Rich Miller
How gaps in data flow hit your bottom line
April 30, 2015
By Rich Miller
Rich Miller is co-founder and CEO of OpenTempo, which provides workforce optimization and staff scheduling solutions for health care organizations.
The benefits of interoperability for Electronic Health Records are well known – better outcomes, increased efficiency, and informed decision-making. Yet very few healthcare organizations make the leap to seeing the benefits of interoperability for their staffing data. Staffing data, such as staff schedules, time tracking, and payroll, are typically housed in completely distinct systems. This creates a knowledge gap that hits healthcare organizations directly in their bottom line, affecting costs for overtime and locums, and creating delays or cancellations for patient procedures.
Data flow and overtime
Most practices are not aware of overtime and under-time risk until the end of a pay period. It is rare that a practice knows in the middle of a payroll period which provider is going into overtime. Even rarer are the practices that know how many of their providers are not being utilized for their full 40-hour commitment. A lack of visibility into the data creates an imbalance in staff utilization, one that frequently leads to excessive overtime costs and underutilized staff.
To correct this situation, practices need to close the gap between data in the staff schedules and data in the payroll system. By integrating scheduling data with time tracking data (what is scheduled to be worked with what has already been worked), practices are able to see who is at risk for overtime and who is being under-utilized before the pay period ends. Seeing this information before the end of the pay period is critical, as that is the crucial moment when practices can adjust staffing levels and prevent excessive overtime from occurring. This gives practices much better control over their labor budget, which is 50-60 percent of an average practice’s operating budget.
Using data to better match staff with patient need
Further benefits can be realized by linking the data among staff schedules, EHRs, and provider credentialing information. This seemingly unlikely trio of information allows staff to be assigned in accordance with patient need, while simultaneously keeping track of provider credentials.
By integrating these areas, practices are able to ensure they have the right credentialed providers for each day’s patient load. A seemingly obvious step, it requires data sharing among systems that are typically unable to communicate with each other. As a result, many practices find themselves short-staffed, or inappropriately staffed, indicating a situation where they have sufficient providers available, but the credential mix is incorrect, or being short staffed based on the volume of patient cases.
To cope, practices frequently resort to hiring locums, at great expense to their operating budget. Worse, when practices have to scramble to find an available provider with the needed credentials, patients experience delays. This is a risk to patient care, as well as to patient satisfaction scores. Moreover, it leads to rooms and equipment standing idle while patients wait for an appropriate provider.
A data flow concern specific to academic medical centers
Academic medical centers experience a distinct type of data gap when it comes to staff. As they need to work resident rotations into their daily and call schedules, the common approach of keeping separate resident and faculty/staff schedules works against them. When these two systems work together, not only does it take less time to assign staff, but it can clearly be seen when, for example, a credentialed resident can be assigned to a case in lieu of unavailable faculty. By linking these systems, AMCs are able to view the entirety of their available staff, and make staffing decisions accordingly.
By closing the gaps around staffing data, healthcare organizations are able to make significant improvements in workforce efficiency, as well as reducing excessive labor costs. Data integration in this area impacts overtime costs, locums, and patient delays. Academic medical centers see an additional benefit, by integrating schedule data for residents and permanent staff.
Rich Miller is co-founder and CEO of OpenTempo, which provides workforce optimization and staff scheduling solutions for health care organizations.
The benefits of interoperability for Electronic Health Records are well known – better outcomes, increased efficiency, and informed decision-making. Yet very few healthcare organizations make the leap to seeing the benefits of interoperability for their staffing data. Staffing data, such as staff schedules, time tracking, and payroll, are typically housed in completely distinct systems. This creates a knowledge gap that hits healthcare organizations directly in their bottom line, affecting costs for overtime and locums, and creating delays or cancellations for patient procedures.
Data flow and overtime
Most practices are not aware of overtime and under-time risk until the end of a pay period. It is rare that a practice knows in the middle of a payroll period which provider is going into overtime. Even rarer are the practices that know how many of their providers are not being utilized for their full 40-hour commitment. A lack of visibility into the data creates an imbalance in staff utilization, one that frequently leads to excessive overtime costs and underutilized staff.
To correct this situation, practices need to close the gap between data in the staff schedules and data in the payroll system. By integrating scheduling data with time tracking data (what is scheduled to be worked with what has already been worked), practices are able to see who is at risk for overtime and who is being under-utilized before the pay period ends. Seeing this information before the end of the pay period is critical, as that is the crucial moment when practices can adjust staffing levels and prevent excessive overtime from occurring. This gives practices much better control over their labor budget, which is 50-60 percent of an average practice’s operating budget.
Using data to better match staff with patient need
Further benefits can be realized by linking the data among staff schedules, EHRs, and provider credentialing information. This seemingly unlikely trio of information allows staff to be assigned in accordance with patient need, while simultaneously keeping track of provider credentials.
By integrating these areas, practices are able to ensure they have the right credentialed providers for each day’s patient load. A seemingly obvious step, it requires data sharing among systems that are typically unable to communicate with each other. As a result, many practices find themselves short-staffed, or inappropriately staffed, indicating a situation where they have sufficient providers available, but the credential mix is incorrect, or being short staffed based on the volume of patient cases.
To cope, practices frequently resort to hiring locums, at great expense to their operating budget. Worse, when practices have to scramble to find an available provider with the needed credentials, patients experience delays. This is a risk to patient care, as well as to patient satisfaction scores. Moreover, it leads to rooms and equipment standing idle while patients wait for an appropriate provider.
A data flow concern specific to academic medical centers
Academic medical centers experience a distinct type of data gap when it comes to staff. As they need to work resident rotations into their daily and call schedules, the common approach of keeping separate resident and faculty/staff schedules works against them. When these two systems work together, not only does it take less time to assign staff, but it can clearly be seen when, for example, a credentialed resident can be assigned to a case in lieu of unavailable faculty. By linking these systems, AMCs are able to view the entirety of their available staff, and make staffing decisions accordingly.
By closing the gaps around staffing data, healthcare organizations are able to make significant improvements in workforce efficiency, as well as reducing excessive labor costs. Data integration in this area impacts overtime costs, locums, and patient delays. Academic medical centers see an additional benefit, by integrating schedule data for residents and permanent staff.