A detailed description of the implementation of inpatient insulin orders with a commercial electronic health record system

Abstract

In the setting of Meaningful Use laws and professional society guidelines, hospitals are rapidly implementing electronic glycemic management order sets. There are a number of best practices established in the literature for glycemic management protocols and programs. We believe that this is the first published account of the detailed steps to be taken to design, implement, and optimize glycemic management protocols in a commercial computerized provider order entry (CPOE) system. Prior to CPOE implementation, our hospital already had a mature glycemic management program. To transition to CPOE, we underwent the following 4 steps: (1) preparation and requirements gathering, (2) design and build, (3) implementation and dissemination, and (4) optimization. These steps required more than 2 years of coordinated work between physicians, nurses, pharmacists, and programmers. With the move to CPOE, our complex glycemic management order sets were successfully implemented without any significant interruptions in care. With feedback from users, we have continued to refine the order sets, and this remains an ongoing process. Successful implementation of glycemic management protocols in CPOE is dependent on broad stakeholder input and buy-in. When using a commercial CPOE system, there may be limitations of the system, necessitating workarounds. There should be an upfront plan to apply resources for continuous process improvement and optimization after implementation.

Keywords: CPOE; diabetes mellitus; electronic health record (EHR); inpatient glycemic management; insulin.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

© 2014 Diabetes Technology Society.

Figures

Figure 1.

Spreadsheet translating paper orders into electronic orders. This spreadsheet shows our “work in progress” translation of paper order forms into a format that could be built into computerized provider order entry order sets.

Figure 2.

Paper insulin order set.

Figure 3.

(A) Combined nutritional and correctional insulin order in computerized provider order entry (collapsed for initial order selection). (B) Administration instructions after selecting a combined nutritional and correctional insulin order in A (average scale). (C) Administration instructions after selecting a combined nutritional and correctional insulin order in A: bedtime and 2 am dosing (average scale).

Figure 4.

Defaulted nursing and hypoglycemia protocol orders. Every glycemic management order set was built with preselected orders to standardize nursing care and hypoglycemia protocol orders.

Figure 5.

Nursing insulin verification tool. (A) The nurses enter the patient’s point of care testing result for blood glucose and use the patient’s correctional scale to determine the proper correctional dose. The system then adds together the nutritional and correctional doses to come up with the total rapid-acting insulin dose. (B) This information populates a flowsheet.

Figure 6.

Insulin glucose flowsheet. In this view, 4-hour time intervals are chosen to allow a review of more data points. To view the exact times of blood glucose or insulin doses, a 1-hour time interval could instead be chosen.

Figure 7.

Flowchart of steps to move from paper to computerized provider order entry hyperglycemia management order sets.

Figure 8.

Pharmacist and nurse medication administration record (MAR) view of insulin orders. The insulin administration instructions were altered to allow formatting of the instructions to be clearly legible and well laid out for the pharmacist (A) and for the nurses in the MAR (B).

Figure 9.

Utilization of glycemic management order set. Some of our order sets have not been frequently used, such as the subcutaneous insulin postmeal order set. Note that our inpatient computerized provider order entry go-live was June 2012, so these data points represent 1 month after go-live, 1 year later, and the current state.