Each question half-page one referance
It is always difficult when an established and respected leaders leave a team of individuals who admired what they did and how they lead them. This is a common occurrence as leaders don’t always stay around and leave to pursue better career opportunities for themselves, it is administrations job to help fill the shoes that there leaders have left behind. When it comes to evaluating new leadership effectiveness on productivity, the answer truly is subjective, especially in the short term. Every leader is different in their own way, however there are metrics that administration can keep tab on in order to see how effective their new leadership is. Three metrics I would use in determining effectiveness would be 1.) measure workforce attitudes. Team members hold the most intimate interactions with the leader and have a good idea of how they work together. Especially if they did have a great leader previously to this, they have a good idea on how to compare the two in terms of effectiveness on morale and productivity. Sending out surveys on how the team feels about their new leader can give administration a good benchmark on how effective the new leader is. 2.) Measure patient satisfaction. Great leaders create engaged employees who find purpose in what they do and feel close to the company as a whole. If leaders have employees who were previously engaged, and have turned unengaged, it will effect patient satisfaction in terms of quality and care. If there is a sudden rise in unhappy patients, it could be attested to unhappy employees that stem from poor and uneffective leadership. 3.) Evaluate strategic objectives. Healthcare organizations have “strategic objectives, so leaders should measure themselves on whether those goals are being met or not. If not, then leaders should do a deeper dive to uncover the causes” (Forbes, 2021). #2
ccording to Joseph (2019), short-term effects of [leadership] change can sometimes be painful, but it can have a positive impact on a business’ success in the long run (para. 1). However, in this example I believe that the likely impact on morale and productivity will be positive in both the short and long terms. I make this assumption because of how long the organization took to replace the chair. They likely looked for an ideal candidate, and one that could step in and be effective right away, and research shows that effective leaders can use a number of tactics to keep employees motivated, focused and productive (Evans, 2017, para. 1). Statistics show that a supervisor directly influences 70% of an employee’s motivation (Orechwa, 2022). For this group, that is going from no leadership to new effective leadership, employees will likely be motivated and productive.
There are several metrics that could be used to help determine the effectiveness of the new leadership on productivity. In this example productivity must be considered as a relative measure in order to compare the productivity of the staff through historical benchmarking. The organization should first look at similar productivity ratios calculated from under previous leadership periods, the period with no leadership, and under the current leadership. By comparing historical benchmarks the organization can compare how productivity has changed from period to period and assess to what extent leadership was a factor in the variation.
A metric than can be calculated for historical comparisons is Hours per Patient Day (or Visit). According to this measure, two units that have the same staffing levels and treat the same number of patients should be equally productive (Ozcan, 2017). In this case, one could assume that the staff should have been able to treat the same number of patients during the period of no leadership as they do under the new leadership. If the results show that the staff is now treating more patients within the same amount of time, then they are working more productively and therefore the assumption can be made that leadership has had a positive impact. If the inverse is true, then one can assume that new leadership has not yet had a positive impact on productivity.
The final metric that can be used for determining productivity is Data Envelopment Analysis (DEA). DEA measures relative efficiency by the ratio of total weighted output to total weighted input and is considered to be a total factor productivity measure (Ozcan, 2017, p. 318). In other words, it is a more effective overall measurement of productivity. This ratio can also be applied historically to compare periods of time. Again, in this case leadership could compare the productivity from before the leadership change to that of productivity after the change, and even the period when there was no leadership.