Engineering a Learning Healthcare System: Using Health Information Technology to Develop an Objective Nurse Staffing Tool 1
Ellen M Harper, RN, MBA, DNP student1 CernerCorporation, Kansas City, Missouri, USA
Abstract Nurses represent the largest proportion of direct healthcare providers. Overstaffed or understaffed units will have implications for the quality, cost, patient, and nurse satisfaction. It is vital that nurses are armed with appropriate instruments and data to help them plan and implement efficient and effective nursing teams. A compelling case is made for the association between nursing care and clinical, quality, and financial outcomes. Even though there is a great body of work on the correlation, there is little agreement on the best approach to determine the correct balance between the patient-to-nurse ratios. The sheer number of variables depicted in the literature suggests why precise evidenced based formulas are difficult to achieve. This paper will describe a practice based knowledge generation mixed methods study using detailed observation and electronic health record abstraction to generate a structural equation for use in predicting staffing needs. Introduction Nurses represent the largest proportion of direct providers, thus also the largest single labor cost for acute care facilities, which are increasingly challenged to be financially viable1. Consequently, decisions about the size and mix of nursing teams are critical for health service managers2,3. Overstaffed or understaffed units will have implications for the quality, cost of patient care, patient satisfaction, and nurses’ job satisfaction4. It is vital that nurses are armed with appropriate instruments and data to help them plan and implement efficient and effective nursing teams. Leaders must balance nursing staffing between financial viability and quality of care. A clear and compelling case is made for the association between nurses and nursing care with clinical, quality, and financial outcomes2-5. Research investigating links between hospital nurse staffing and patient outcomes began with studies examining mortality, and expanded to include adverse events and infections4,6. Even though there is a great body of work on the correlation3,6,7,9, there is little agreement on the best approach to determine the correct balance between the patient-to-nurse ratios. An important study introduced the value equation: quality divided by costs with the value being better with increased quality at less cost4. Two studies examined the inconsistencies and limitations in existing studies, and design issues of current methods and measures7,8. Resources are needed to assist in this balance. The sheer number of variables and countless linkages depicted in the literature suggests why precise evidenced based formulas for deploying nursing staff to ensure safe, high-quality patient care are difficult10-13. Evidenced based guidelines for allocating resources to ensure optimal outcomes in acute care cannot be offered until all factors are considered including working environments admissions, discharges, transfers (ADT), staffing beyond head counts and skill mix, patient needs, processes and outcomes of care can be measured with precision13-15. As part of the development of a practice based, sophisticated “real time” software solution to assist nursing leadership in providing the right level of nursing care. The objectives of the exploratory work are (a) to determine the programming process necessary to accurately identify from the EHR the definitive patient care data to ensure quality coverage on an hour by hour basis in acute care facilities and (b) to describe the development of an overarching model, the design and results of data collection, and the basis for the software solution will be described. Background Workload measurement and patient classification systems (PCS) were developed to measure the amount and type of nursing care per patient to predict staffing needs in the acute care environment. Connor in 196119 conducted the seminal work on the concept of nursing intensity and developed a PCS that matched nursing resources with patient demands. Methods that link PCS data and staffing are mixed20. No common definition of nursing intensity has been accepted by the profession15 nor has a universal model to measure nursing intensity become apparent 21. Weaknesses, gaps, and concerns of omissions were identified during development and testing of PCS
tools including: dissimilar and highly variable scoring of different tools on the same patient21, the measurement of patient severity19, and use of clinical decision making12. The lack of confidence in these systems waned because they are subjective and do not include the complexity of care or the nurses’ skill level 20. Additionally, these systems are costly and time consuming to implement, and are stand-alone systems that are not integrated into the EHR, payroll, staff scheduling, or cost accounting systems. The potential to use healthcare information technology (HIT) as a tool to assess nurse staffing decisions is a rather new phenomena explored by some of the thought leaders in nursing informatics18,4. Healthcare software companies have the potential to offer hospitals objective tools that use practice based HIT and mathematical formulas to analyze complex practice based data drawn from sources such as historical patterns of patient activities; the popularity of surgeries on certain days; recovery times from various conditions; and daily admission and discharge records to generate new knowledge. Discovery of the right combination of “hard data” and “soft data” is the impetus for this work to develop an innovative software program which will more precisely determine the workload for nursing care hours, based on an algorithm developed from data on patient care needs as retrieved from the EHR and validated by observational correlation. The basis of the algorithm relies not on the overall diagnosis, but the hour-by-hour plethora of information about patients (i.e. risk factors, interventions, orders, medications, and activity), nurses, hospital policies and other factors that can be used to make more accurate staffing predictions. The unit specific nature of the data is an important feature of the instrument. It allows the focus of decisions to be on what is occurring on the specific unit involving its unique patient population and nurses. Methods The study used a quantitative dominant transformative mixed methods research method and correlational design, which involved collecting both the quantitative and qualitative data simultaneously and then merging the data by transforming the qualitative data into quantitative data for correlation between the variables. The data collection was organized into three primary groups: Direct Care, Indirect Care and Unit Related. Direct Care is when the nurse interacts with the patient directly (e.g. dressing change, talking to patient/family, medication administration). Indirect Care is when the nurse performs patient care activities, but not necessarily with the patient (e.g. documenting, preparing medications, and talking to nurse/doctor about patient). Unit Related Care included activities pertaining to the hospital unit or staff (e.g. shift report, meals, schedules, cleaning, etc.) The study utilized four sets of data. 1. Direct observation of the registered nurses (RN). 2. Direct observation of the patients. 3. Data extracted from the EHR for the observed patients. 4. Data extracted from the EHR of the unit’s admissions, transfers and discharges over the time period. Figure 1 represents the new model created to accurately predict staffing using the Care Demand Index TM. Figure 1
Two data collection sheets were developed for the observation aspect of the study. The RN observation and patient observation collection sheets were designed to efficiently capture and organize activity data relevant to the nurse’s and patient care activities. The qualitative observation time study was conducted at a community hospital in southern California. Direct observations of nurses and patients were completed over the course of 72 hours on a medical/surgical adult only unit. The direct patient activities were captured by observing six patients each day over
the time period. The in-direct patient activities and unit activities were captured by observing a RN over the same 72 hours. RN observers were assigned two patients each for their 12 hour shift. Using a stop watch, each patient observer was responsible for recording the type of caregiver that entered the patients’ rooms, what activities were performed, time of day, and how long each activity took. Indirect and Unit care was captured by observing one RN each 12 hour shift. Again using a stop watch, the nurse observer was responsible for recording what type of activity performed, time of day, and how long each activity took. . The quantitative aspect of this mixed method study used the historical patterns of nurse and patient activities found within the EHR. The clinical data was extracted from the EHR for the observed patients. We used a query tool to extract all of the electronic documentation, clinical events, medications, interventions, outcomes and orders on the study group patients. Each data element included the date and time the activity was scheduled for, the time the activity occurred and who documented it. The number and times of the ADT events were pulled for the unit for the 72 hours in which the study took place. Results Over the course of the 72 hours of observation the Direct Care Total n = 834. Each Activity Group was evaluated individually and then by time of day (broken out by 4 hour increments). Figure 2 demonstrates the breakdown of the Assessment Activity Group by time of day. This data helped us to focus on the Activity Groups that constituted the most time and the heaviest time of day they took place. Figure 2 Direct Care Observation Overall Cohort Percentiles 50th
Total Time Spent doing activity (mins)
N
Mean
SD
0:00 to 4:00
28
1.29
0.71
1
1
1
36
4:00 to 8:00
22
2.41
1.65
1
1.5
4
53
8:00 to 12:00
59
2.34
2.68
1
1
3
138
12:00 to 16:00
33
2.42
2.08
1
1
3
80
16:00 to 20:00
31
3.90
4.41
1
2
5
121
20:00 to 24:00
22
1.86
1.83
1
1
2
41
25th
75th
P-Value
Assessments (shift, ongoing, pain, episodic)
0.002841
The observation of the Indirect and Unit Care Total n = 635. Similar to the Direct Care, the Indirect Activities were also broken out by 4 hour time slots. Figure 2 demonstrates the breakdown of the Charting, Documentation Activity Group. We then could see the relationship between charting activities regardless of the time of day they took place showed that charting was often recurring regardless of the time of day, yet had variation in the mean time to chart between the day and night hours. Figure 3 Overall Cohort N
Mean
SD
13.01
25th
Percentiles 50th
75th
Total Time Spent doing activity (mins)
10
12
30
175
P-Value
Charting, Documentation 0:00 to 4:00
9
19.44
4:00 to 8:00
6
14.33
9.99
8
11
25
86
8:00 to 12:00
25
5.88
7.64
2
2
8
147
12:00 to 16:00
21
6
8
2
3
9
134
16:00 to 20:00
15
5.53
4.85
2
4
10
83
20:00 to 24:00
10
16.70
25.65
3
5
9
167
0.001719
The quantitative data was similarly reviewed. Multiple iterations of data mapping and review were required to accurately map the EHR data into the respective Activity Groups used in the observation study. Additionally, the EHR has a plethora of data and there were challenges to accurately identify the start and stop times for each activity (i.e. medication administration) in order to assign to an activity group, the time of day, and the role of the caregiver who performed the activity. Figure 4 gives a summary of the EHR clinical data.
Figure 4
Total Average/Patient Std Dev Min Max
Clinical Events 23,794 2,379 956.2 1,166 4,263
Orders 28,712 2,871 1,559.2 1,016 6,219
Order Actions 28,712 2,871 1,559.2 1,016 6,219
Tasks 470 47 17.1 19 80
Plan Entries 524 52 41.6 0 130
Documentation Items 14,375 1,438 522.8 742 2,157
When all the datasets were mapped to the Activity Groups we were able to transform the qualitative data into quantitative data for correlation between the variables. Using Assessment Activity group (Figure 5) we were able to turn data (orders) back into time, thus able to determine the amount of patient care data needed to accurately predict the work needed for each patient within a 4 hour time period. Figure 5 Overall Cohort Assessments
Total Number of Active Total Number of Clinical Total Number of Orders Events Logged Documentation Events
Total Number of Care Minutes Provided (per Observation Data)
Total Number of Active Orders Per 1 Minute of effort B / E)
7:00 thru 10:00
584
169
48
142
11:00 thru 14:00
678
74
154
97
6.99
15:00 thru 18:00
746
63
21
65
11.48
19:00 thru 22:00
744
191
20
92
8.09
23:00 thru 2:00
578
35
151
33
17.52
3:00 thru 6:00
577
37
42
31
18.61
4.11
The results of the study were able to tell us the following correlations and helped focus the analysis 1.
The volume of activity – the study helped to point out the activities that make up the greatest share of direct care activities. We are not confident that we can collapse activity groups to a smaller number
2.
The amount of time it takes to perform activities.
3.
The relationship between direct and indirect care
4.
The impact of time of admission. There were 10 patients admitted to the unit during the 72 hours of observation.
Discussion To our knowledge this is the first attempt at using informatics, the computer and digital patient data to conduct research to objectively estimate real time RN staffing needs. Accurate predictive models depend on identifying which variables affect future resource demand the most. Although the pilot study was small, it has allowed us to use data mining techniques to determine which variables (orders, procedures, clinical documentation, etc.) have the greatest impact on the amount of direct care a patient is getting. Identifying these variables is not in general possible for human being to do; in fact “hunches” are usually wrong and reflect inside-the–box thinking and biases. Our hypothesis is that the amount of indirect care required is proportional to the amount of direct care the patient is receiving plus the amount of ADT activity that is taking place on the unit. The coefficient of proportionality would be dependent on the processes of a specific hospital unit and potentially the patient’s variables (age, BMI, etc.). Using a three-phase approach, the observation and retrospective data collection (Phase 1) described in this paper is the foundation for which gathering the qualitative and quantitative data. Predicating patient admissions (Phase 2) will require a retrospective analysis of past patient arrivals to determine how many patients are expected to arrive by day of week, hour of day, and accounting for seasonal variations. Along with real time monitoring of admission orders as well as what is happening in other units or areas of the hospital where new patients may be transferred in. The observation unit had seven admissions from the Operating Room (OR) and three from the
emergency department. The second step to calculate patient census is to determine when a patient will depart form the unit. Similar to predicting future admissions, determining length of stay and departures can be done by historic review of the time patient’s spent on the unit by patient variables and real time monitoring of key discharge or transfer information. Our goal is to accurately predict 24 hours. Phase 3 will test the accuracy and validate the algorithms created in Phase 2 to predict the direct care. Rather than direct observation, the study will utilize Real Time Location System (RTLS). RTLS provide hospital administrators with actionable information regarding the location, status and movement of equipment and people. Traditionally, RTLS has been used to track equipment, tracking patients and tracking providers. This study will use patient and nurses tracked to determine the time spent engaged with direct care to predict the indirect care the patient requires. References 1. 2. 3. 4. 5. 6. 7.
8. 9.
10. 11.
12. 13.
14. 15. 16. 17. 18. 19. 20.
Buerhaus, P. I. (2010, March-April). It’s time to stop the regulation of hospital nurse staffing dead in its tracks. Nursing Economics, 28(2), 110-113. Retrieved from http://www.nursingeconomics.net Aiken, L., Clark, S., Sloane, D., Sochalski, J., & Silber, J. (2002). Hospital nurse staffing and patient mortality, nurse burnout. AMA, 288(16), 1987-1993. Eck Birmingham, S. (2010, June). Evidence-based staffing: the next step. Nurse Leader, 24-35. doi: 10.1016/j.mnl/2010.03.003 Douglas, K. (2010, January-February). The human side of staffing. Nursing Economics, 28(1), 56-62. doi: Retrieved from http://web.ebscohost.com/ehost American Nurses Association. (2010). Registered Nurse Safe Staffing Act of 2010 (S. 3491/ H.R. 5527). Retrieved from http://www.safestaffingsaveslives.org/ Anderson, R., & Kerfoot, K. (2009). The time has come for evidence-based staffing and scheduling. Nursing Economics, 27(5), 277-279. Clark, S. P., & Donaldson, N. E. (2008). Nurse staffing and patient care quality and safety. . In Agency for Healthcare Research and Quality (Ed.), Patient Safety and Quality: An Evidence-Based Handbook for Nurses (pp. 1-25). Retrieved from http://www.ahrq.gov/qual/nurseshdbk/nurseshdbk.pdf. Hurst, K. (2005, June). Relationships between patient dependency, nursing workload and quality. International Journal of Nursing Studies, 42(), 75-84. Retrieved from http://www.sciencedirect.com/ Hughes, R. G. (2008). Nurses at the sharp end of patient care. In Agency for Healthcare Research and Quality (Ed.), Patient Safety and Quality: An Evidenced-Based Handbook for Nurses (pp. 1-30). Retrieved from http://www.ahrq.gov/qual/nurseshdbk/nurseshdbk. Institute of Medicine. (2010). Future of nursing: leading change, advancing health. In . Washington, DC: The National Academies Press. Schroeder, R. E., Rhodes, A. M., & Shields, R. E. (1984). Nurse acuity systems: CASH vs. GRASP: (a determination of nurse staff requirements. Nursing Forum, 21(), 72-77. doi: 10.1111/j.17446198.1984.tb01136.xRetrieved from http://onlinelibrary.wiley.com Seago, J. A. (2001). Nurse staffing, models of care delivery, and interventions. In (Ed.), Agency for Health Care Research and Quality (Publication No. 01-E058,. Retrieved from http://www.ncbi.nlm.nih.gov/books/ Bakken, S., Lucero, R., Yoon, S., & Hardiker, N. (2011). Implications for nursing research and generation of evidence. In A. Cashin & R. Cook (Eds.), Evidenced-based practice in nursing informatics (pp. 113-127). : Hershey, PA. Zone-Smith, L.K. (2004). Predicting real-time nurse staffing needs in hospitals: A new tool to measure nursing intensity. Journal of Nursing Administration, 34,210. Seago, J. A. (2002). The California experiment: Alternatives for minimum nurse-to-patient ratios. Journal of Nursing Administration, 32(1), 48-58. doi: 10.1097/00005110-200201000-00012 Hyun, S., Bakken, S., Douglas, K., & Stone, P. W. (2008, May-June). Evidence-based staffing: potential roles for informatics. Nursing Informatics, 26(3), 159-158. Retrieved from http://www.mylawagnerpr.com. Connor, R.J. (1960) A hospital inpatient classification system. Dissertation Abstracts International, 21(565). Harper, K., & McCully, C. (2007). Acuity systems dialogue and patient classification system essentials. Nursing Administration Quarterly, 31(4), 284-299 Welton, J. M., Unruh, L., & Halloran, E. J. (2006). Nurse staffing, nursing intensity, staff mix, and direct nursing care costs across Massachusetts hospitals. Journal of Nursing Administration, 36, 416-425 O’Brien-Pallas, L., Thomson, D., McGillis Hall, L., Pink, G., Kerr, M., Wang, S., Meyer, R. (2004). Evidencebased standards for measuring nurse staffing and performance. Retrieved from Canadian Health Services Research Foundation Web site (www.chrsf.ca)
Ellen M Harper, RN, MBA, DNP student1 CernerCorporation, Kansas City, Missouri, USA
Abstract Nurses represent the largest proportion of direct healthcare providers. Overstaffed or understaffed units will have implications for the quality, cost, patient, and nurse satisfaction. It is vital that nurses are armed with appropriate instruments and data to help them plan and implement efficient and effective nursing teams. A compelling case is made for the association between nursing care and clinical, quality, and financial outcomes. Even though there is a great body of work on the correlation, there is little agreement on the best approach to determine the correct balance between the patient-to-nurse ratios. The sheer number of variables depicted in the literature suggests why precise evidenced based formulas are difficult to achieve. This paper will describe a practice based knowledge generation mixed methods study using detailed observation and electronic health record abstraction to generate a structural equation for use in predicting staffing needs. Introduction Nurses represent the largest proportion of direct providers, thus also the largest single labor cost for acute care facilities, which are increasingly challenged to be financially viable1. Consequently, decisions about the size and mix of nursing teams are critical for health service managers2,3. Overstaffed or understaffed units will have implications for the quality, cost of patient care, patient satisfaction, and nurses’ job satisfaction4. It is vital that nurses are armed with appropriate instruments and data to help them plan and implement efficient and effective nursing teams. Leaders must balance nursing staffing between financial viability and quality of care. A clear and compelling case is made for the association between nurses and nursing care with clinical, quality, and financial outcomes2-5. Research investigating links between hospital nurse staffing and patient outcomes began with studies examining mortality, and expanded to include adverse events and infections4,6. Even though there is a great body of work on the correlation3,6,7,9, there is little agreement on the best approach to determine the correct balance between the patient-to-nurse ratios. An important study introduced the value equation: quality divided by costs with the value being better with increased quality at less cost4. Two studies examined the inconsistencies and limitations in existing studies, and design issues of current methods and measures7,8. Resources are needed to assist in this balance. The sheer number of variables and countless linkages depicted in the literature suggests why precise evidenced based formulas for deploying nursing staff to ensure safe, high-quality patient care are difficult10-13. Evidenced based guidelines for allocating resources to ensure optimal outcomes in acute care cannot be offered until all factors are considered including working environments admissions, discharges, transfers (ADT), staffing beyond head counts and skill mix, patient needs, processes and outcomes of care can be measured with precision13-15. As part of the development of a practice based, sophisticated “real time” software solution to assist nursing leadership in providing the right level of nursing care. The objectives of the exploratory work are (a) to determine the programming process necessary to accurately identify from the EHR the definitive patient care data to ensure quality coverage on an hour by hour basis in acute care facilities and (b) to describe the development of an overarching model, the design and results of data collection, and the basis for the software solution will be described. Background Workload measurement and patient classification systems (PCS) were developed to measure the amount and type of nursing care per patient to predict staffing needs in the acute care environment. Connor in 196119 conducted the seminal work on the concept of nursing intensity and developed a PCS that matched nursing resources with patient demands. Methods that link PCS data and staffing are mixed20. No common definition of nursing intensity has been accepted by the profession15 nor has a universal model to measure nursing intensity become apparent 21. Weaknesses, gaps, and concerns of omissions were identified during development and testing of PCS
tools including: dissimilar and highly variable scoring of different tools on the same patient21, the measurement of patient severity19, and use of clinical decision making12. The lack of confidence in these systems waned because they are subjective and do not include the complexity of care or the nurses’ skill level 20. Additionally, these systems are costly and time consuming to implement, and are stand-alone systems that are not integrated into the EHR, payroll, staff scheduling, or cost accounting systems. The potential to use healthcare information technology (HIT) as a tool to assess nurse staffing decisions is a rather new phenomena explored by some of the thought leaders in nursing informatics18,4. Healthcare software companies have the potential to offer hospitals objective tools that use practice based HIT and mathematical formulas to analyze complex practice based data drawn from sources such as historical patterns of patient activities; the popularity of surgeries on certain days; recovery times from various conditions; and daily admission and discharge records to generate new knowledge. Discovery of the right combination of “hard data” and “soft data” is the impetus for this work to develop an innovative software program which will more precisely determine the workload for nursing care hours, based on an algorithm developed from data on patient care needs as retrieved from the EHR and validated by observational correlation. The basis of the algorithm relies not on the overall diagnosis, but the hour-by-hour plethora of information about patients (i.e. risk factors, interventions, orders, medications, and activity), nurses, hospital policies and other factors that can be used to make more accurate staffing predictions. The unit specific nature of the data is an important feature of the instrument. It allows the focus of decisions to be on what is occurring on the specific unit involving its unique patient population and nurses. Methods The study used a quantitative dominant transformative mixed methods research method and correlational design, which involved collecting both the quantitative and qualitative data simultaneously and then merging the data by transforming the qualitative data into quantitative data for correlation between the variables. The data collection was organized into three primary groups: Direct Care, Indirect Care and Unit Related. Direct Care is when the nurse interacts with the patient directly (e.g. dressing change, talking to patient/family, medication administration). Indirect Care is when the nurse performs patient care activities, but not necessarily with the patient (e.g. documenting, preparing medications, and talking to nurse/doctor about patient). Unit Related Care included activities pertaining to the hospital unit or staff (e.g. shift report, meals, schedules, cleaning, etc.) The study utilized four sets of data. 1. Direct observation of the registered nurses (RN). 2. Direct observation of the patients. 3. Data extracted from the EHR for the observed patients. 4. Data extracted from the EHR of the unit’s admissions, transfers and discharges over the time period. Figure 1 represents the new model created to accurately predict staffing using the Care Demand Index TM. Figure 1
Two data collection sheets were developed for the observation aspect of the study. The RN observation and patient observation collection sheets were designed to efficiently capture and organize activity data relevant to the nurse’s and patient care activities. The qualitative observation time study was conducted at a community hospital in southern California. Direct observations of nurses and patients were completed over the course of 72 hours on a medical/surgical adult only unit. The direct patient activities were captured by observing six patients each day over
the time period. The in-direct patient activities and unit activities were captured by observing a RN over the same 72 hours. RN observers were assigned two patients each for their 12 hour shift. Using a stop watch, each patient observer was responsible for recording the type of caregiver that entered the patients’ rooms, what activities were performed, time of day, and how long each activity took. Indirect and Unit care was captured by observing one RN each 12 hour shift. Again using a stop watch, the nurse observer was responsible for recording what type of activity performed, time of day, and how long each activity took. . The quantitative aspect of this mixed method study used the historical patterns of nurse and patient activities found within the EHR. The clinical data was extracted from the EHR for the observed patients. We used a query tool to extract all of the electronic documentation, clinical events, medications, interventions, outcomes and orders on the study group patients. Each data element included the date and time the activity was scheduled for, the time the activity occurred and who documented it. The number and times of the ADT events were pulled for the unit for the 72 hours in which the study took place. Results Over the course of the 72 hours of observation the Direct Care Total n = 834. Each Activity Group was evaluated individually and then by time of day (broken out by 4 hour increments). Figure 2 demonstrates the breakdown of the Assessment Activity Group by time of day. This data helped us to focus on the Activity Groups that constituted the most time and the heaviest time of day they took place. Figure 2 Direct Care Observation Overall Cohort Percentiles 50th
Total Time Spent doing activity (mins)
N
Mean
SD
0:00 to 4:00
28
1.29
0.71
1
1
1
36
4:00 to 8:00
22
2.41
1.65
1
1.5
4
53
8:00 to 12:00
59
2.34
2.68
1
1
3
138
12:00 to 16:00
33
2.42
2.08
1
1
3
80
16:00 to 20:00
31
3.90
4.41
1
2
5
121
20:00 to 24:00
22
1.86
1.83
1
1
2
41
25th
75th
P-Value
Assessments (shift, ongoing, pain, episodic)
0.002841
The observation of the Indirect and Unit Care Total n = 635. Similar to the Direct Care, the Indirect Activities were also broken out by 4 hour time slots. Figure 2 demonstrates the breakdown of the Charting, Documentation Activity Group. We then could see the relationship between charting activities regardless of the time of day they took place showed that charting was often recurring regardless of the time of day, yet had variation in the mean time to chart between the day and night hours. Figure 3 Overall Cohort N
Mean
SD
13.01
25th
Percentiles 50th
75th
Total Time Spent doing activity (mins)
10
12
30
175
P-Value
Charting, Documentation 0:00 to 4:00
9
19.44
4:00 to 8:00
6
14.33
9.99
8
11
25
86
8:00 to 12:00
25
5.88
7.64
2
2
8
147
12:00 to 16:00
21
6
8
2
3
9
134
16:00 to 20:00
15
5.53
4.85
2
4
10
83
20:00 to 24:00
10
16.70
25.65
3
5
9
167
0.001719
The quantitative data was similarly reviewed. Multiple iterations of data mapping and review were required to accurately map the EHR data into the respective Activity Groups used in the observation study. Additionally, the EHR has a plethora of data and there were challenges to accurately identify the start and stop times for each activity (i.e. medication administration) in order to assign to an activity group, the time of day, and the role of the caregiver who performed the activity. Figure 4 gives a summary of the EHR clinical data.
Figure 4
Total Average/Patient Std Dev Min Max
Clinical Events 23,794 2,379 956.2 1,166 4,263
Orders 28,712 2,871 1,559.2 1,016 6,219
Order Actions 28,712 2,871 1,559.2 1,016 6,219
Tasks 470 47 17.1 19 80
Plan Entries 524 52 41.6 0 130
Documentation Items 14,375 1,438 522.8 742 2,157
When all the datasets were mapped to the Activity Groups we were able to transform the qualitative data into quantitative data for correlation between the variables. Using Assessment Activity group (Figure 5) we were able to turn data (orders) back into time, thus able to determine the amount of patient care data needed to accurately predict the work needed for each patient within a 4 hour time period. Figure 5 Overall Cohort Assessments
Total Number of Active Total Number of Clinical Total Number of Orders Events Logged Documentation Events
Total Number of Care Minutes Provided (per Observation Data)
Total Number of Active Orders Per 1 Minute of effort B / E)
7:00 thru 10:00
584
169
48
142
11:00 thru 14:00
678
74
154
97
6.99
15:00 thru 18:00
746
63
21
65
11.48
19:00 thru 22:00
744
191
20
92
8.09
23:00 thru 2:00
578
35
151
33
17.52
3:00 thru 6:00
577
37
42
31
18.61
4.11
The results of the study were able to tell us the following correlations and helped focus the analysis 1.
The volume of activity – the study helped to point out the activities that make up the greatest share of direct care activities. We are not confident that we can collapse activity groups to a smaller number
2.
The amount of time it takes to perform activities.
3.
The relationship between direct and indirect care
4.
The impact of time of admission. There were 10 patients admitted to the unit during the 72 hours of observation.
Discussion To our knowledge this is the first attempt at using informatics, the computer and digital patient data to conduct research to objectively estimate real time RN staffing needs. Accurate predictive models depend on identifying which variables affect future resource demand the most. Although the pilot study was small, it has allowed us to use data mining techniques to determine which variables (orders, procedures, clinical documentation, etc.) have the greatest impact on the amount of direct care a patient is getting. Identifying these variables is not in general possible for human being to do; in fact “hunches” are usually wrong and reflect inside-the–box thinking and biases. Our hypothesis is that the amount of indirect care required is proportional to the amount of direct care the patient is receiving plus the amount of ADT activity that is taking place on the unit. The coefficient of proportionality would be dependent on the processes of a specific hospital unit and potentially the patient’s variables (age, BMI, etc.). Using a three-phase approach, the observation and retrospective data collection (Phase 1) described in this paper is the foundation for which gathering the qualitative and quantitative data. Predicating patient admissions (Phase 2) will require a retrospective analysis of past patient arrivals to determine how many patients are expected to arrive by day of week, hour of day, and accounting for seasonal variations. Along with real time monitoring of admission orders as well as what is happening in other units or areas of the hospital where new patients may be transferred in. The observation unit had seven admissions from the Operating Room (OR) and three from the
emergency department. The second step to calculate patient census is to determine when a patient will depart form the unit. Similar to predicting future admissions, determining length of stay and departures can be done by historic review of the time patient’s spent on the unit by patient variables and real time monitoring of key discharge or transfer information. Our goal is to accurately predict 24 hours. Phase 3 will test the accuracy and validate the algorithms created in Phase 2 to predict the direct care. Rather than direct observation, the study will utilize Real Time Location System (RTLS). RTLS provide hospital administrators with actionable information regarding the location, status and movement of equipment and people. Traditionally, RTLS has been used to track equipment, tracking patients and tracking providers. This study will use patient and nurses tracked to determine the time spent engaged with direct care to predict the indirect care the patient requires. References 1. 2. 3. 4. 5. 6. 7.
8. 9.
10. 11.
12. 13.
14. 15. 16. 17. 18. 19. 20.
Buerhaus, P. I. (2010, March-April). It’s time to stop the regulation of hospital nurse staffing dead in its tracks. Nursing Economics, 28(2), 110-113. Retrieved from http://www.nursingeconomics.net Aiken, L., Clark, S., Sloane, D., Sochalski, J., & Silber, J. (2002). Hospital nurse staffing and patient mortality, nurse burnout. AMA, 288(16), 1987-1993. Eck Birmingham, S. (2010, June). Evidence-based staffing: the next step. Nurse Leader, 24-35. doi: 10.1016/j.mnl/2010.03.003 Douglas, K. (2010, January-February). The human side of staffing. Nursing Economics, 28(1), 56-62. doi: Retrieved from http://web.ebscohost.com/ehost American Nurses Association. (2010). Registered Nurse Safe Staffing Act of 2010 (S. 3491/ H.R. 5527). Retrieved from http://www.safestaffingsaveslives.org/ Anderson, R., & Kerfoot, K. (2009). The time has come for evidence-based staffing and scheduling. Nursing Economics, 27(5), 277-279. Clark, S. P., & Donaldson, N. E. (2008). Nurse staffing and patient care quality and safety. . In Agency for Healthcare Research and Quality (Ed.), Patient Safety and Quality: An Evidence-Based Handbook for Nurses (pp. 1-25). Retrieved from http://www.ahrq.gov/qual/nurseshdbk/nurseshdbk.pdf. Hurst, K. (2005, June). Relationships between patient dependency, nursing workload and quality. International Journal of Nursing Studies, 42(), 75-84. Retrieved from http://www.sciencedirect.com/ Hughes, R. G. (2008). Nurses at the sharp end of patient care. In Agency for Healthcare Research and Quality (Ed.), Patient Safety and Quality: An Evidenced-Based Handbook for Nurses (pp. 1-30). Retrieved from http://www.ahrq.gov/qual/nurseshdbk/nurseshdbk. Institute of Medicine. (2010). Future of nursing: leading change, advancing health. In . Washington, DC: The National Academies Press. Schroeder, R. E., Rhodes, A. M., & Shields, R. E. (1984). Nurse acuity systems: CASH vs. GRASP: (a determination of nurse staff requirements. Nursing Forum, 21(), 72-77. doi: 10.1111/j.17446198.1984.tb01136.xRetrieved from http://onlinelibrary.wiley.com Seago, J. A. (2001). Nurse staffing, models of care delivery, and interventions. In (Ed.), Agency for Health Care Research and Quality (Publication No. 01-E058,. Retrieved from http://www.ncbi.nlm.nih.gov/books/ Bakken, S., Lucero, R., Yoon, S., & Hardiker, N. (2011). Implications for nursing research and generation of evidence. In A. Cashin & R. Cook (Eds.), Evidenced-based practice in nursing informatics (pp. 113-127). : Hershey, PA. Zone-Smith, L.K. (2004). Predicting real-time nurse staffing needs in hospitals: A new tool to measure nursing intensity. Journal of Nursing Administration, 34,210. Seago, J. A. (2002). The California experiment: Alternatives for minimum nurse-to-patient ratios. Journal of Nursing Administration, 32(1), 48-58. doi: 10.1097/00005110-200201000-00012 Hyun, S., Bakken, S., Douglas, K., & Stone, P. W. (2008, May-June). Evidence-based staffing: potential roles for informatics. Nursing Informatics, 26(3), 159-158. Retrieved from http://www.mylawagnerpr.com. Connor, R.J. (1960) A hospital inpatient classification system. Dissertation Abstracts International, 21(565). Harper, K., & McCully, C. (2007). Acuity systems dialogue and patient classification system essentials. Nursing Administration Quarterly, 31(4), 284-299 Welton, J. M., Unruh, L., & Halloran, E. J. (2006). Nurse staffing, nursing intensity, staff mix, and direct nursing care costs across Massachusetts hospitals. Journal of Nursing Administration, 36, 416-425 O’Brien-Pallas, L., Thomson, D., McGillis Hall, L., Pink, G., Kerr, M., Wang, S., Meyer, R. (2004). Evidencebased standards for measuring nurse staffing and performance. Retrieved from Canadian Health Services Research Foundation Web site (www.chrsf.ca)
