upon having good and accurate data, but the true benefit and highest reward come from refinement of a caring environment for the patients and families where safety and quality come first.
As you dig into the case studies in this book, take with you the assurance that all of the work described in those case studies is within reach for you and your organization, too. If we can do it, you can do it.
4 Leadership for Improved Healthcare Outcomes
Linda Valentino and Mary Ann Hozak
All of the improved healthcare outcomes reported in this book can be traced back to a leader who understood that data must resonate with people. Data engages people when it is generated using measurement instruments specified to capture data that paints a picture of the organizational culture and associated operations. Highly specified data inspires action because it shows staff members what they are already doing well or need to fix to achieve improved operations and outcomes. When we, the authors of this chapter, share specific, recognizable, highly relevant data with staff members, they are most often at the edge of their seats. As they listen for the next piece of data, they anticipate the discussion because they already have ideas to improve operations. This chapter shares how leaders in healthcare organizations used data to make carefully targeted operational changes to improve outcomes.
Both of us have worked closely with a data analyst and have learned from him not only how to identify patterns within our data that reveal what is really going on operationally, but even more vitally, the importance of giving staff members access to data that really resonates for them. Once leaders from every level in the organization are working to tell their story more completely using data, and prioritizing their actions accordingly, widespread cultural and operational transformation begins.
Data as a Tool to Make the Invisible Visible
Data can help you identify leadership or teamwork issues such as incivility, and it can help bring factual realities into difficult conversations. What follows are two ways that data rather predictably reveals issues in organizations and units across the world, and one example of how the use of data can help facilitate difficult conversations.
Identifying a Bully
In the United States, the term “bully” refers to an employee who harasses or even abuses other coworkers (Lever, Dyball, Greenberg & Stevelink, 2019). Data derived from the Healthcare Environment Survey (HES), which measures nurse job satisfaction, behaves in a particular way when a bully is present. If satisfaction with coworker relationships is low and variables such as professional growth and satisfaction with patient care are high, it indicates that there is a bully on the unit. It is unknown how many times this pattern has been identified, but it has always proven to foretell the presence of a bully. (The data analyst can look at your data and tell you have a bully on your unit before he even visits.) It is also always the case that when this data is presented to the staff, the staff members all look at one another, knowing who the bully is, but not saying anything. The revelation of such data gives leaders greater urgency to address the issue. Bullies create chaos where they work, which causes relational distress for everyone. It is also the experience of the analyst that bullies typically work in pairs, with one person instigating the bullying behavior and the other coworker facilitating it. While it is often very difficult to move a bully out of an organization entirely, once the presence of a bully is revealed to all, action can usually be taken to start remedying the situation.
Managers Who Are “Buddies”
Another issue that can be identified by use of the HES is managers who are reluctant to enforce policies because they are too eager to be liked by their staff. Again, using the HES job satisfaction data, when the score for satisfaction with “relationship with unit manager” is high but all 10 of the other aspects of job satisfaction are noticeably lower, it is often because the unit manager has become “one of the gang” and is not tending to the work of actually leading the unit. In this “manager‐as‐buddy” scenario, it is also usually the case that the manager complains along with the employees and does not hold the staff accountable to follow policy, or understand their role, or accountability, or what their level of authority is in terms of patient care.
Something interesting (and initially alarming) shows up in the data when these “buddies” begin to evolve into good leaders. When leaders become clear in their own roles, they usually realize the importance of helping staff members become clear in their roles, which means, among other things, requiring them to follow policies. Not surprisingly, scores reflecting the staff’s satisfaction with the manager will drop—sometimes precipitously—once the manager begins, after a period of having reliably been a “buddy,” to really lead. What is fascinating, however, is to watch the 10 aspects of job satisfaction for the nurses under this person's supervision that were initially so low, steadily improve. If you understand the data, you realize pretty quickly that the falling score for satisfaction with the manager is not a reflection of the manager's poor performance, but actually a symptom of staff members adjusting to losing a manager who did not adhere to policy. It is common for the data analyst to have to spend some time encouraging managers who initially feel bad when their unit's score for satisfaction with “relationships with the unit manager” falls, especially after they have begun working so hard to be better managers. It can take 2–3 years for staff to adjust to the change when a manager shifts from one who lets people do mostly as they please to one who leads with role clarity and policy enforcement. If not for the presence of an analyst who understands the nuances of such data, the data would be disheartening, and nothing productive would likely come of it. It is the real‐life story the data tells that is of inestimable value.
It is the real‐life story the data tells that is of inestimable value.
Using Data to Bring Objectivity to Sensitive Topics
Sometimes data brings needed clarity to difficult subjects, such as tensions between doctors and nurses. It is not uncommon for nurses and doctors to have stressful relationships. As our data analyst colleague has seen time and again, this is often due to poor role clarity or poor system clarity in the independent, interdependent, and dependent functions of each role. The roles can overlap, and interdependence between nurses and doctors sets them up for tension if they lack role or system clarity in teamwork.
As you can imagine, simply announcing the reality that nurses in an organization or unit have said they are dissatisfied with their relationships with physicians would be counterproductive. When the data is understood in context, however, it can be used to facilitate some very helpful discussions and/or spur people to incorporate relationship‐building opportunities into their action plans. Presenting the data as it relates to commensurate scores in role clarity or system clarity can help facilitate discussions on how people from each profession experience (or would like to experience) teamwork, or to identify where specific tensions exist in clinical care. Such discussions have helped reveal opportunities for resolution.
In an organization in Scotland, when the scores showed strained relationships between nurses and physicians, their solution was to hold retreats where nurses and physicians got together to discuss issues, both professional and personal, in a safe and nurturing atmosphere. It was a relatively small action that yielded noticeable improvement. And, of course, people do not take such actions in order to achieve a better score on a report; it is to have the care that comes with better relationships become standard in the organization.
Without effective leadership, change will not occur. Leaders need to pay attention to what our data analyst colleague
