called did show up for their appointments.
- Overall, we feel that the night before phone calls were helpful; patients that cancelled actually called before their appointment time.

## ACT

*What changes should we make before the next test cycle?  *We have now implemented calling patients the night before their appointments to remind them. Some staff think we have more disconnected phones or wrong numbers than these results indicate. They believe that when we ask patients to update the registration form, they just say, “it’s all the same” to get back into the clinic faster.
*What will the next test cycle be?  *For patients that come in tomorrow, Wednesday, June 27th, each one will be given a slip of paper to update their address and phone number in the room while waiting for the provider. At the end of the day, reception will compare what the patient filled out to the chart to find the number of discrepancies.
*Are we ready to implement the change?  *Yes

## PDSA #7

*Purpose/Objective*:  To determine if we give patients a piece of paper to fill out address and phone number while they are waiting in the exam room, we will find discrepancies with the information in the chart.
- Background: We have found that if we call patients the night before, they show up for their appointments. However, we’ve had a difficult time ensuring we have the correct phone number and address. Although we ask patients to update their information at check in, this doesn’t always happen. The last PDSA showed that 22% of patient had an incorrect phone number.

## PLAN

- The Change:
*What are we testing?*  The match between address and phone in chart vs. what patients put on piece of paper.
*Who are testing on? *Marni Kopenski’s patients.
*When are we testing?  *Wednesday, June 27.
*Where are we testing?  *In the clinic.
- Our Prediction:
-     We predict that 25% of patients will say their information is correct/no changes and when we compare slip to chart, there will be a discrepancy.

## PLAN cont.

- Data
*What data do we need to collect?  *Ask patients if address or phone has changed since we saw them last and have them fill out slip of paper while waiting in exam room.
*Who will collect the data?  *Reception will ask about any changes; RN’s will hand out and collect slip of paper; reception will compare with chart.
*When will the data be collected?**  *On June 27th after patients are gone.
*Where will the data be collected?* In clinic by RN’s and reception.

## DO (carry out the test)

*What was actually tested?  *The accuracy of the information in our charts as compared to what patients filled out on slip of paper.
*What happened?  *Reception did not ask if there was an address or phone number change before handing out slips (every patient got one).
*Problems?* Reception stated they were too busy to ask about changes before handing patients the slips.

## STUDY

- 11 of 17 scheduled patients showed up for their appointments and it was found that 8 of the 11 did not have discrepancies between the information they provided on the slip and what was in the chart. 3 patients did have a change (and these were the 3 we were not able to reach the night before). We were close to our prediction of 25% discrepancy rate and feel this is pretty accurate.

## ACT

*What changes should we make before the next test cycle?  *If we can’t reach a patient to confirm their appointment, they will be given a sheet to fill out their updated information when they come in the next day.
*What will the next test cycle be?  *We will retest with the receptionist just handing out the slips to the patients who we are unable to reach the night before. If anyone else in the clinic learns of a change in information, they will have access to the slips and can give them to the patient (the slips will be in the exam rooms).
*Are we ready to implement the change?  *Yes

## Your Observations

- What was effective?

- Opportunities to do better?

## Theory and Concepts-2

## Repeated Use of the Cycle

- Hunches Theories Ideas

- Changes That Result in Improvement

- DATA

## Repeated Use of the Cycle

**Hunches Theories Ideas**

**Changes That Result in Improvement**

**DATA**

**Very Small Scale Test**

**Follow-up Tests**

**Wide-Scale Tests of Change**

**Implementation of Change**

**Notes:**
You should anticipate multiple test cycles before you have a solid implementation.   

You can think of the PDSA cycle as an engine, powered by data and your enthusiasm, moving up a ramp.

## Repeated Use of the Cycle

**Hunches Theories Ideas**

**Changes That Result in Improvement:**
- After cycles have demonstrated that the change CAN work, use more cycles to help you figure out how the change WILL work, every day

**DATA**

**Investigation **

**Demonstration**

**Implementation**

**Notes:**
This is another view of the PDSA ramp.

To move quickly and to learn more about what it takes to make a change, take advantage of the paradox:

to learn more, take smaller steps... but study them intently.  

Take on one patient to test the first change, or run the change for two hours on one Day.  There is a lot to learn in the beginning of the collaborative and you can’t afford long cycles at first—you run the risk of putting things in place that can’t be sustained or have a relatively small flaw in infrastructure that will cause failure that could have been caught if you had only tested more thoroughly.

What you can learn depends on how much time you can afford to study and reflect before you act.  Study relies on data about the change, including OBSERVING the change (the DO step).  No clinic can afford to watch a month-long test of a new form—but you can afford to watch the use of a new form on one patient and learn a lot about how to improve it.

## What we mean by implementation

- You know a change has been **implemented **when you can have 100% staff turnover in your organization and the change will remain in place.
**Implementation** requires that staff and leaders have built the change into formal plans, job definitions, training, and intentional reviews. Implementation typically requires a process perspective, too.

**Notes:**
Pretty stringent standard!  Our experience in the past is that teams have claimed implementation far short of this standard....and the change has NOT persisted when the original team stopped “pushing” or was reassigned.

## Aim: Improve primary care appointment availability through reducing and standardizing appointment types

- Reduction of appointment types will increase appointment availability

- Improved access

- Cycle 1:Define a small number of appointment types

- Cycle 2:Compare requests to the types for one week

- Cycle 3:Test the types with 1-3 physicians

- Cycle 4: Standardize appointment types

- Cycle 5: Staff education in
-                new standards

**Notes:**
Theory:  Reduction of appointment types will increase appointment availability

What can you change first?

## Overall Aim: Increase Access

*Specific Test Cycles*

- Standard panel size

-  Scheduling
- protocols

-   Appointment
-   types

- Team approach
- to care

## Principles of Testing a Change

- 1.	Build Knowledge Sequentially
- Test on a small scale
- Use multiple cycles
- 2.	Increase the Ability to Predict from the Results of the Test
- Collect data over time during the test
- Test over a wide range of conditions

**Notes:**
Remember the following principles about testing a change:
1.   By using small scale tests and multiple cycles of testing, you have built knowledge about how to use many of the key collaborative changes in your organization.  
2.  You should now  be in a position to predict whether the changes you have tested will have the desired impact on your aims.  Your prediction is based on solid evidence -- data collected over time during the test and the fact that you have tested the change under a wide range of conditions.  
If  you feel that you do not yet have solid evidence to allow you to make this type of prediction, then additional testing prior to implementation (using the test on a broader scale) should be planned.

## Why predict?

- Explicit prediction of outcomes in uncertain situations followed by comparison of outcomes to the prediction may enhance our ability to learn.
- This effect has been suggested to occur at the level of brain chemistry:  “...activating dopamine neurons by uncertainty mobilizes attention, motivates risk-taking, and promotes learning about relationships between external stimuli and consequential events.” (Shizgal and Arvanitogiannis (2003), p. 1858 discussing Fiorillo et al (2003))
- Forces you to think about test measures
- Can increase staff interest in your tests

**Notes:**
PREDICTION may feel awkward at first and you may tend to gloss over or skip making a prediction.  But there are at least three benefits indicated here.

Prediction and subsequent comparison of outcomes to prediction appears to affect the quality of your learning.
If you make a prediction, it is natural to have to specify how you will know if the prediction will hold or not—you will have to specify one or more measures.  NOTE:  measurement for prediction within a PDSA cycle refers to immediate measurement of success or failure of your cycle.  It is distinct from measurement of the overall system of care (our collaborative measures.)

EXAMPLE:  Suppose you want to test two different formats to invite patients to come for FOBT screening.  You might predict method A will be better than method B.  So you have to measure response rates to the invitation—count up the number of responses from each method.

This “local” measurement is distinct from the overall collaborative measure of FOBT screening rate.

3. You can test competing ideas and have a bit of fun with friendly competition on predictions.

## Data for Improvement

- Key measures (to answer question #2)
- Data to support cycles (short term, process focus, include qualitative)
- Implementation relies on a combination of the two types

**Notes:**
The main idea is to communicate to the audience that there are different kinds of data to support improvement.
There are long-term measures that help us answer the question: “How will we know that a change is an improvement” through the collaborative and after.

There are short-term measures that are used to support one or two PDSA cycles. These are often less rigorous process measures and include qualitative measures and observations.

As the team moves more into PDSA cycles for implementation, both types of measures play a role. Implementation still has short-term process and qualitative implications, but many implementation cycles should also impact the key measures.

A side discussion may be necessary to explain surrogate measures, but only if necessary.

## Successful Cycles to Test Changes

- Do not try to get buy-in, consensus, etc.
- Be innovative to make test feasible
- Collect useful data during each test
- Test over a wide range of conditions
- Plan multiple cycles for a test of a change (think a couple of cycles ahead)
- Scale down size of test (# of patients, location)
- Test with volunteers

**Notes:**
1.  Utilizing volunteers means that you don’t have to get buy-in from everyone at the beginning.  
2.  Sometimes designing a test can be challenging, particularly if it is difficult to find a small enough scale to test a very large process or if the test may have patient or provider safety implications.  In such cases, other methods such as simulation can be used to interject a change into  a system and observe it’s effects.
3.   Remember to collect data during the test that will give you information about the impact of the test.  These process measures can be used only for the duration of the test so that the data collection for testing doesn’t detract from the data collection for the overall measures related to the teams’ aims. 
4.  Also remember to test over a wide range of conditions.  Some examples include testing on multiples shifts, with different patient populations, different days of the week, etc.  

## Questions for Users of the PDSA Cycle

- Is the planning based on theory? Stated?
- Are the predictions made prior to data collection?
- Are multiple cycles run?
- Is there documentation of what was learned?
- Does the learning provide a basis for action?

**Notes:**
Use these questions to help evaluate the strength of a test or series of tests.  If a team can answer yes to all of these questions, then they have used the PDSA cycle as a tool for learning...one that will accelerate improvement in the organization.  Teams can use these questions as a check list in evaluating how well they are utilizing the PDSA for learning.

## 	Example from On-Line Course: Accelerating Learning and Improvement

***What Cycle can we complete by ******3 PM******?***
- Willing to compromise on scope, size, rigor, and sophistication, but the Cycle must be completed, recorded, and sent to us by the time that we get back together at 3 pm ET.

## Documenting Improvements

## Link changes to your measuresAnnotated Time Series

## Family of Measures for Chronic Care BTS

## Track your PDSA cycles and Key measure(s).  Annotate your graph to reflect whether a change led to an improvement

- NE Team:  Actual team PDSA cycles and graph

## PDSA WorksheetPlan Do Study Act

**Organization:  Whiteriver**
**Date:  11/9/07**
**PURPOSE/OBJECTIVE OF CYCLE**
- Define patient perception of appointment time
**   **4 question survey of Hon-Dah staff regarding perceptions regarding appt times

## PLAN:  the change, data collection and predict

**The Change**
**What are we testing?**
**	**When do patients think their appt time starts?  Do patients perceive that they should meet with the provider at their appt time?
**Who are we testing the change on?**
**	****     **5 Hon-Dah employees   Use extremes...
-          younger, older
**When are we testing?**
**	****     **November 7
**Where are we testing?**
- 	     Hon-Day conference center.... And casino  

## Predictions
What do we expect to happen?
          People will say that the visit with the provider  
         begins at their appointment time (vs. the visit process  
         starting at their appointment time)
Data
What data do we need to collect?
    Read questions to them:  
     If your clinic appointment is at 9:00, what do you   
     expect to happen at 9:00?
Who will collect the data?  
     Emilia and Cindy
When will the data be collected?
            This afternoon......
Where will the data be collected?
            In the casino area 

**What do we expect to happen?**
-           People will say that the visit with the provider
-          begins at their appointment time (vs. the visit process
-          starting at their appointment time)
**Data**
**What data do we need to collect?**
-     Read questions to them:
-      If your clinic appointment is at 9:00, what do you
-      expect to happen at 9:00?
**Who will collect the data?  **
-      Emilia and Cindy
**When will the data be collected?**
-             This afternoon......
**Where will the data be collected?**
-             In the casino area

## DO:  Carry out the change/test; collect data and begin analysis

**What was actually tested**
- 5 persons who asked their perceptions
**What happened?**
- 1:  the doctor (she’d arrive 5 minutes early)
- 1: doctor
- 1: nurse (vital signs)
- 1: doctor (arrives 20 minutes early to get everything done and see her doctor at 9:00)
- 1: not to get seen  (if I come early I still wait.   I expect that I’ll see my doctor at 10:00)
**Observations**
- Can group perceptions into 3 categories:  doctor, nurse, no one
- 2 said they would come early for appt
**Problems**
- No problem with understanding the question.

## STUDY:  Complete analysis of data, summarize what was learned, and compare data to predictions

- 60% said they would see the doctor at 9:00
- 20% said they would see a nurse at 9:00
- 20% said they’d wait an hour to be seen
- 80% expect to see a care provider at 9:00
- Majority of people’s perceptions met our prediction.
- If we want to be patient focused, we need to adapt to their perceptions**  **
** **
- ACT:  What changes should we make before the
- next test cycle?
- We know how to ask the question....
- The prediction is good.
- Where test next
- Increase the number of people asked.
- What will the next test cycle be?
-       November 8 first thing in the AM for 10 minutes... as many as possible.
- Are we ready to implement the change?
-      NOOOOOOOOOOOOOOOO

## Several cycles later....

## PDSA WorksheetPlan Do Study Act

**Organization:  Whiteriver**
**Date:  May to June 2008**
**PURPOSE/OBJECTIVE OF CYCLE**
- Decrease interval between patient’s appt time and when provider interaction begins.

## PLAN:  the change, data collection and predict

**The Change**
**What are we testing?**
**	**Moving clinic start times from 8:20am to 8:40am and 12:40pm to 1:00pm.
**Who are we testing the change on?**
**	****     **Clinic staff, providers and empanelled
-         patients in IPC clinics.
**When are we testing?**
**	****     **Beginning with June provider schedule (5/19-
-          6/22).
**Where are we testing?**
- 	     Clinic A

## Predictions
What do we expect to happen?
          Staff will be more prepared for clinic day and    
         patients will be seen nearer to their actual appt time.
Data
What data do we need to collect?
   Cycle time data and experiences of staff.  
Who will collect the data?  
    Clinic A staff.
When will the data be collected?
	May 19-June 22, 2008
Where will the data be collected?
         Clinic A

**What do we expect to happen?**
-           Staff will be more prepared for clinic day and
-          patients will be seen nearer to their actual appt time.
**Data**
**What data do we need to collect?**
-    Cycle time data and experiences of staff.
**Who will collect the data?****  **
-     Clinic A staff.
**When will the data be collected?**
**	**May 19-June 22, 2008
**Where will the data be collected?**
-          Clinic A

## DO:  Carry out the change/test; collect data and begin analysis

- What was actually tested
- 	Shift of appt day 20 minutes later, from 0820-1040 to 0840-1100 and 1240-1540 to 1300-1600.
- 	(Data reported through end of May only).
- What happened?
- 	Patients saw their providers nearer to their actual appt times and overall patient cycle times decreased.  No negative impact was noted on appt times, staff lunches or when the clinic day ended.

## Observations
Staff were very positive about being prepared for the day, value-added time increased.  Staff said they “didn’t even notice” when asked about impact to lunch times or the end of the clinic day.  The time between appt  and provider interaction has previously been decreasing with clerks rooming patients.
Problems
Staff have become so efficient there is no longer time for the patient to complete the paper intake self-screen and staff have reverted to doing this verbally.

- Staff were very positive about being prepared for the day, value-added time increased.  Staff said they “didn’t even notice” when asked about impact to lunch times or the end of the clinic day.  The time between appt  and provider interaction has previously been decreasing with clerks rooming patients.
**Problems**
- Staff have become so efficient there is no longer time for the patient to complete the paper intake self-screen and staff have reverted to doing this verbally.

## STUDY:  Complete analysis of data, summarize what was learned, and compare data to predictions

| Cycle Time Results  | 5/01 to 5/18 | 5/19 to 5/30 |  |
| --- | --- | --- | --- |
| AVG Cycle W/O Pharm  | 1:09 | 0:50  |  |
| AVG Cycle W/ Pharm  | 1:51  | 1:32  |  |
| AVG  Minutes Early  | 0:16  | 0:06  |  |
| AVG Arrival to Room  | 0:06  | 0:06  |  |
| AVG Appt to Room  | -0:09  | 0:00  |  |
| AVG Arrival to Provider  | 0:46  | 0:33 |  |
| AVG Appt to Provider  | 0:30  | 0:27 |  |
| Value Added Time  | 55%  | 61%  |  |

- Times from patient arrival to provider decreased from 46 to 33 minutes
- Interval between actual appt time and provider interaction  decreased from 30 to 27 minutes
- Overall cycle times decreased by 19 minutes.
- Value-added time increased 6%.
- Staff: more prepared for clinic day so visits and day flow more smoothly.

## STUDY:  Complete analysis of data, summarize what was learned, and compare data to predictions

- No negative impact to appts, lunches or the end of the clinic day were noted.
- Interval between actual appt  time and provider interaction had been decreasing with testing of clerks rooming patients.  Appt time shift appears to continue this decrease.
-  Predictions were supported

## Cycle time impact to patients

## Cycle Time Results-Without Pharmacy visit

## Cycle Time Results-Including Pharmacy visit

## ACT:

**What changes should we make before the next test cycle?**
- 	Test cycle needs to be completed as June data has not been analyzed.
**What will the next test cycle be?**
- Pending completion of this cycle
**Are we ready to implement the change?**
- Pending completion of this cycle

## Your Observations

- What was effective?

- Opportunities to do better?

## More Questions and Discussion

## Next Steps

## How could you begin?

- Think about the following:
- What are the current results within the IPC-IHC for your organization?  Is there anything that stands out as needing improvement
- Do you have a team already at work? Ask the team about plans for tests in the coming week or so.
- What are some things that you think could go better in the clinic flow and communication?
- What are things that the patients and families complain about?
- What are things that you are doing well, but not doing reliably or regularly at this time?
- Think about timeliness issues...have any?

## Example:

- Took a look at the intake screening bundle results and found that depression screening is not routinely being done and pulls down the percentage of screening bundles being completed in our organization.
- Identified the current process
- Nurse identifies current status
- Physician does the screen in the room
- Hunch to test:   We feel that if the 2-question screen was done initially by the nurse, followed up by nurse administered PHQ when warranted, it will streamline the visit and also increase the percent of screens being done.

## Another Example

- Physicians are complaining that the review of the record for current status is taking too long and delaying the clinic flow.
- Hunch:  If the iCare summary sheet was printed off by registration, it could be placed on the chart and sent back to the nurse to verify more quickly that scanning the chart.
- Can you test this without the patients present?  Role play, use each other to test the flow

## One more....

- Done without patients present!

## Slide 95

## Additional notes on the Model for Improvement

*Model for Improvement analogous to behavior in other organisms*
- Behavior analogous to the Model for Improvement is exhibited by cells swimming in a media—something like the Model for Improvement appears to have a long evolutionary history!
- a.        “...cells carry out measurements and record them all the time.  The bacterium swimming upstream in a glucose gradient was my initial candidate example of an autonomous agent. The bacterium does so by molecular ‘sensors’ that measure glucose, a molecular motor with a stator and a rotor that can rotate in either direction, and a flagellum that can rotate in two directions, causing ‘swimming’ in one direction and ‘tumbling’ in the other. The cell achieves swimming ‘upstream’ by continuing to swim if the glucose concentration is rising and tumbling then swimming in a random direction if not.”  Kaufmann (2000), p. 150
- b.        Human sperm act in the same way as the bacterium. The sperm seek a human egg, navigating a chemical gradient with measurements made by an olfactory receptor.
*          “...*it has been known for some time that olfactory receptors (ORs) reside in spermatozoa, the function of these ORs is unknown. Here, we identified, cloned, and functionally expressed a previously undescribed human testicular OR, hOR17-4. ... hOR17-4 functions in human sperm chemotaxis and may be a critical component of the fertilization process.” Spehr et al (2003), p. 2054
-            _Explanation of chemotaxis_:  “To determine the behavioral implications of our findings, we investigated the effects of bourgeonal on human sperm swimming. An initial experiment tested for the accumulation of cells in microcapillaries placed within ascending, uniform, or descending chemical gradients. Results were used to distinguish between mechanisms of chemotaxis (directed movement with respect to a chemical concentration gradient), chemokinesis (change in swim speed), and cell trapping (arrest of swimming). In both the ascending and uniform gradients, cell accumulation within capillaries increased significantly as a function of dose) [two-way analysis of variance (ANOVA): F4, 281 = 30.01, P < 0.0001]. Moreover, densities of cells in capillaries within an ascending gradient were significantly higher than those within a uniform or descending gradient (two-way ANOVA: F2, 281 = 164.28, P < 0.0001). Finally, at each effective concentration (10-8 to 10-5 M), accumulation of cells in capillaries was ranked in the following order, according to chemical gradient: ascending>> uniform > descending. These collective results are consistent only with a chemotaxis mechanism--that is, directed movement toward a region of locally elevated bourgeonal concentration .” (Spehr et al. p. 2057)
-            In the bacterium example, the aim is food; in the sperm example, the aim is an egg.  In each example, there is a need for measurement and a requirement that the cell can change direction in response to measured performance.  Then there is a test cycle.  We do not claim there is a conscious plan by the cells, rather simply that after an action is taken (swimming in a particular direction), measurements are taken and compared with desired behavior of the signal (glucose increasing, buorgeonal increasing), and change in direction is made if desired behavior of the signal is not seen.  These steps are clearly analogs of Do, Study and Act.