The Next Generation of SPD Leadership: Human Plus Machine

The False Choice

A technician responds to an equipment alert. A tracking system shows where an instrument was last scanned. An inspection device helps reveal damage that may be difficult to see. A dashboard shows a developing capacity problem before the operating room feels its effects. Technologyisalreadypartofhow sterileprocessingprofessionalssee, understand, and manage the work.

Conversations about artificial intelligence, automation, and robotics often return to one question: Will technology replace people? That framing misses how sterile processing actually works. Trained professionals already use equipment, information, standards, and judgment together to protect the patient.

The real issue is not whether technology will become more capable. It will. The more important question is how sterile processing leaders will evaluate these tools, prepare employees to use them, and preserve accountability when decisions are increasingly supported by systems that collect data, generate alerts, automate steps, or recommend action.

Seven emerging sterile processing leaders from health systems across the United States considered what this shift will require. They approached it through data, predictive quality, implementation, workforce readiness, operational reliability, and SPD’s role in enterprise technology decisions. All came back to the same principle: technology should strengthen human expertise rather than replace it.

The leaders are (Figure 1):

The leaders are (Figure 1) Brandy Bowser, CRCST with Kaiser Permanente;

Mamadou Cissé, CST, HSR, with Hospital for Special Surgery; Bobbie Gaskins with the Cleveland Clinic; MK Parrott with Ascension; Felicia Sheriff, CHL, CIS, CSPDT, with Harris Health; Eduardo Velazquez. CHL, CRCST, CFER, CSPDT, with UCHealth; and Michele Webb with Cooper University Hospital.

The next phase of sterile processing will not be determined by which department owns the most advanced tools. The better test is whether a tool solves a real problem, fits the workflow, produces reliable information, and helps people make safer decisions.

Artificial intelligence, automation, instrument tracking, advanced inspection systems, and operational analytics may significantly change the work over the coming decade. They may reduce repetitive steps, make risk more visible, improve access to information, and help departments anticipate demand. They will also create new responsibilities involving training, data integrity, maintenance, downtime, oversight, and change management.

More capable technology will demand more from leaders, not less. Employees will need help using new systems without giving up the judgment, accountability, and collaboration at the center of sterile processing.

Technology Is Already Changing the Work

Technology is not waiting for some distant future to change sterile processing. It is already shaping how departments monitor equipment, track instruments, inspect devices, document work, identify risk, and respond to operational demands. The challenge for leaders is no longer simply deciding whether to adopt technology. It is making sure the technology solves a real problem and becomes part of dependable daily work.

Felicia Sheriff sees one opportunity in the information departments already receive. Equipment warnings, audit findings, and workflow data do more than identify that something went wrong. They can help leaders see where employees need clarification, additional practice, or stronger support. When information is used this way, technology becomes part of the department’s coaching system, connecting quality findings with learning at the point of work.

MK Parrott brings the same practical test to technology decisions: Does the tool help someone reach the right information, solve the actual problem, or perform the work more effectively? A system may be technically impressive, but its value depends on whether it fits the workflow and helps employees make better decisions. Technology shouldn’t sit beside the work as a separate project. It should make the work easier to understand and perform reliably.

Eduardo Velazquez focuses on what happens after a technology is selected. Automation, robotics, and artificial intelligence may improve consistency and help departments keep pace with demand, but implementation creates its own work. Processes may need to be redesigned. Employees need training. Equipment requires maintenance. Leaders need plans for downtime and enough operational understanding to recognize when human oversight is still required.

That implementation work cannot happen only at the department level. Michele Webb argues that sterile processing should be involved earlier in organizational decisions involving surgical growth, space, instrumentation, equipment, and digital infrastructure.SPD is already a high-technology environment, yet the people who understand its workflows may be consulted only after major decisions have been made. By then, the department is often asked to adapt to a plan it did not help shape.

Brandy Bowser brings the frontline employee into that conversation. She hears both interest and concern about artificial intelligence and automation from the people closest to the work, and she considers those reactions valid. Employees need an opportunity to understand what is changing, build the skills required to use new tools, and raise concerns about how technology affects the workflow. Involving them early can improve adoption while helping leaders identify practical issues that may not have been visible during planning.

Technology adoption is not a one-time decision. Leaders must choose a problem worth solving, involve the people who know the work, prepare the department, support the system, and determine whether it actually improves quality, reliability, or patient safety. Progress is not measured by how much technology a department owns, but by whether people can use it well.

From Clipboard to Dashboard

Mamadou Cissé captures the change in a single image:

“Leading an SPD department in 10 years is going to look a lot more like leading with a dashboard than leading with a clipboard.” —Mamadou Cissé

The clipboard represents a familiar kind of leadership: walking the department, checking the work, recording what happened, and responding to the problem in front of you. Those practices will not disappear. Leaders still need to remain close to the work and understand what employees are experiencing. The dashboard adds another level of visibility by helping them see patterns that may be difficult to recognize one tray, one shift, or one event at a time.

Hospitals already hold significant operational information across electronic health records, instrument-tracking systems, equipment-monitoring platforms, utilization records, scanning activity, quality reports, and other workflow systems. Much of that information remains separate, however, leaving leaders to piece together the story after a disruption occurs. The opportunity is not simply to collect more data. It is to connect readily available information in ways that help leaders understand what is happening and what may happen next.

Better visibility could show which instruments are repeatedly unavailable, which items are frequently missing, where inventory is underused, or where workflow delays are beginning to affect capacity. It could also help leaders recognize service-line demand, anticipate equipment constraints, and identify quality concerns before they become recurring events.

That changes the work of leadership. Instead of spending most of the day responding to shortages, delays, and breakdowns after they become urgent, leaders may be able to intervene earlier. A developing pattern can prompt a conversation, an inventory decision, a workflow adjustment, or additional support before the operating room feels the effect.

The shift also requires a different set of skills. Future SPD leaders will still need to understand instrumentation, standards, workflows, and the realities of the department. They will also need to know how to interpret data, question what it shows, and explain what it means to people outside sterile processing.

The dashboard does not make the decision. It gives the leader a clear view from which to make it. The clipboard is not disappearing. Direct observation, conversation, and presence in the department remain essential. The dashboard becomes valuable when it strengthens that operational understanding rather than replacing it.

Data Is Not the Same as Insight

A dashboard can make information visible, but visibility alone does not create understanding. Sterile processing departments can collect thousands of scans, timestamps, alerts, audit findings, and quality records without becoming any better at identifying what needs attention. Data becomes useful only when leaders can connect it to the work and decide what to do with it.

Mamadou Cissé cautions against relying on vanity metrics, numbers that are easy to report but do not explain what is actually happening on the floor. A favorable average may hide repeated delays on one shift. A completion rate may appear strong even when employees scan inconsistently. A count may show how much work was completed without revealing the pressure, rework, or constraints involved in completing it.

The question is not simply whether a metric is available, but whether it helps leaders understand performance.

That understanding begins with data integrity. Every dashboard depends on the workflow that produces its information. Missed scans, inconsistent documentation, unclear definitions, and disconnected systems can create a picture that appears precise but does not reflect operational reality. Before acting on the data, leaders must understand where it came from, how reliably it was captured, and what may be missing.

Cisséen envisions stronger integration among instrument tracking, equipment monitoring, clinical systems, and other operational platforms. Over time, technologies such as machine vision may help verify that the digital record matches what is physically occurring. Even then, leaders will need to question the information rather than assume that a more sophisticated system is automatically more accurate.

MK Parrott focuses on what happens once the information can be trusted. Data should help teams find where the problem actually exists rather than where people assume it exists. A delay that appears to be caused by technician productivity may instead begin with missing inventory, late case changes, equipment constraints, or uneven demand. Better information helps leaders direct improvement efforts toward the source of the problem rather than the most visible symptom.

Bobbie Gaskin extends that idea from explanation to prediction. Instead of reviewing quality events only after they occur, leaders may eventually be able to recognize patterns across inspection findings, tracking records, workflow deviations, equipment alerts, and quality reports. A change in those patterns could signal increasing risk and create an opportunity to intervene before another event reaches the operating room or patient.

The future SPD leader will need to do more than collect information or read a dashboard. Leaders will need to test what the data appear to show, compare it with what employees are experiencing, and translate it into a practical response. The value of data are not the report it produces. It is the better question, earlier intervention, or more informed decision that follows.

The Promise and Reality of Automation

Automation is often presented as a way to improve consistency, reduce variation, and help departments keep pace with growing demand. Those benefits are real, particularly when technology takes on repetitive steps, supports standard work, or gives employees better visibility into the process. Eduardo Velazquez, however, cautions that automation does not remove complexity. It changes where that complexity sits.

A manual process depends heavily on employee time, technique, and attention. An automated process may reduce some of that burden, but it also depends on equipment availability, maintenance, software, supplies, validated workflows, and employees who understand how to operate and monitor the system. The work does not disappear. Some of it moves from performing the task to managing the technology that supports it.

That shift becomes most visible when the equipment does not function as intended. A department that relies on automation must know how to recognize a failure, escalate the problem, and continue operating safely while the issue is resolved. Downtime procedures cannot exist only in a policy. Employees need enough familiarity with them to respond under pressure, particularly if the automated process has replaced a manual method they rarely use.

Automation may also require changes to the surrounding workflow. Adding equipment to an existing process without reconsidering staffing, space, handoffs, maintenance responsibilities, and contingency plans can create new bottlenecks instead of removing old ones. The value of the technology depends on how well the entire operation is prepared to support it.

Velazquez’s perspective puts the leadership responsibility in clearer terms. Purchasing automation is only the beginning. Leaders must prepare the people, workflow, infrastructure, and backup plans to ensure the technology improves work without becoming a single point of failure.

Successful automation is not measured only by what happens when the system is working. It is also measured by whether the department can recognize a problem, respond appropriately, and continue protecting the patient when it is not.

Buying automation may be a capital decision. Making automation successful is a leadership decision.

Artificial Intelligence as an Amplifier

Artificial intelligence is often discussed as though it will act independently of the people and systems around it. In practice, AI works with what it is given. It can organize information, identify patterns, and bring relevant resources closer to the user, but it does not correct weak data, unclear processes, or gaps in professional knowledge. It amplifies the environment in which it is used.

MK Parrott sees its value in reducing the distance between a professional and the information needed to perform the work. AI may help employees locate an IFU, find a technical resource, summarize information, or recognize when additional guidance is needed. Used well, it can reduce time spent searching and allow people to focus more attention on the decision in front of them.

Bobbie Gaskin sees another possibility in quality and risk detection. Information from inspection findings, instrument tracking, equipment alerts, and workflow systems may contain patterns that are difficult for a single person to recognize amid thousands of daily transactions. AI could help bring those patterns into view earlier. The technology may identify that something deserves attention, but a trained professional must still determine whether the finding is meaningful and what action should follow.

AI can also amplify poor information. Incomplete scans, inconsistent documentation, outdated resources, or misunderstood workflows can lead to confident outputs that do not reflect the work accurately.

Employees need to know when to question a result, confirm it against the standard, and raise a concern rather than accepting the system’s recommendation at face value.

Eduardo Velazquez brings the discussion back to reliability. AI creates value only when the department can implement, monitor, and support it as part of the operation. Brandy Bowser adds the workforce perspective: employees need sufficient understanding and engagement to build confidence in the technology without becoming overly dependent on it.

The advantage will not belong simply to departments with access to the most advanced tools. It will belong to professionals who know how to use those tools thoughtfully, when to trust the information, when to verify it, and when human judgment must take the lead.

AI can make information faster to find and patterns easier to see. It cannot decide what the patient requires. That responsibility remains with people.

Human Factors Still Matter Most

Every technology eventually meets a human being trying to use it during real work. The employee may be rushed, interrupted, unfamiliar with the equipment, or responding to an alert that is difficult to interpret. A system can be technically sound and still create risk if the people using it do not understand it, trust it too much, or feel unable to question what it tells them.

Bobbie Gaskin connects this responsibility to psychological safety. Employees must be able to question a system output, admit that they do not understand something, and stop the process when the result does not

appear right. Technology may produce an answer with confidence, but confidence is not the same as accuracy. People need permission to pause, verify, and escalate.

Felicia Sheriff sees the same interaction as an opportunity for learning. Equipment alerts, audit findings, and workflow information can help leaders identify where employees need clearer guidance, more practice, or a better understanding of the process.Theleader’sresponsematters.An alert can become a coaching moment, or another reason employees hesitate to speak.

Human factors also become especially visible when technology fails. Eduardo Velazquez emphasizes the importance of remaining level-headed during downtime and helping the team move safely through the disruption. Brandy Bowser focuses on preparing employees for changes so they can build confidence without becoming overly dependent on the tools. In both cases, reliability depends on more than the system. It depends on how well people have been prepared to respond.

Michele Webb extends the issue beyond the department. When SPDs are involved early in decisions about equipment, instrumentation, space, and digital infrastructure, technology is more likely to reflect the actual workflow. Inclusion is not simply a matter of professional recognition. It helps organizations avoid designing systems around assumptions that do not match the work.

Technology can identify a risk, but a person must decide whether to act. It can reveal a pattern, but a leader must determine what the pattern means. It can standardize part of a process, but employees must recognize when the process is no longer operating as expected. As technology becomes more capable, these human responsibilities do not disappear. They become more important.

As automation becomes more sophisticated, human factors become more important, not less.

The Future SPD Leader

SPD leadership is expanding beyond the department’s immediate work. Tomorrow’s leaders will still be accountable for quality, staffing, throughput, and patient safety. They will also need to connect culture, systems, data, technology, and change without losing the human judgment at the center of the work.

That broader role also changes where SPD leadership must show up. Webb emphasizes the need for SPD leaders to participate earlier in decisions regarding surgical expansion, equipment, instrumentation, space, and digital infrastructure, so that technology reflects operational realities from the outset. Bowser refocuses on implementation: Leaders must listen to frontline concerns, create opportunities to learn, and help teams adapt without losing sight of the patient. No single new skill defines the role; the responsibilities are connected (Figure 2).

Technical expertise remains the foundation, but future leaders will carry it into more places: technology decisions, data interpretation, workforce preparation, change management, and enterprise planning. Their value will come from connecting human capability with technological capability in the daily work.

Human Plus Machine

The future of sterile processing will not be defined by choosing between human expertise and technological capability. It will be shaped by how effectively the two work together. Technology can make patterns visible, bring information closer to the work, automate repetitive steps, and help teams recognize risk earlier. People provide the context, judgment, and accountability needed to determine what that information means and what action the patient requires.

Creating that connection is a leadership responsibility. Leaders must ensure that technology addresses a real operational need, reflects the realities of the workflow, and is supported by reliable data, thoughtful implementation, workforce preparation, and clear plans for oversight and downtime. Without those conditions, even an advanced tool can create new confusion, dependence, or risk.

The strongest departments will not necessarily have the most technology. They will have leaders who use visibility to ask better questions, prepare teams for automated work, and draw on the expertise of the people performing it. Better tools will create new choices and new points of failure. Leaders must prepare employees to use the systems, question the output, and respond when technology does not perform as expected.

Much of that work will reach beyond the department. Decisions about surgical growth, equipment, digital infrastructure, data, and workforce strategy all affect how technology functions in SPD. Future leaders need a voice in shaping those decisions, not just responsibility for carrying them out. That is how SPD can improve today’s work and help the organization plan what comes next.

The future of sterile processing will not be built by people alone or by technology alone. It will be built by leaders capable of connecting the strengths of both.