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Figure. The ordering patterns from January through March of 2010 (pre-iPad) and 2011 (post-iPad). Patterns were analyzed and plotted by the number of orders placed per general medicine admission (y-axis) in hourly increments (x-axis) for the first 24 hours of admission. The total number of orders per patient in the 3-month period was not statistically different (26.6 orders in 2010 vs 25.8 in 2011). After iPad implementation in 2011 by attending rounds at 7 AM, 38% of the orders were placed (6618 orders before 7 AM out of 17 414 total orders in 24 hours) vs 33% in 2010 (5598 orders before 7 AM out of 16 770 total orders in 24 hours). At the time of postcall team departure at 1 PM, with iPads in 2011, 64% of orders (11 085 orders before 1 PM out of 17 414 total orders in 24 hours) were placed vs 56% of orders in 2010 (9416 orders before 1 PM out of 16 770 total orders in 24 hours). Both differences are statistically significant (P < .001).

Figure. The ordering patterns from January through March of 2010 (pre-iPad) and 2011 (post-iPad). Patterns were analyzed and plotted by the number of orders placed per general medicine admission (y-axis) in hourly increments (x-axis) for the first 24 hours of admission. The total number of orders per patient in the 3-month period was not statistically different (26.6 orders in 2010 vs 25.8 in 2011). After iPad implementation in 2011 by attending rounds at 7 AM, 38% of the orders were placed (6618 orders before 7 AM out of 17 414 total orders in 24 hours) vs 33% in 2010 (5598 orders before 7 AM out of 16 770 total orders in 24 hours). At the time of postcall team departure at 1 PM, with iPads in 2011, 64% of orders (11 085 orders before 1 PM out of 17 414 total orders in 24 hours) were placed vs 56% of orders in 2010 (9416 orders before 1 PM out of 16 770 total orders in 24 hours). Both differences are statistically significant (P < .001).

1.
 Beyond duty hour reform: redefining the learning environment. http://www.im.org/AcademicAffairs/PSI/Documents/09-06-04%20Beyond%20Duty%20Hour%20Reform%20FINAL%20Report.pdf. Accessed May 16, 2011
2.
Arora VM, Georgitis E, Siddique J,  et al.  Association of workload of on-call medical interns with on-call sleep duration, shift duration, and participation in educational activities.  JAMA. 2008;300(10):1146-1153PubMedArticle
3.
Osheroff JA, Forsythe DE, Buchanan BG, Bankowitz RA, Blumenfeld BH, Miller RA. Physicians' information needs: analysis of questions posed during clinical teaching.  Ann Intern Med. 1991;114(7):576-581PubMed
4.
Zeng Q, Cimino JJ, Zou KH. Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation.  J Am Med Inform Assoc. 2002;9(3):294-305PubMedArticle
5.
Verghese A. Culture shock: patient as icon, icon as patient.  N Engl J Med. 2008;359(26):2748-2751PubMedArticle
Research Letters
Mar 12, 2012

Impact of Mobile Tablet Computers on Internal Medicine Resident Efficiency

Author Affiliations

Author Affiliations: Departments of Pulmonary/Critical Care (Dr Patel) and Medicine (Drs Chapman, Luo, Woodruff, and Arora), University of Chicago, Chicago, Illinois.

Arch Intern Med. 2012;172(5):436-438. doi:10.1001/archinternmed.2012.45

Internal medicine residents' increased workload compounded by limited work hours creates work compression and competition between service responsibilities and educational goals.1 Moreover, residents report spending the bulk of their time in indirect patient care, such as updating medical charts, documentation, and ordering tests, at the expense of direct patient care or education.2 Unfortunately, the implementation of electronic health records actually increases time in indirect care and the need for available computer workstations to advance care. These trends, coupled with the growing information needs for patient care,3,4 have led to more time spent locating a computer or working on the computer at the expense of time at the bedside5 or at conference.

We sought to implement and evaluate the deployment of personal mobile computers on resident workflow efficiency and patient care.

Methods

A total of 115 internal medicine residents were given Apple iPads (Cupertino, California) and instructions on how to access to the medical record (Epic; Verona, Wisconsin) via Citrix client, publications, and paging systems via shortcuts to the Web address on the home screen in October 2010. Residents were surveyed in the month prior to and 4 months after deployment of personal mobile computing devices to assess the potential impact on their workflow and efficiency.

In addition, data from the electronic health record were examined to ascertain the time frame of all patient care orders placed in the first 24 hours of a new patient's admission to the general medicine service from January to March of 2011. These data were compared with the time frame for orders during patient admission in the same 3-month period in 2010 (prior to distribution of iPads) to assess any change in ordering efficiency with personal mobile computing. This time period was used since it was after residents had 3 months to learn how to use the iPad and fully integrate it into their workflow. The rate of patient orders per admission by admission hour was compared for both groups using 2 sample tests of proportion with statistical significance defined as P = .05.

This study was deemed to be institutional review board exempt.

Results

Nearly all residents (114 of 115 [99%]) completed the postimplementation survey. The postsurvey demonstrated that almost 90% of residents (100) were using their iPad for clinical responsibilities at work, with almost 75% of these residents (72) using their iPad every day. More than three-quarters of residents (78%) noted that they were more efficient on the wards, with a self-reported time savings of about an hour a day. Furthermore, more than half (56%) felt that they could attend more conferences by using their iPads. Sixty-eight percent of all housestaff reported that patient care delays were averted with the iPad. Interestingly, interns were more likely than residents to report that the iPad improved their efficiency on the wards (89% of interns vs 71% of residents; P = .03).

From January to March of 2010 and 2011, there were 631 and 675 general medicine admissions, which generated 16 770 and 17 414 total orders placed in the first 24 hours of admission, respectively. There was no difference (P = .58) in the number of orders per admission in 2010 before iPads (27 orders per admission) and 2011 after iPads (26 orders per admission).

Interestingly, timing of orders with respect to time of patient admission did change after iPad deployment (Figure). Specifically, 5% more orders were placed prior to postcall attending rounds, which are scheduled at 7 AM. Likewise, there were 8% more orders placed prior to the time at which postcall teams are scheduled to leave the hospital. This results in a statistically significant increase in the proportion of orders placed prior to postcall attending rounds (33% precall vs 38% postcall; P < .001) and before departure of postcall team (56% precall vs 64% postcall; P < .001). In addition, there were also more orders placed in the first 2 hours of admission (odds ratio, 1.06; 95% CI, 1.00-1.12; P = .04) in 2011 (with iPads) vs 2010 (without iPads).

Comment

The implementation of personal mobile computing via iPads was associated with improvements in both perceived and actual resident efficiency. Resident perception of improvement in workflow efficiency seems to be consistent with data demonstrating that orders were placed earlier in a patient's admission. Furthermore, more orders were entered before the postcall team had to leave the hospital. In addition to enhancing efficiency of residents, the iPads may have facilitated greater continuity of patient care since the primary service was able to advance care for the patients they admitted and will follow before they execute a handoff.

As a single-institution study, the results may not be generalizable. Furthermore, the origin of orders, whether placed on iPads vs desktop computers, was not discernable in this analysis. However, the number of computer workstations was unchanged in the study period. Despite these limitations, it seems that personal mobile computing can help improve perceived and actual resident efficiency in an era of increasing work compression.

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Article Information

Correspondence: Dr Arora, Department of Medicine, University of Chicago, 5841 S Maryland Ave, MC 2007, AMB W216, Chicago, IL 60637 (varora@medicine.bsd.uchicago.edu).

Author Contributions: Drs Patel and Arora had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Patel, Luo, Woodruff, and Arora. Acquisition of data: Patel, Chapman, Luo, Woodruff, and Arora. Analysis and interpretation of data: Patel, Woodruff, and Arora. Drafting of the manuscript: Patel, Woodruff, and Arora. Critical revision of the manuscript for important intellectual content: Patel, Chapman, Luo, Woodruff, and Arora. Statistical analysis: Patel and Arora. Obtained funding: Chapman, Woodruff, and Arora. Administrative, technical, and material support: Patel, Chapman, Luo, Woodruff, and Arora. Study supervision: Woodruff and Arora.

Financial Disclosure: None reported.

Additional Contributions: We thank the University of Chicago internal medicine residents for participation in the mobile computing program and the Office of the Chairman of Medicine for their generous funding. Sarah Bennington. BS, and Mary Ghilardi, RN, assisted in Epic orders analysis. Maria Jacobson, BS, Cindy Kitching, BS, and Don Saner. MS, assisted in research and implementation.

References
1.
 Beyond duty hour reform: redefining the learning environment. http://www.im.org/AcademicAffairs/PSI/Documents/09-06-04%20Beyond%20Duty%20Hour%20Reform%20FINAL%20Report.pdf. Accessed May 16, 2011
2.
Arora VM, Georgitis E, Siddique J,  et al.  Association of workload of on-call medical interns with on-call sleep duration, shift duration, and participation in educational activities.  JAMA. 2008;300(10):1146-1153PubMedArticle
3.
Osheroff JA, Forsythe DE, Buchanan BG, Bankowitz RA, Blumenfeld BH, Miller RA. Physicians' information needs: analysis of questions posed during clinical teaching.  Ann Intern Med. 1991;114(7):576-581PubMed
4.
Zeng Q, Cimino JJ, Zou KH. Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation.  J Am Med Inform Assoc. 2002;9(3):294-305PubMedArticle
5.
Verghese A. Culture shock: patient as icon, icon as patient.  N Engl J Med. 2008;359(26):2748-2751PubMedArticle
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