JONA Volume 45, Number 5, pp 239-242 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Electronic Nursing Care Reminders I m p lic a t io n s f o r N u rs in g Leaders R o n a ld J . P i s c o t t y , P h D , R N -B C B e a t r i c e K a lis c h , P h D , R N , F A A N A n g e l G ra c e y -T h o m a s H o s s e in Y a r a n d i, P h D
OBJECTIVE: The aim o f this study is to report the results o f a replication study o f the relationship between selfreported nursing care rem inder (NCR) use and missed nursing care. DESIGN: A descriptive crosssectional correlational design was used. The sample (N = 124) was composed o f medical/surgical and IC C R N s working on acute care hospital units in a large M idwestern teaching hospital. METHODS: The M ISSC A R E Sur vey, Nursing Care Reminders Usage Survey, and the Im pact o f Health Care Information Technology Sur vey were used to collect data. Ad-
Author Affiliations: Assistant Professor (Dr Piscotty), Research Assistant (Ms GraceyThomas), Professor (Dr Yarandi), Wayne State University, College of Nursing, Detroit, Michigan; Titus Distinguished Professor (Dr Kalisch), School of Nursing, University of Michigan, Ann Arbor. An American Organization of Nurse Executives Foundation for Nursing Lead ership, Research, and Education Seed Grant funded this study. The authors declare no conflicts of interest. Correspondence: D r Piscotty, Wayne State University, College of Nursing, 5557 Cass Ave, 364 Cohn, Detroit, MI 48202
([email protected]). DOI: 10.1097/NNA.OOOOOOOOOOOOO192
JO N A • Vol. 45, N o. 5 • May 2015
justed hierarchical multiple regression was used to determine study outcomes. RESULTS: N urses w ho use N C R s m ore frequently have decreased re ports o f missed nursing care. Nurses w ho perceive the im pact o f health care technology as positive on their practice also have decreased missed nursing care. CONCLUSION: The results o f this study suggest that N C R s are an effective intervention to decrease missed nursing care in acute care hospitals.
and other resources, including the reputation of organizations and individuals. It is estimated that measurable healthcare errors cost the United States approximately $17.1 billion per year.4 Attempts have been made nationwide to de crease healthcare errors with the use of health information technol ogy (HIT), but most acute care or ganizations have not implemented electronic health records (EHRs) to their fullest potential." Background
In the age of Big Data, efficient in terpretation of data into meaning ful information is param ount to make informed clinical and finan cial decisions. In healthcare, it is of major importance that the data are interpreted appropriately to prevent errors and improve quality of care. Preventable healthcare er rors have been estimated to result in 440,000 deaths per year and would be the 3rd leading cause of death in the United States.1'2 These errors, including missed nursing care (standard, required nursing care that is not completed),3 are costly in terms of both monetary
Clinical Decision Support as an Answer Clinical decision support may be 1 tool in the leader’s arsenal to reduce errors and improve care. Clinical decision support systems (CDSSs) are a form of technology embedded within EHRs. Clinical decision support systems provide evidence-based recommendations, alerts, or reminders using patientspecific information to improve clin ical reasoning and decision making. Choi et al6 reported that the need for improved patient safety sup ported the use of CDSS by registered nurses (RNs). Yuan and colleagues7
239
^SB 3 Leadership Perspectives A 0 N E
reported that nurses rapidly learned to use a CDSS developed to provide support in clinical decision making. H ealth inform ation technology such as CDSS may be helpful in improving or augmenting clinical reasoning.8 Shojania and col leagues9 reported that CDSS in terventions in which clinicians are prompted were beneficial at the point of care. Reminders targeted tow ard nurses are a type of CDSS that have demonstrated promise in reducing the amount of missed nursing care.8 We conducted a study that exam ined the relationship between nurs ing care reminders (NCRs), the impact of HIT on nursing practice, and missed nursing care to deter mine if there was a relationship a among them .8 The results showed that nurses using NCRs in the EHR more frequently had decreased re ports of missed nursing care.8 P u rp o s e
I The purpose of this study is to I conduct a replication of our previous study8 examining the relation ship between self-reported NCR usage, missed nursing care, and nurses’ perceptions about HIT on their practice. Two hypotheses were tested in this study: (1) Nurses who use NCRs more frequently will have decreased reports of missed nursing care, and (2) nurses who have more positive reports of the impact of HIT on their practice will have decreased reports of missed nursing care.
I
M e th o d s
Design and Sample The methods used in this replica tion study are similar to those used in the original study.8 The study de sign was a descriptive cross-sectional correlational design. A convenience sample of medical/surgical and in
240
tensive care unit (ICU) RNs work ing on acute care hospital units (n = 12) in the fall of 2014 in 1 large Midwestern teaching hospital was used. All RNs providing direct pa tient care were sampled (N = 276). Instrum entation The NCR Usage Survey was used to measure frequency of reminder use, whereas the Impact of Health care Information Technology Sur vey (I-HIT) scale10 was used to de termine nurses’ perceptions of the impact of HIT on their practice. Part A of the Missed Nursing Care Survey11 was used to measure ele ments of missed nursing care. Spe cific details of each survey, such as development, sample items, valid ity, and reliability, can be found in the original studies.8’10’11 The re liabilities of the NCR Usage Survey and I-HIT Scales were determined using Cronbach’s a and were .70 and .96, respectively, in this study. This is consistent with previous studies.8’10 Procedure Before the study was conducted, institutional review board and hos pital approvals were obtained. Sur vey packages were delivered to each unit and placed in a lounge or con ference room. Each individual pack age for each respondent contained all 3 surveys, an information sheet, and directions as to how they would receive a monetary incentive ($20). Flyers were placed in high-visibility areas to remind nurses to complete the survey. Completed packages were due in 10 weeks and were to be de posited into a secure box placed on each unit. A research assistant col lected the completed surveys and entered the data into SPSS 22 (IBM Corp, Armonk, New York).
R e s u lt s
Sample The sample consisted of 124 nurses (response rate, 45% ). M ost of the RNs reported holding a baccalau reate in nursing (BSN) degree (66.9%, n = 83), were female (76.6%, n = 95), were between the ages of 25 and 34 years (48.4%, n = 60), and worked on a medical and/or surgical unit (61.3% , n = 75). A large propor tion of the sample worked full-time (95.2%, n = 118) (Table 1). Hypotheses 1 (Model 1) The variables of perceived staffing adequacy, shift worked, unit acu ity (unit case mix index [CMI]), and experience in current role were included in step 1 of the regression model to control for their effect on missed nursing care. After entering NCR use into the regression model during step 2, a negative relation ship with missed nursing care was found, indicating that nurses who use NCRs more frequently have decreased reports of missed nurs ing care. Perceived staffing ade quacy (P = —.18, P = .036), shift worked (p = .30, P < .001), CMI (P = —.17, P = .05), experience in current role (p = .19, P = .018), and NCR use (p = -.2 2 , P = .008) were all statistically significant predictors of missed nursing care (Table 2). With other variables held constant, missed nursing care was negatively related to perceived staff ing adequacy, CMI, and NCR use and positively related to shift worked and experience in current role. Hypotheses 2 (Model 2) The control variables of perceived staffing adequacy, shift worked, unit acuity (unit CMI), and experience in current role were included in step 1
JONA • Vol. 45, No. 5 • May 2015
r Table l.
A 0 N E
Leadership Perspectives
Demographic Characteristics o f the Sample (N = 124) 0/ /o
Characteristics
n
Age 65 y Missing
18 60 21 22 2 0 1
14.5 48.4 16.9 17.7 1.6 0 0.8
Gender Male Female Missing
28 95 1
22.6 76.6 0.8
Experience in role Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y
16 36 36 13 23
12.9 29.0 29.0 10.5 18.5
Experience as RN Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y Missing
14 31 42 12 24 1
11.3 25.0 33.9 9.7 19.4 0.8
Experience with current EHR Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y
5 37 54 26 2
4.0 29.8 43.5 21.0 1.6
Highest education level Associate’s degree Bachelor’s degree Graduate degree Missing
28 88 6 2
22.6 71.0 4.8 1.6
Degree type RN diploma Associate degree in nursing (ADN) Bachelor’s of science in nursing (BSN) Bachelor’s degree outside of nursing Master’s degree in nursing (MSN) or higher Missing
8 27 83 2 3 1
6.5 21.8 66.9 1.6 2.4 0.8
Unit type ICU Medical/surgical
48 76
38.7 61.3
Shift worked Days Evenings Nights Rotates between shifts
61 5 52 6
49.2 4.0 41.9 4.8
118 5 1
95.2 4.0 0.8
Employment status Full-time Part-time Missing
JONA • Vol. 45, No. 5 • May 2015
of the regression model to control for their effect on missed nursing care. After entering I-HIT into the regression model during step 2, a negative relationship with missed nursing care was found, indicating that nurses who have more positive perceptions of the impact of HIT on their practice have decreased reports of missed nursing care. Shift worked (P = .26, P = .002), experience in current role (P = .22, P = .011), and I-HIT (p = —.18, P = .036) were all statisti cally significant predictors of missed nursing care (Table 2). With other variables held constant, missed nurs ing care was negatively related to I-HIT and positively related to shift worked and experience in current role. Discussion
The results from this study support both of the hypotheses tested: In creased use of reminders is associ ated with decreased reports of missed nursing care, and RNs who have more positive perceptions about the impact of HIT on their practice have decreased reports of missed i nursing care. The findings of this study suggest that reminders have the potential to decrease missed nurs ing care. In addition, nurses who feel that HIT is beneficial in their practice are more likely to use the technology to reduce missed nursing care. This study supports the findings of our previous study.x This lends support to the future use of these types of CDSS in acute care settings to de crease missed nursing care and im prove patient care. An alternative explanation for the results may be that nurses who preferred using NCRs may have been more willing to complete their care activities to begin with. This is difficult to measure unless longitu dinal measures of NCRs and missed
241
^7f
A 0 N E
* v ) Leadership Perspectives and ultimately, patients. The trend in the United States is to use H IT to its full potential. The use of pertinent NCRs is 1 tool to decrease errors and im prove patient care.
Table 2. Summary o f Hierarchical Multiple Regression for Variables Predicting Missed Nursing Care (n = 123a) Model 1 Variable Staffing adequacy Shift worked CMI Experience current role NCR I-HIT R2 F for change in R2
B
SE B
-8 .1 0 7.22 -2 .1 0 1.83 -0 .3 7
3.83 1.95 1.06 0.76 0.14
Model 2
P
B
SE B
P
- ,1 8 b .30c - ,1 7 b ,19b -2 2 c
-7 .4 5 6.37 -1 .8 6 2.03
3.96 2.03 1.10 0.78
- . 1 6C .2 6C -.1 5 ,22b
-0 .0 9
0.04 0.20 4.52b
- ,1 8 b
R
0.27 7.25c
aOne case was identified as an outlier for 2 of the main variables. The case was removed before regression analysis was conducted. bP < .05. CP < . 01.
nursing care are conducted for each participant. An additional explana tion may be that most of the sample is younger than the average age of R N s in the United States. The new generation of nurses may view tech nology as m ore beneficial and may be m ore likely to em brace it. Leadership Im plications There are 2 m ajor implications from the findings of this study for nurse leaders (NLs). First, NLs should en courage the use and development of effective N C R s to decrease missed nursing care. N urse leaders are in a position to influence the use of rem inders to decrease errors. They are also well positioned in the or ganization to advocate for H IT and for the developm ent of rem inders th at nurses will find helpful and n o t burdensom e. Second, H IT system designers, N Ls, and inform atics experts m ust take into account nurses’ percep tions of HIT. N urse leaders w o rk ing together with nursing informatics experts can collaboratively design effective nursing reminders. Leaders engaging clinical nurses in critical discussions ab o u t the types of re minders they find redundant or bur
242
densome versus helpful would be beneficial in designing an effective EH R th at nurses will use to its full potential. L im itations A limitation is the small sample size; however, the study was adequately powered for the analyses. The sam ple size was determined a priori using power analysis. Use of a convenience sam ple is also a lim itation as self selection m ay occur. This w as ad dressed by sampling m ultiple R N s on differing units. Finally, this study examined only 1 hospital and 1 EHR, lim iting generalizability. However, the results from the study were sig nificant and similar to a previous study.8 This dem onstrates the p o tential for these findings to be gen eralized beyond this sample. C o n c lu s io n s
Exam ining the proper types of re m inders and m ethods of delivery to RNs is needed. Longitudinal stud ies w ould also be beneficial to de term ine if these relationships hold over time. This study supports the use of N CRs to decrease missed nurs ing care. The findings are relevant to healthcare organizations, NLs, RNs,
eferences
1. James JT. A new, evidence-based esti mate of patient harms associated with hospital care. ] Patient Saf. 2013;9(3): 122-128. 2. Centers for Disease Control Prevention. Leading causes of death. http://www .cdc.gov/nchs/fastats/leading-causes-ofdeath.htm. Accessed January 5, 2013. 3. Kalisch BJ, Landstrom G, Williams RA. Missed nursing care: errors of omission. Nurs Outlook. 2009;57(l):3-9. 4. Van Den Bos J, Rustagi K, Gray T, Halford M, Ziemkiewicz E, Shreve J. The $17.1 billion problem: the annual cost of measurable medical errors. Health A ff (Millwood). 2011;30(4):596-603. 5. H1MSS Analytics. Current EMRAMsm Scores, http://www.himssanalytics.org/ emram/scoreTrends.aspx. Accessed January 13, 2015. 6. Choi M, Choi R, Bae YR, Lee SM. Clinical decision support systems for pa tient safety: a focus group needs assess ment with Korean ICU nurses. CIN Comput Inform Nurs. 2011^9(11)^71-678. 7. Yuan MJ, Finley GM, Long J, Mills C, Johnson RK. Evaluation of user inter face and workflow design of a bedside nursing clinical decision support system. Interact J Med Res. 2013;2(l):e4. 8. Piscotty RJ, Kalisch BJ. The relation ship between electronic nursing care re minders and missed nursing care. CIN Comput Inform Nurs. 2014;32(10): 475-481. 9. Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database SystRev. 2009;(3):CD001096. doi: 10 .1002/14651858.CD001096.pub2. 10. Dykes PC, Hurley A, Cashen M, Bakken S, Duffy ME. Development and psychometric evaluation of the Impact of Health Technology (I-H1T) scale. J Am Med Inform Assoc. 2007;14(4):507-514. 11. Kalisch BJ, Williams RA. Development and psychometric testing of a tool to measure missed nursing care. ] Nurs Adm. 2009;39(5):211-219.
JONA • Vol. 45, No. 5 • May 2015
Copyright of Journal of Nursing Administration is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Electronic Nursing Care Reminders I m p lic a t io n s f o r N u rs in g Leaders R o n a ld J . P i s c o t t y , P h D , R N -B C B e a t r i c e K a lis c h , P h D , R N , F A A N A n g e l G ra c e y -T h o m a s H o s s e in Y a r a n d i, P h D
OBJECTIVE: The aim o f this study is to report the results o f a replication study o f the relationship between selfreported nursing care rem inder (NCR) use and missed nursing care. DESIGN: A descriptive crosssectional correlational design was used. The sample (N = 124) was composed o f medical/surgical and IC C R N s working on acute care hospital units in a large M idwestern teaching hospital. METHODS: The M ISSC A R E Sur vey, Nursing Care Reminders Usage Survey, and the Im pact o f Health Care Information Technology Sur vey were used to collect data. Ad-
Author Affiliations: Assistant Professor (Dr Piscotty), Research Assistant (Ms GraceyThomas), Professor (Dr Yarandi), Wayne State University, College of Nursing, Detroit, Michigan; Titus Distinguished Professor (Dr Kalisch), School of Nursing, University of Michigan, Ann Arbor. An American Organization of Nurse Executives Foundation for Nursing Lead ership, Research, and Education Seed Grant funded this study. The authors declare no conflicts of interest. Correspondence: D r Piscotty, Wayne State University, College of Nursing, 5557 Cass Ave, 364 Cohn, Detroit, MI 48202
([email protected]). DOI: 10.1097/NNA.OOOOOOOOOOOOO192
JO N A • Vol. 45, N o. 5 • May 2015
justed hierarchical multiple regression was used to determine study outcomes. RESULTS: N urses w ho use N C R s m ore frequently have decreased re ports o f missed nursing care. Nurses w ho perceive the im pact o f health care technology as positive on their practice also have decreased missed nursing care. CONCLUSION: The results o f this study suggest that N C R s are an effective intervention to decrease missed nursing care in acute care hospitals.
and other resources, including the reputation of organizations and individuals. It is estimated that measurable healthcare errors cost the United States approximately $17.1 billion per year.4 Attempts have been made nationwide to de crease healthcare errors with the use of health information technol ogy (HIT), but most acute care or ganizations have not implemented electronic health records (EHRs) to their fullest potential." Background
In the age of Big Data, efficient in terpretation of data into meaning ful information is param ount to make informed clinical and finan cial decisions. In healthcare, it is of major importance that the data are interpreted appropriately to prevent errors and improve quality of care. Preventable healthcare er rors have been estimated to result in 440,000 deaths per year and would be the 3rd leading cause of death in the United States.1'2 These errors, including missed nursing care (standard, required nursing care that is not completed),3 are costly in terms of both monetary
Clinical Decision Support as an Answer Clinical decision support may be 1 tool in the leader’s arsenal to reduce errors and improve care. Clinical decision support systems (CDSSs) are a form of technology embedded within EHRs. Clinical decision support systems provide evidence-based recommendations, alerts, or reminders using patientspecific information to improve clin ical reasoning and decision making. Choi et al6 reported that the need for improved patient safety sup ported the use of CDSS by registered nurses (RNs). Yuan and colleagues7
239
^SB 3 Leadership Perspectives A 0 N E
reported that nurses rapidly learned to use a CDSS developed to provide support in clinical decision making. H ealth inform ation technology such as CDSS may be helpful in improving or augmenting clinical reasoning.8 Shojania and col leagues9 reported that CDSS in terventions in which clinicians are prompted were beneficial at the point of care. Reminders targeted tow ard nurses are a type of CDSS that have demonstrated promise in reducing the amount of missed nursing care.8 We conducted a study that exam ined the relationship between nurs ing care reminders (NCRs), the impact of HIT on nursing practice, and missed nursing care to deter mine if there was a relationship a among them .8 The results showed that nurses using NCRs in the EHR more frequently had decreased re ports of missed nursing care.8 P u rp o s e
I The purpose of this study is to I conduct a replication of our previous study8 examining the relation ship between self-reported NCR usage, missed nursing care, and nurses’ perceptions about HIT on their practice. Two hypotheses were tested in this study: (1) Nurses who use NCRs more frequently will have decreased reports of missed nursing care, and (2) nurses who have more positive reports of the impact of HIT on their practice will have decreased reports of missed nursing care.
I
M e th o d s
Design and Sample The methods used in this replica tion study are similar to those used in the original study.8 The study de sign was a descriptive cross-sectional correlational design. A convenience sample of medical/surgical and in
240
tensive care unit (ICU) RNs work ing on acute care hospital units (n = 12) in the fall of 2014 in 1 large Midwestern teaching hospital was used. All RNs providing direct pa tient care were sampled (N = 276). Instrum entation The NCR Usage Survey was used to measure frequency of reminder use, whereas the Impact of Health care Information Technology Sur vey (I-HIT) scale10 was used to de termine nurses’ perceptions of the impact of HIT on their practice. Part A of the Missed Nursing Care Survey11 was used to measure ele ments of missed nursing care. Spe cific details of each survey, such as development, sample items, valid ity, and reliability, can be found in the original studies.8’10’11 The re liabilities of the NCR Usage Survey and I-HIT Scales were determined using Cronbach’s a and were .70 and .96, respectively, in this study. This is consistent with previous studies.8’10 Procedure Before the study was conducted, institutional review board and hos pital approvals were obtained. Sur vey packages were delivered to each unit and placed in a lounge or con ference room. Each individual pack age for each respondent contained all 3 surveys, an information sheet, and directions as to how they would receive a monetary incentive ($20). Flyers were placed in high-visibility areas to remind nurses to complete the survey. Completed packages were due in 10 weeks and were to be de posited into a secure box placed on each unit. A research assistant col lected the completed surveys and entered the data into SPSS 22 (IBM Corp, Armonk, New York).
R e s u lt s
Sample The sample consisted of 124 nurses (response rate, 45% ). M ost of the RNs reported holding a baccalau reate in nursing (BSN) degree (66.9%, n = 83), were female (76.6%, n = 95), were between the ages of 25 and 34 years (48.4%, n = 60), and worked on a medical and/or surgical unit (61.3% , n = 75). A large propor tion of the sample worked full-time (95.2%, n = 118) (Table 1). Hypotheses 1 (Model 1) The variables of perceived staffing adequacy, shift worked, unit acu ity (unit case mix index [CMI]), and experience in current role were included in step 1 of the regression model to control for their effect on missed nursing care. After entering NCR use into the regression model during step 2, a negative relation ship with missed nursing care was found, indicating that nurses who use NCRs more frequently have decreased reports of missed nurs ing care. Perceived staffing ade quacy (P = —.18, P = .036), shift worked (p = .30, P < .001), CMI (P = —.17, P = .05), experience in current role (p = .19, P = .018), and NCR use (p = -.2 2 , P = .008) were all statistically significant predictors of missed nursing care (Table 2). With other variables held constant, missed nursing care was negatively related to perceived staff ing adequacy, CMI, and NCR use and positively related to shift worked and experience in current role. Hypotheses 2 (Model 2) The control variables of perceived staffing adequacy, shift worked, unit acuity (unit CMI), and experience in current role were included in step 1
JONA • Vol. 45, No. 5 • May 2015
r Table l.
A 0 N E
Leadership Perspectives
Demographic Characteristics o f the Sample (N = 124) 0/ /o
Characteristics
n
Age 65 y Missing
18 60 21 22 2 0 1
14.5 48.4 16.9 17.7 1.6 0 0.8
Gender Male Female Missing
28 95 1
22.6 76.6 0.8
Experience in role Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y
16 36 36 13 23
12.9 29.0 29.0 10.5 18.5
Experience as RN Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y Missing
14 31 42 12 24 1
11.3 25.0 33.9 9.7 19.4 0.8
Experience with current EHR Up to 6 mo >6 mo to 2 y >2 y to 5 y >5 y to 10 y >10 y
5 37 54 26 2
4.0 29.8 43.5 21.0 1.6
Highest education level Associate’s degree Bachelor’s degree Graduate degree Missing
28 88 6 2
22.6 71.0 4.8 1.6
Degree type RN diploma Associate degree in nursing (ADN) Bachelor’s of science in nursing (BSN) Bachelor’s degree outside of nursing Master’s degree in nursing (MSN) or higher Missing
8 27 83 2 3 1
6.5 21.8 66.9 1.6 2.4 0.8
Unit type ICU Medical/surgical
48 76
38.7 61.3
Shift worked Days Evenings Nights Rotates between shifts
61 5 52 6
49.2 4.0 41.9 4.8
118 5 1
95.2 4.0 0.8
Employment status Full-time Part-time Missing
JONA • Vol. 45, No. 5 • May 2015
of the regression model to control for their effect on missed nursing care. After entering I-HIT into the regression model during step 2, a negative relationship with missed nursing care was found, indicating that nurses who have more positive perceptions of the impact of HIT on their practice have decreased reports of missed nursing care. Shift worked (P = .26, P = .002), experience in current role (P = .22, P = .011), and I-HIT (p = —.18, P = .036) were all statisti cally significant predictors of missed nursing care (Table 2). With other variables held constant, missed nurs ing care was negatively related to I-HIT and positively related to shift worked and experience in current role. Discussion
The results from this study support both of the hypotheses tested: In creased use of reminders is associ ated with decreased reports of missed nursing care, and RNs who have more positive perceptions about the impact of HIT on their practice have decreased reports of missed i nursing care. The findings of this study suggest that reminders have the potential to decrease missed nurs ing care. In addition, nurses who feel that HIT is beneficial in their practice are more likely to use the technology to reduce missed nursing care. This study supports the findings of our previous study.x This lends support to the future use of these types of CDSS in acute care settings to de crease missed nursing care and im prove patient care. An alternative explanation for the results may be that nurses who preferred using NCRs may have been more willing to complete their care activities to begin with. This is difficult to measure unless longitu dinal measures of NCRs and missed
241
^7f
A 0 N E
* v ) Leadership Perspectives and ultimately, patients. The trend in the United States is to use H IT to its full potential. The use of pertinent NCRs is 1 tool to decrease errors and im prove patient care.
Table 2. Summary o f Hierarchical Multiple Regression for Variables Predicting Missed Nursing Care (n = 123a) Model 1 Variable Staffing adequacy Shift worked CMI Experience current role NCR I-HIT R2 F for change in R2
B
SE B
-8 .1 0 7.22 -2 .1 0 1.83 -0 .3 7
3.83 1.95 1.06 0.76 0.14
Model 2
P
B
SE B
P
- ,1 8 b .30c - ,1 7 b ,19b -2 2 c
-7 .4 5 6.37 -1 .8 6 2.03
3.96 2.03 1.10 0.78
- . 1 6C .2 6C -.1 5 ,22b
-0 .0 9
0.04 0.20 4.52b
- ,1 8 b
R
0.27 7.25c
aOne case was identified as an outlier for 2 of the main variables. The case was removed before regression analysis was conducted. bP < .05. CP < . 01.
nursing care are conducted for each participant. An additional explana tion may be that most of the sample is younger than the average age of R N s in the United States. The new generation of nurses may view tech nology as m ore beneficial and may be m ore likely to em brace it. Leadership Im plications There are 2 m ajor implications from the findings of this study for nurse leaders (NLs). First, NLs should en courage the use and development of effective N C R s to decrease missed nursing care. N urse leaders are in a position to influence the use of rem inders to decrease errors. They are also well positioned in the or ganization to advocate for H IT and for the developm ent of rem inders th at nurses will find helpful and n o t burdensom e. Second, H IT system designers, N Ls, and inform atics experts m ust take into account nurses’ percep tions of HIT. N urse leaders w o rk ing together with nursing informatics experts can collaboratively design effective nursing reminders. Leaders engaging clinical nurses in critical discussions ab o u t the types of re minders they find redundant or bur
242
densome versus helpful would be beneficial in designing an effective EH R th at nurses will use to its full potential. L im itations A limitation is the small sample size; however, the study was adequately powered for the analyses. The sam ple size was determined a priori using power analysis. Use of a convenience sam ple is also a lim itation as self selection m ay occur. This w as ad dressed by sampling m ultiple R N s on differing units. Finally, this study examined only 1 hospital and 1 EHR, lim iting generalizability. However, the results from the study were sig nificant and similar to a previous study.8 This dem onstrates the p o tential for these findings to be gen eralized beyond this sample. C o n c lu s io n s
Exam ining the proper types of re m inders and m ethods of delivery to RNs is needed. Longitudinal stud ies w ould also be beneficial to de term ine if these relationships hold over time. This study supports the use of N CRs to decrease missed nurs ing care. The findings are relevant to healthcare organizations, NLs, RNs,
eferences
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JONA • Vol. 45, No. 5 • May 2015
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