Using big data in hospital fm to enhance employee productivity and quality of life (1)

Using Big Data in Hospital Facilities Management to Enhance Employee Productivity and Quality of Life

James Ware, PhD Executive Director The Future of Work...unlimited

Lisa Herms, MSc Research Analyst, Sodexo

2The information and concepts contained in this document are the proprietary property of Sodexo.

As such, they cannot be reproduced or utilized without permission. ©2015

“Big Data,” the phenomenon every industry and professional is hailing as the next greatest innovation, has its origins at least in part in the healthcare industry. Healthcare leaders are already attempting to use large-scale patient-level data to improve patient outcomes and population health. Yet interestingly, the healthcare industry is lagging behind other industries, many of which have adapted the Big Data movement to their advantage in a new format — as a mechanism to monitor employees in order to improve their quality of life and productivity.

With modern technology, it is possible to capture and analyze more information about work activities than ever before, and the promise of using Big Data in hospital facilities management (FM) is compelling. Hospitals have the capabilities to gain deep insight into day-to-day operational activities, which can trigger workflow redesign, more efficient staffing, and significant improvements in employee well-being and quality of life. At the same time, Big Data initiatives can help mitigate many hospital workplace risks (i.e., liability cases, infections and contagious diseases, adverse safety events).

As hospitals are facing pressures on costs, efficiency and better outcomes from all sides, realizing the value of Big Data can be a powerful tool. Well-designed and implemented Big Data initiatives in hospital FM can contribute to employee productivity by reducing the time wasted on non-value-adding activities by staff, thereby driving operational efficiency. At the same time, these initiatives can help monitor and ensure employee safety, health and well-being, ultimately contributing to an increased quality of life among the workforce. Examples of utilizing Big Data in hospital FM are aplenty and continuously growing.

USING BIG DATA TO MONITOR THE PHYSICAL HOSPITAL ENVIRONMENTIn a very classical setting, hospital FM can utilize Big Data to its advantage in monitoring the physical environment and facilities. Environmental factors have a major impact on hospital employees’ performance and productivity, as well as their well-being and quality of life.

For example, many hospitals have already adopted room pressure monitors and controls, especially in isolation units and operating rooms. Hospitals are increasingly using room sensors that also monitor other environmental factors — from energy use and lighting, to humidity and radiation. These sensors can be centralized and monitored, or even programmed to send out alerts when levels exceed a designated safety threshold. In the future, these room sensors may not only trigger a centralized alert, but also might dispatch environmental services teams to investigate.

Dirt-detecting robots and cleaners already exist, even for mere household use. With the advance of technology and data, it is likely that hospitals will soon link room sensors and monitors with these cleaning robots. For example, the germ-zapping robots frequently adopted by hospitals could be linked to room sensors that indicate room entries and exits and be automatically deployed once a certain activity level has been reached. Other advances might include the adoption of toilets that trigger cleaning services after a designated number of flushes, or sensors on high-touch areas.



USING BIG DATA TO IMPROVE HOSPITAL WORKFLOWIn terms of improving efficiency and employee productivity, Big Data can also be used to monitor and improve the workflow within a hospital. To achieve this goal, data can be gathered to track patients, employees, and equipment and medical supplies.

Patient TrackingJust as there are systems in place that help transfer patient information and make it available to the different departments and physicians within a hospital, so too can the movement of a patient throughout the facility be monitored. This can be achieved through either real-time location systems (RTLS) or a combination of existing data sources and manually entered status updates. These types of systems are currently primarily used in emergency departments, and overall adoption is presently low (less than 5%);1 however, their use is likely to expand.

Monitoring patient pathways can uncover inefficiencies and throughput issues. Without going into detail about the merits of allowing a more efficient flow of patient-level data in a hospital, even simply observing which pathways patients take from admission to discharge — including where they are logged and which employees they come in contact with — can reveal potential inefficiencies and bottlenecks. Monitoring the waiting times at various stages of a patient’s hospital stay will allow the hospital to calculate through-put time in the hospital as a whole, as well as in specific departments. Inefficiencies that are identified could be the result of staffing decisions, lack of clear guidelines or poor information transfer internally — or a combination thereof. Data collected on patient flow can help to identify the origin of any throughput problems.



There are an abundance of success stories of this Big Data method:

§ Christiana Hospital: Following implementation of an RTLS-based tracking system for patients, Christiana experienced reductions in patient visit length, reductions in patients leaving without being seen, and improvements in patient and staff satisfaction. In the 12-month period after implementation, the average time to be treated and released was reduced by 14 minutes, and the average time to be treated and admitted was reduced by 36 minutes.1

§ Providence Holy Cross Medical Center: Through better integration of patient tracking software, Providence experienced a 53% decrease in bed turnaround time and an 85% decrease in admission turnaround time within three months. Savings are estimated at $1.7 million for the first year; the medical center also experienced a reduction in delays at shift changes, fewer calls between departments, and fewer crisis scenarios.1

§ St. Vincent’s Hospital: St. Vincent’s realized an additional benefit of patient tracking: time saved by housekeeping. Prior to the adoption of the new system, housekeeping staff had no way of knowing when a patient was scheduled to be discharged. Consequently, unnecessary room cleans were frequently performed near the end of a patient’s day. With more detailed information about the past and future flow of a patient through the hospital departments, staff can now eliminate unnecessary “routine cleans” by doing one “comprehensive clean” after the patient has been discharged.1

Employee TrackingSimilar to patient tracking technology, some hospitals have also implemented tracking systems for their healthcare staff, particularly nurses. Location tracking of nurses can shed light on unnecessary trips back and forth between locations, suggesting unfavorable layout, inefficient routines or poor stocking of medical supplies. This data can further help to inform real-time staffing issues, moving away from the traditional staffing based on the ward census at midnight and toward a more flexible and targeted model that considers staff workload and health as well as patient needs.

At Florida Hospital Celebration Health, staff members wear location tags that attach to their clothing, broadcasting an ultrasound signal that is tracked through sensors in the ceiling. The movement of staff on duty can be analyzed in a “heat map” view that shows the areas of greatest activity, making it possible to pinpoint locations and identify tasks that may have been distributed inefficiently. While the hospital has not yet progressed to the point of reallocating staff based on the movement sensor analysis, it has found opportunities to redesign inefficient workflows.

For example, staff responsible for taking used IV pumps to be sterilized had been performing that task at 6 a.m. — a time when there was much patient and staff activity, hence preventing them from performing this task at optimal efficiency. Following the analysis of the workflow, this task was reassigned to a less busy time, between 1 a.m. and 3 a.m. In another example, it was found that nurses were assigned to take care of patients located at opposite ends of a ward, resulting in unnecessary scrimmaging back and forth.2

Case studies show that these systems can save each nurse 1 mile per day in extra steps, reduce phone calls by 75%, and reduce time spent searching for staff by 64%.3



Equipment and Medical Supplies DataAn equally important part of hospital workflow, aside from the flow of people (staff and patients), is the flow of equipment and medical supplies through a hospital facility. In order to reduce shortages and allow quick response, hospitals can implement sophisticated mechanisms to continuously track the stock of certain medical supplies.

For example, many hospitals monitor supplies and medication via barcodes attached to each individual item. A centralized data collection server can then send out signals if stock is running low — before it is empty — and trigger automatic restocking by the appropriate maintenance staff. This improves the efficiency of nurses and doctors, who no longer need to take trips back and forth between the supply area and their department when necessary items are running low.

A similar system can be implemented to reduce downtime of high-tech medical equipment. Clinical Technology Management systems collect and monitor data from key pieces of medical equipment throughout a hospital, and automatically trigger planned and corrective maintenance. This has proven to improve hospital efficiency.

After adopting Sodexo’s clinical technology management solution, the Central Maine Medical Center achieved a 98% response rate for equipment failure within 15 minutes, 97% uptime, improved inventory management, and more than 20% cost savings.4



USING BIG DATA TO MONITOR EMPLOYEE EXPERIENCE, HEALTH AND WELL-BEINGFacility managers can also utilize Big Data to monitor employee activities, habits and work styles as part of a larger effort to optimize employees’ workplace experience, including ensuring their psychological and physical well-being.

Workplace ExperienceAnalyzing employees’ usual activities can allow a hospital to better understand specific “employee market segments” and create a more targeted workplace experience matching the particular needs and desires of different categories of employees. Just as consumer product companies have learned to define and cater to small market niches, FM can also utilize Big Data to better understand the interplay between individual work styles, workplace design and productivity.

Talent shortages, coupled with the trend toward unique workplace experiences, are driving hospitals to design customized workplaces for specific hospital departments and staff.

For example, mid-career nursing professionals no doubt have very different workplace preferences than younger entry-level physicians. The good news is that the data necessary to inform these decisions is — or soon will be — readily available. Employees’ demographic information is already easily accessible, but Big Data can take this a step further. Observing employees’ daily work routine, food consumption patterns and social media usage can help an employer create an even more comprehensive picture of staff preferences, thereby allowing more targeted design of the hospital workplace experience.

Employees’ Psychological Well-BeingTo further improve employee well-being and mental health, there are smartphone apps that can monitor and report the volume, tone of voice, and speed of a phone or in-person conversation between employees. More refined applications and devices can also measure an individual’s stance and position when speaking to other employees. While this may sound suspiciously like a “Big

Brother”-type phenomenon, the resulting group-based emotional assessment could be highly useful.

For example, such an assessment could alert hospital management to a decline in employee engagement or brewing work-related controversy between individuals and groups. Furthermore, this type of data can be recorded and analyzed, even without recording the actual topic and content of conversation, thus guaranteeing some form of privacy and — especially in the hospital setting — confidentiality when patient information is being discussed.

Employees’ Physical Well-BeingIn a hospital setting, one of the key risks is that of infection and the rapid spread of contagious diseases. Big Data can help ensure the health and safety of hospital employees who work with diagnosed or at-risk patients. For example, monitoring the body temperatures and coughing frequency of a group of nurses in a ward could raise a yellow flag about the spread of potentially contagious illnesses to or by patients.

This warning could trigger preliminary steps to prevent further infection in the hospital. If it is too late for prevention, such a system could at least indicate to management an advance warning about impending sick leaves and the need to anticipate substitute employees to maintain a critical work flow. The same techniques can be utilized for monitoring employee exposure to radiation and hazardous biological waste.



Hand Hygiene PracticesWith hospital-acquired infections costing $30 billion a year, more and more emphasis is being placed on hand hygiene compliance by hospital staff. Big Data systems can help to increase compliance, thereby improving both employee and patient health. For example, technology that monitors hand hygiene behaviors can alert management to poor compliance among specific departments. Hospital managers can then elect to implement a solution, e.g., a targeted awareness campaign or better integration of sanitation into the workflow in certain locations.

Simple monitoring can be taken a step further with the use of more technologically-advanced badges and sensors. Attached to either employee clothing or fixed in designated rooms, these devices can send out subtle reminders to employees to wash their hands following certain procedures and patient contact. More and more hospitals are using these new monitoring technologies, which have shown to be associated with significant reductions in infections. Not only is this good for patients, but it also contributes to the health and safety of hospital staff, thereby leading to an overall increase in productivity.

USING BIG DATA AGAINST THE BACKDROP OF THE CHANGING HOSPITAL WORKFORCEWhether it is monitoring of patient, staff or equipment flow through a hospital, collection and monitoring of data can help uncover inefficiencies and inform staffing decisions in a hospital. Against the backdrop of the changing nature of the hospital workforce, this may be particularly important.

Given the growing talent shortages of nurses and primary care physicians, in combination with the globalization of talent markets and seemingly insatiable demand for healthcare, it is very likely that the future hospital will employ fewer full-time staff members. Instead, there will be increased use of part-time and “free agents” — physicians and support staff whose skills can be utilized by several different hospital locations. This trend is already particularly prominent among hospitals who provide services from highly specialized doctors.

Not only will this have implications for the functioning of a hospital HR team, but it will also lead to more distributed departmental teams. Hospital FM may become responsible for providing a functioning facility for fewer full-time employees and more part-time clinicians who come and go as they are needed on particular shifts.

Big Data initiatives may be invaluable in helping FM leaders ensure the productivity, safety and well-being of a frequently changing hospital workforce.



CAVEATS OF USING BIG DATA IN HOSPITAL FMDespite the business case and value for hospital FM to utilize Big Data, caution must be heeded in implementing any new initiatives.

Observer EffectsAccording to the Heisenberg Uncertainty Principle, in scientific research, one cannot measure anything without having an impact on the thing one is measuring. In social sciences, the same idea holds. When people are aware that they are being observed and measured, they change their behavior. One can argue whether or not they change it for the better or for worse — but the effect most certainly is never neutral. With more media coverage and publicity of Big Data, it is safe to say that employees, in hospitals and elsewhere, are well aware of how their work and personal data can be — and is — used.

It is also important to note that staff members usually know the work behaviors their managers are observing and seeking to change. Hence, employees may seek to manipulate data and their observed behavior in a way that produces the results and indicators management wants. This is not necessarily an undesirable and adverse

employee behavior; however, when implementing Big Data initiatives, management must be aware of this phenomenon, so as not to run the risk of interpreting any data without the appropriate context.

Communication and Informed ConsentAcross all industries, it is unquestionably unwise to implement data monitoring programs without informing employees — in healthcare, it may be nearly impossible to do so. In fact, one of the biggest risks with Big Data initiatives is for management to begin collecting and analyzing data without informing employees — and to then have this discovered by the employees.

In social sciences research, there are strict ethics requirements about “informed consent.” Before the start of a research project, subjects must be clearly informed about which data will be collected, how and where the data will be stored, who has access to it, and how it will be used. This type of “opt-in” approach, whereby research subjects must consent to the data collection, will likely become part of the “research” process organizations use when they implement a Big Data initiative. This approach is particularly important in the hospital setting, which is already defined by privacy and data confidentiality.



It is vital that employees receive clear and transparent information about any Big Data hospital initiative.

Being open about the existing issues and the reasons for implementing such initiatives not only conveys respect for employees, but also alleviates concerns about “Big Brother” or management wanting to interfere with the clinical staff’s jobs. An even more powerful means of engaging staff is to include them in planning and design conversations about the data collection effort and its purpose, intended use and benefits.

Overcoming Staff ConcernsThe business case for utilizing Big Data is clear from a hospital-wide perspective. Few hospital workers would argue against the value of using employee productivity data to report on hospital workforce ROI to senior management. However, when an organization begins monitoring individual employee work activities, such initiatives can become highly contentious. There is a risk that the employees will see the effort as

micro-management and possibly even a precursor to workforce reductions.

With this in mind, managers must be very careful that the data collection is not seen as indicating a lack of trust in employees. This is especially important in the healthcare field, given the large amount of intrinsic motivation and morale that are often key drivers of employee engagement.

In particular, nurse tracking systems have met with resistance by staff members. For example, the management of Florida Hospital Celebration Health admits that at first, the idea of nurse tracking was met with a considerable degree of fear by the staff. However, after the nurses were assured that the data collected would be used only to track work flow — not individual performance and punitive measures — there were no issues.5

These objections do not mean that Big Data initiatives and systems cannot be used; rather, the question is how they should implemented. As previously stated, it is vital for management to communicate with and engage employees, to ensure that the data is effectively used to promote hospital staff well-being and productivity.

The table below highlights some key considerations that hospital FM professionals should keep in mind when choosing to implement a Big Data initiative.

Do’s and Don’ts of Using Big Data in Hospital FM

Do’s Don’ts » Involve all affected employees from the start.

» Explain in clear terms the rationale for data collection, what data will be tracked, how it will be tracked, and how it will be used.

» Be open and transparent about all data collection efforts. Require employee consent and establish opt-in procedures.

» Continuously improve the data collection mechanisms and review if and what data is proving truly useful.

» Implement some form of employee benefit or incentive scheme in return for sharing their data.

» Always consider context when analyzing the data; data is an enabler, not a solution.

» Be aware that data collection will change your organizational culture and employee behavior.

» Don’t collect data that will not be used.

» Don’t be a “data hoarder,” collecting any and all data you can get your hands on; know why you are collecting every piece of data.

» Don’t manipulate the data or discard unfavorable data.

» Don’t sell or disclose data without prior permission.

» Don’t use data for individual punitive actions.

» Don’t collect data in secret.



CONCLUSIONThe promise of Big Data in hospital FM is enormous. Big Data initiatives have the potential to contribute to employee productivity via reducing wasted time and allowing employees to spend time on truly value-adding activities. Big Data can also help ensure employee safety, health and well-being, which furthers their quality of life (see Figure 1).

Yet particularly in the healthcare setting, in which privacy and confidentiality have always been a key issue, management must be careful about how initiatives are implemented. Data collection should not be used to micro-manage hospital staff, who are often driven by intrinsic motivation.

In healthcare, many mechanisms and initiatives for data collection already exist — from patient records, to monitoring of machines and rooms — and the foundation for more large-scale FM initiatives has already been established. Going forward, it will be essential that data be extracted and consolidated from the various systems that are already in place and then further complemented by the introduction of new data collection systems. This data must also be made available to hospital management and employees, to inform decision-making both in the C-Suite and in the ward.

Figure 1. Big Data in Hospital FM — Initiatives, Pathways, and Outcomes

INITIATIVES

PATHWAYS

OUTCOMES

Monitor the Physical EnvironmentMonitor the Workflow

Monitor Employee Activities

Reduce Wasted Time & Ine�cienciesEnsure Employee Safety,

Health and Comfort

Increased Hospital Employee Productivityand Quality of Life



REFERENCES1. California Healthcare Foundation. (2011). Using Tracking Tools to Improve Patient Flow in Hospitals. Retrieved from http://www.

chcf.org/publications/2011/04/tracking-tools-improve-patient-flow

2. Carr, D. (2014). Florida Hospital Tracks Nurses Footsteps, Work Patterns. InformationWeek. Retrieved from http://www.informationweek.com/healthcare/analytics/florida-hospital-tracks-nurses-footsteps-work-patterns/d/d-id/1127700

3. Versus RTLS. (2015). Nurse Locator. Retrieved from http://www.versustech.com/rtls-solutions/nurse-locator/

4. Sodexo. (2015). At a Glance Case Study: Central Maine Medical Center.

5. Zimlich, R. (2014). Florida hospital using tracking devices on nurses to improve workflow efficiencies, collect patient data. Healthcare Traveler. Retrieved from http://healthcaretraveler.modernmedicine.com/healthcare-traveler/content/tags/florida/florida-hospital-using-tracking-devices-nurses-improve-work

James WareJames Ware, PhD, is a meeting design strategist. A former Harvard Business School professor, he has invested his entire career in enabling change leaders to take charge of their organizational futures by exploring, interpreting, and leveraging the changing nature of work, the workforce, and the workplace.

Jim is the founder and executive director of The Future of Work…unlimited and the global research director for Occupiers Journal Limited. He has co-authored several books about the digital economy and its implications for leadership and organizational performance. His most recent book, Making Meetings Matter: Leading Powerful and Effective Corporate Conversations in the Digital Age, will be published in February 2016.

Jim holds PhD, MA, and BSc degrees from Cornell University and an MBA (With Distinction) from the Harvard Business School. He lives and works in northern California.

Lisa HermsLisa Herms, MSc, is a Research Analyst with Sodexo’s Innovations 2 Solutions (I2S) team. In her role on the I2S team, she leads a variety of research initiatives and produces thought leadership in both the healthcare and corporate sectors, with a focus on quality of life and well-being in the workplace. Her areas of expertise include public health, international health policy, health economics, and behavioral health economics.

Prior to joining Sodexo, Lisa was an intern at the Office of Health Economics and at various financial institutions in the United States and in Europe. She received her MSc in International Health Policy (Health Economics) from the London School of Economics and Political Science in 2015, and a BA in Economics from the University of St. Gallen in 2014.

ABOUT THE AUTHORS