Behavior scientists recognize that technology-based interventions can assist in effectively and efficiently creating behavior change (Erath & DiGennaro Reed, 2019; Johnson & Rubin, 2011; Twyman, 2014). Yet, the use of technology in interventions is still fledging, and there is a tremendous opportunity for growth. Organizations, especially hospitals, are ripe for technology-based interventions that deliver ongoing feedback and promote high performance and satisfaction. Hospitals are already accustomed to collecting a lot of data, and excellent performance is critical for patient outcomes and hospital solvency (Kelly & Gravina, 2017). For this blog, I asked my colleagues, Drs. Julie Smith and Lori Ludwig, to share the work important they are doing at Performance Ally.
Patient Feedback is Critical . . . Current Surveys are Ineffective
When I (Julie) was on the Board of a major academic medical center, I paid particular attention to our patient satisfaction survey results. The scores drove many important outcomes including payor reimbursements, provider incentive pay, and health outcomes. Knowing that every aspect of a patient’s experience depended on the behaviors of care team members, I helped the CEO understand that the survey feedback would never reliably improve the behaviors of care team members: it was too delayed, non-specific, and disconnected from individual care provider behaviors.
With the support of the CEO and executives at a second healthcare system, my colleagues and I began developing software to improve the patient experience as care was delivered. Using the science of behavior analysis, we were able to get >95% of patients engaged in providing realtime feedback, and 100% of staff involved in using the app daily without any “push” by management.
This level of engagement was truly a miracle. Other healthcare vendors have been able to get <10% of patients involved in providing real-time feedback. And on even the best of OBM projects, what percent of employees would willingly sign up to get performance feedback that was highly transparent? In my experience, not very many!
Seeing the huge potential of digitally enabled behavior change, I co-founded Performance Ally with Lori Ludwig and other behavior analysts. Our goal is to develop enterprise software that will optimize human performance, at scale, in any industry. Our app, Ally Assist™, creates “performance networks” of people who depend on each other to achieve mutual goals. It will help them:
- Set mutual performance expectations.
- Provide realtime feedback to each other to ensure expectations are met.
- Remove barriers to human performance as quickly as possible.
Right now, we are developing three integrated modules: one for leaders to observe and coach, one for individuals and teams to self-manage, and one to gather behavior-based customer feedback.
Here is a brief overview of how the customer feedback module worked in our first implementation in a healthcare system.
Total Engagement in Defining Vital Behaviors
We engaged all users in defining what a great patient experience looked like. Patients, families, care team members, senior leaders, and subject matter experts participated in focus groups and surveys to identify the following four behavioral categories, including the detailed Vital Behaviors that underpin each category:
- Be kind and considerate.
- Involve me in care decisions.
- Work as a team so I know I’m in good hands.
- Keep me informed about my care progress and any delays.
Individual care team members personalized a Vital Behavior that they committed to improving, e.g., “I want to use age-appropriate language to involve your child.”The work unit team also selected a team behavioral goal that they wanted to improve, e.g., “We will update you every 15 minutes about your care progress.”
Vital Behaviors at the organizational, team, and individual level were then converted into survey questions to be randomly sampled. These Vital Behaviors were updated dynamically; once a behavioral goal was mastered, a new one was selected and entered into the survey with ease.
(NOTE: In a future version of the software, crowdsourcing will be used to engage all users in rapidly identifying and updating the Vital Behaviors.)
Personalized Patient Expectations Survey
At the beginning of a healthcare encounter, patients were asked to complete a 60-second pre-treatment survey, which captured their top care expectation for that visit, based on the four behavior categories and Vital Behaviors. Then they were asked to describe why that choice was important to them. For example, “My top priority today is that you be kind and considerate because my child is afraid of doctors.” They were then asked to provide the preferred names of the patient and others in the room, as well as everyone’s relationship to the patient.
Care Team Link
Before a care team member first entered the treatment room, they accessed the patient’s expectation survey. From a service perspective, knowing the patient’s name and top expectation in behavioral terms before entering the room enabled each care team member to quickly establish a personal relationship.
Personalized Patient Experience Survey
Near the end of the healthcare encounter, the patient was asked to complete a 60-second post-treatment survey. The first question was, “How effectively did the care team meet your top expectation for that visit?” This key question closed the vital feedback loop between what was expected by the patient and then delivered.
To drive patient feedback to specific individuals, the photos of all care team members were then displayed on the survey. Patients were asked to select the pictures of those care team members who “exceeded their expectations.”
Additionally, the patient was asked to provide feedback on how well the care team did on achieving the behavioral goal it set. Finally, the patient was asked, “How likely are you (the patient) to recommend our hospital to your family and friends?” This key question is tied to payor reimbursements.
The patient was invited to take another 60-seconds to provide more specific behavioral feedback to an individual care team member who interacted with them. Patients rated the individual on how well they performed on their personal behavior goal. They also rated them on four additional randomly selected behaviors from all the Vital Behaviors chosen by the organization. They were given the option of completing comment sections related to thank you notes and suggestions for improvement.
Because more than 95% of patients completed the survey process, each care team member knew that there was a high probability that any given patient might be the one who winds up rating them. This randomized survey process led to tremendous behavior change on the part of care team members. Also, a plethora of data was available to review during team huddles, 1:1 coaching discussions, and leadership meetings.
We are excited to see how this system will be used to accelerate and sustain behavior change to achieve any result—in any organization. If you have questions or suggestions, please feel free to contact us. We need the best thinking of behavior analysts so we can create an app that will position OBM as an indispensable management system!
For more details about this case story, you can view this webinar: https://www.youtube.com/watch?v=XDYRjtLeL9U&feature=youtu.be
Dr. Julie Smith: Julie’s hallmark is her astonishing energy and ability to help global organizations achieve “mission impossible” using innovative approaches based on the science of behavior analysis. As co-founder of CLG (now known as Alula), Julie and her colleagues pioneered the most powerful behavior-centric performance improvement approach available today, as evaluated by multiple independent benchmark studies. Recently, Julie co-founded Performance Ally to create enterprise software that provides realtime performance feedback and behavior change support to individual associates, leaders, and teams, so adjustments can be made immediately to deliver better outcomes. This software, which is still in development, will optimize human actions realtime in any industry while creating a high-performance culture where employees win at work every day. In 2016, Julie was honored to receive the Outstanding Contribution Award from the Organizational Behavior Management Network. She also is an inductee of the West Virginia Business Hall of Fame. But Julie won’t rest (or retire) until a technology-supported, behavior-based management system becomes the gold standard across all organizations, large and small, for getting the right things done every day to achieve unprecedented results.
Dr. Lori Ludwig: Lori is a co-founder of Performance Ally, a tech start-up creating a revolutionary behavior change management platform to optimize individual, team, and organizational performance. She has over 20 years of experience helping clients with strategic planning, measurement design, process improvement, performance management, brand development, and performance based instruction. Lori has worked with a variety of companies across different industries, from global Fortune 500s, creative start-ups, human services, non-profits, to local small businesses. Lori earned her B.S. in Psychology and Creative Writing, M.S. in Industrial Organizational Psychology and Ph.D. in Applied Behavior Analysis from Western Michigan University. She currently serves as the President-Elect for the Organizational Behavior Management Network, a Trustee of the Cambridge Center for Behavioral Studies, and a Board Member of Blue Ridge Women in Agriculture.
Erath, T. G., DiGennaro Reed, F. D. (2019). A brief review of technology-based antecedent training procedures. Journal of Applied Behavior Analysis. Advanced Online Publication.
Johnson, D. A., & Rubin, S. (2011). Effectiveness of interactive computer-based instruction: A review of studies published between 1995 and 2007. Journal of Organizational Behavior Management, 31(1), 55-94.
Kelley, D., & Gravina, N. (2018). A new paradigm in healthcare: An open door for OBM. Journal of Organizational Behavior Management, 38(1), 73-89.
Twyman, J. (2014). Envisioning education 3.0: The fusion of behavior analysis, learning, science and technology. Mexican Journal of Behavior Analysis, 40, 20-38.