Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).

With respect to insulin ordering practices, there was no significant difference in the use of basal insulin in hyperglycemic patients between the preintervention period and postintervention period (81% versus 91%; P = 0.17), nor in the dose of basal insulin prescribed (results not shown), but there was an increase in the use of scheduled nutritional insulin for those patients with hyperglycemia receiving nutrition: 40% versus 75%, P < 0.001 (Table 3). The percent of patients receiving supplemental (sliding scale) insulin by itself (ie, without ever receiving basal or nutritional insulin) was lower during the postintervention period (29% versus 8%, P < 0.001). Nonsignificant differences were seen in the rates of prescribing an appropriate dose and type of nutritional insulin. Notably, there was no difference at all in the proportion of patient‐days in which insulin adjustments were made when 2 or more episodes of hyperglycemia or hypoglycemia were present during the previous day (56% of patient‐days in both groups; P = 0.90).

The primary outcome, the mean percent of glucose readings between 60 and 180 mg/dL per patient, was 59.1% in the preintervention period and 64.7% in the postintervention (P = 0.13 in unadjusted analysis; Table 3). When adjusted for A1C, admission glucose, and insulin use prior to admission, the adjusted absolute difference in the percent of glucose readings within range was 9.7% (95% confidence interval [CI], 0.6%‐18.8%; P = 0.04; Table 3). Regarding other measures of glucose control, the patient‐day weighted mean glucose was 174.7 mg/dL in the preintervention period and 164.6 mg/dL postintervention (P = 0.02), and there was no significant difference in the percent of patient‐days with any hypoglycemia (glucose <60 mg/dL) or severe hypoglycemia (glucose <40 mg/dL; Table 3). There were also no significant differences in the mean number of hypoglycemic events per patient‐day (6.8 versus 6.6 per 100 patient‐days; relative risk, 0.95; 95% CI, 0.541.67; P = 0.87) or severe hypoglycemic events per patient‐day (1.0 versus 1.4 per 100 patient‐days; relative risk, 1.38; 95% CI, 0.355.53; P = 0.65).

We also compared hospital length of stay in hours between the study groups (Table 3). Length of stay (LOS) was shorter in the postintervention arm in unadjusted analyses (112 versus 86 hours; P < 0.001), and this difference persisted when adjusted for patient insurance, race, gender, and Charlson comorbidity score (25% shorter; 95% CI, 6%‐44%). A comparison of LOS among nonstudy patients on the PACE service during these 2 time periods revealed no difference (105 versus 101 hours). When the length of stay analysis was limited to study patients with a known diagnosis of diabetes, the adjusted effect size was a 31% relative decrease in length of stay.

Figure 1A shows the percent glucose readings within range per patient by hospital day. The greatest differences between groups can be seen on hospital days 2 and 3 (11% absolute differences on both days). Similarly, Figure 1B shows the mean glucose per patient by hospital day. Again, the biggest differences are seen on hospital days 2 and 3 (20 and 23 mg/dL difference between groups, respectively). In both cases, only the day 3 comparisons were significantly different between study groups.

Figure 1

DISCUSSION

In this before‐after study, we found that a multifaceted intervention consisting of a subcutaneous insulin protocol, focused education, and an order set built into the hospital's CPOE system was associated with a significantly higher percentage of glucose readings within range per patient in analyses adjusted for patient demographics and severity of diabetes. We also found a significant decrease in patient‐day weighted mean glucose, a marked increase in appropriate use of scheduled nutritional insulin, and a concomitant decrease in sliding scale insulin only regimens during the postintervention period. Moreover, we found a shorter length of stay during the postintervention period that persisted after adjustment for several clinical factors. Importantly, the interventions described in this study require very few resources to continue indefinitely: printing costs for the management protocol, 4 hours of education delivered per year, and routine upkeep of an electronic order set.

Because this was a before‐after study, we cannot exclude the possibility that these improvements in process and outcome were due to cointerventions and/or temporal trends. However, we know of no other interventions aimed at improving diabetes care in this self‐contained service of nurses, PAs, and hospitalists. Moreover, the process improvements, especially the increase in scheduled nutritional insulin, were rather marked, unlikely to be due to temporal trends alone, and likely capable of producing the corresponding improvements in glucose control. That glucose control stopped improving after hospital day 3 may be due to the fact that subsequent adjustment to insulin orders occurred infrequently and no more often than prior to the intervention. That we did not see greater improvements in glycemic control overall may also reflect the fact that 81% of study patients with inpatient hyperglycemia received basal insulin prior to the intervention.

The reduction in patient LOS was somewhat surprising given the relatively small sample size. However, the results are consistent with those of other studies linking hyperglycemia to LOS18, 19 and we found no evidence for a temporal trend toward lower LOS on the PACE service as a whole during the same time period. While a greater proportion of patients on the PACE service were in the study in the post‐intervention period compared with the preintervention period, we found no evidence that the difference in length of stay was due to increased surveillance for nondiabetics, especially because eligibility criteria depended on phlebotomy glucose values, which were uniformly tested in all inpatients. Also, effects on length of stay were actually stronger when limited to patients with known diabetes. Finally, we controlled for several predictors of length of stay, although we still cannot exclude the possibility of unmeasured confounding between groups.

Since ADA and ACE issued guidelines for inpatient management of diabetes and hyperglycemia, many institutions have developed subcutaneous insulin algorithms, educational curricula, and/or order sets to increase compliance with these guidelines and improve glycemic control. Some of these efforts have been studied and some have been successful in their efforts.13, 14, 2023 Unfortunately, most of these programs have not rigorously assessed their impact on process and outcomes, and the most effective studies published to date have involved interventions much more intensive than those described here. For example, Rush University's intervention was associated with a 50 mg/dL decrease in mean blood glucose but involved an endocrinologist rounding twice daily with house officers for 2 weeks at a time.13 At Northwestern University, a diabetes management service run by nurse practitioners was established, and the focus was on the conversion from intravenous to subcutaneous insulin regimens.14 The RABBIT 2 study that demonstrated the benefits of a basal‐bolus insulin regimen used daily rounding with an endocrinologist.12 More modestly, a program in Pitt County Memorial Hospital in Greenville, NC, relied mostly on diabetes nurse case managers, a strategy which reduced hospital‐wide mean glucose levels as well as LOS, although the greatest improvements in glycemic control were seen in the ICU.19 Our findings are much more consistent with those from University of California San Diego, as yet unpublished, which also used an algorithm, computerized order set, education, as well as continuous quality improvement methods to achieve its aims.22

Our study has several limitations, including being conducted on 1 general medicine service at 1 academic medical center. Moreover, this service, using a physician assistant/hospitalist model, a closed geographic unit, and fairly generous staffing ratio, is likely different from those in many settings and may limit the generalizability of our findings. However, this model allowed us to conduct the study in a laboratory relatively untouched by other cointerventions. Furthermore, the use of PAs in this way may become more common as both academic and community hospitals rely more on mid‐level providers. Our study had a relatively low percentage of patients without a known diagnosis of diabetes compared with other studies, again potentially but not necessarily limiting generalizability. This finding has been shown in other studies at our institution24 and may be due to the high rate of screening for diabetes in the community. Another limitation is that this was a nonrandomized, before‐after trial. However, all subjects were prospectively enrolled to improve comparability, and we performed rigorous adjustment for multiple potential confounding factors. Also, this study had limited statistical power to detect differences in hypoglycemia rates. The preintervention arm was smaller than planned due to fewer diabetic patients than expected on the service and a higher number of exclusions; we prolonged the postintervention period to achieve the desired sample size for that arm of the study.

Our study also has several strengths, including electronic capture of many processes of care and a methodology to operationalize them into measures of protocol adherence. Our metrics of glycemic control were rigorously designed and based on a national task force on inpatient glycemic control sponsored by the Society of Hospital Medicine, with representation from the ADA and AACE.25

Potential future improvements to this intervention include modifications to the daily adjustment algorithm to improve its usability and ability to improve glucose control. Another is the use of high‐reliability methods to improve order set use and daily insulin adjustment, including alerts within the CPOE system and nurse empowerment to contact medical teams if glucose levels are out of range (eg, if greater than 180 mg/dL, not just if greater than 350 or 400 mg/dL). Future research directions include multicenter, randomized controlled trials of these types of interventions and an analysis of more distal patient outcomes including total healthcare utilization, infection rates, end‐organ damage, and mortality.

In conclusion, we found a relationship between a relatively low‐cost quality improvement intervention and improved glycemic control in the non‐ICU general medical setting. Such a finding suggests the benefits of the algorithm itself to improve glucose control and of our implementation strategy. Other institutions may find this intervention a useful starting point for their own quality improvement efforts. Both the algorithm and implementation strategy are deserving of further improvements and future study.

Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).

With respect to insulin ordering practices, there was no significant difference in the use of basal insulin in hyperglycemic patients between the preintervention period and postintervention period (81% versus 91%; P = 0.17), nor in the dose of basal insulin prescribed (results not shown), but there was an increase in the use of scheduled nutritional insulin for those patients with hyperglycemia receiving nutrition: 40% versus 75%, P < 0.001 (Table 3). The percent of patients receiving supplemental (sliding scale) insulin by itself (ie, without ever receiving basal or nutritional insulin) was lower during the postintervention period (29% versus 8%, P < 0.001). Nonsignificant differences were seen in the rates of prescribing an appropriate dose and type of nutritional insulin. Notably, there was no difference at all in the proportion of patient‐days in which insulin adjustments were made when 2 or more episodes of hyperglycemia or hypoglycemia were present during the previous day (56% of patient‐days in both groups; P = 0.90).

The primary outcome, the mean percent of glucose readings between 60 and 180 mg/dL per patient, was 59.1% in the preintervention period and 64.7% in the postintervention (P = 0.13 in unadjusted analysis; Table 3). When adjusted for A1C, admission glucose, and insulin use prior to admission, the adjusted absolute difference in the percent of glucose readings within range was 9.7% (95% confidence interval [CI], 0.6%‐18.8%; P = 0.04; Table 3). Regarding other measures of glucose control, the patient‐day weighted mean glucose was 174.7 mg/dL in the preintervention period and 164.6 mg/dL postintervention (P = 0.02), and there was no significant difference in the percent of patient‐days with any hypoglycemia (glucose <60 mg/dL) or severe hypoglycemia (glucose <40 mg/dL; Table 3). There were also no significant differences in the mean number of hypoglycemic events per patient‐day (6.8 versus 6.6 per 100 patient‐days; relative risk, 0.95; 95% CI, 0.541.67; P = 0.87) or severe hypoglycemic events per patient‐day (1.0 versus 1.4 per 100 patient‐days; relative risk, 1.38; 95% CI, 0.355.53; P = 0.65).

We also compared hospital length of stay in hours between the study groups (Table 3). Length of stay (LOS) was shorter in the postintervention arm in unadjusted analyses (112 versus 86 hours; P < 0.001), and this difference persisted when adjusted for patient insurance, race, gender, and Charlson comorbidity score (25% shorter; 95% CI, 6%‐44%). A comparison of LOS among nonstudy patients on the PACE service during these 2 time periods revealed no difference (105 versus 101 hours). When the length of stay analysis was limited to study patients with a known diagnosis of diabetes, the adjusted effect size was a 31% relative decrease in length of stay.

Figure 1A shows the percent glucose readings within range per patient by hospital day. The greatest differences between groups can be seen on hospital days 2 and 3 (11% absolute differences on both days). Similarly, Figure 1B shows the mean glucose per patient by hospital day. Again, the biggest differences are seen on hospital days 2 and 3 (20 and 23 mg/dL difference between groups, respectively). In both cases, only the day 3 comparisons were significantly different between study groups.

Figure 1

DISCUSSION

In this before‐after study, we found that a multifaceted intervention consisting of a subcutaneous insulin protocol, focused education, and an order set built into the hospital's CPOE system was associated with a significantly higher percentage of glucose readings within range per patient in analyses adjusted for patient demographics and severity of diabetes. We also found a significant decrease in patient‐day weighted mean glucose, a marked increase in appropriate use of scheduled nutritional insulin, and a concomitant decrease in sliding scale insulin only regimens during the postintervention period. Moreover, we found a shorter length of stay during the postintervention period that persisted after adjustment for several clinical factors. Importantly, the interventions described in this study require very few resources to continue indefinitely: printing costs for the management protocol, 4 hours of education delivered per year, and routine upkeep of an electronic order set.

Because this was a before‐after study, we cannot exclude the possibility that these improvements in process and outcome were due to cointerventions and/or temporal trends. However, we know of no other interventions aimed at improving diabetes care in this self‐contained service of nurses, PAs, and hospitalists. Moreover, the process improvements, especially the increase in scheduled nutritional insulin, were rather marked, unlikely to be due to temporal trends alone, and likely capable of producing the corresponding improvements in glucose control. That glucose control stopped improving after hospital day 3 may be due to the fact that subsequent adjustment to insulin orders occurred infrequently and no more often than prior to the intervention. That we did not see greater improvements in glycemic control overall may also reflect the fact that 81% of study patients with inpatient hyperglycemia received basal insulin prior to the intervention.

The reduction in patient LOS was somewhat surprising given the relatively small sample size. However, the results are consistent with those of other studies linking hyperglycemia to LOS18, 19 and we found no evidence for a temporal trend toward lower LOS on the PACE service as a whole during the same time period. While a greater proportion of patients on the PACE service were in the study in the post‐intervention period compared with the preintervention period, we found no evidence that the difference in length of stay was due to increased surveillance for nondiabetics, especially because eligibility criteria depended on phlebotomy glucose values, which were uniformly tested in all inpatients. Also, effects on length of stay were actually stronger when limited to patients with known diabetes. Finally, we controlled for several predictors of length of stay, although we still cannot exclude the possibility of unmeasured confounding between groups.

Since ADA and ACE issued guidelines for inpatient management of diabetes and hyperglycemia, many institutions have developed subcutaneous insulin algorithms, educational curricula, and/or order sets to increase compliance with these guidelines and improve glycemic control. Some of these efforts have been studied and some have been successful in their efforts.13, 14, 2023 Unfortunately, most of these programs have not rigorously assessed their impact on process and outcomes, and the most effective studies published to date have involved interventions much more intensive than those described here. For example, Rush University's intervention was associated with a 50 mg/dL decrease in mean blood glucose but involved an endocrinologist rounding twice daily with house officers for 2 weeks at a time.13 At Northwestern University, a diabetes management service run by nurse practitioners was established, and the focus was on the conversion from intravenous to subcutaneous insulin regimens.14 The RABBIT 2 study that demonstrated the benefits of a basal‐bolus insulin regimen used daily rounding with an endocrinologist.12 More modestly, a program in Pitt County Memorial Hospital in Greenville, NC, relied mostly on diabetes nurse case managers, a strategy which reduced hospital‐wide mean glucose levels as well as LOS, although the greatest improvements in glycemic control were seen in the ICU.19 Our findings are much more consistent with those from University of California San Diego, as yet unpublished, which also used an algorithm, computerized order set, education, as well as continuous quality improvement methods to achieve its aims.22

Our study has several limitations, including being conducted on 1 general medicine service at 1 academic medical center. Moreover, this service, using a physician assistant/hospitalist model, a closed geographic unit, and fairly generous staffing ratio, is likely different from those in many settings and may limit the generalizability of our findings. However, this model allowed us to conduct the study in a laboratory relatively untouched by other cointerventions. Furthermore, the use of PAs in this way may become more common as both academic and community hospitals rely more on mid‐level providers. Our study had a relatively low percentage of patients without a known diagnosis of diabetes compared with other studies, again potentially but not necessarily limiting generalizability. This finding has been shown in other studies at our institution24 and may be due to the high rate of screening for diabetes in the community. Another limitation is that this was a nonrandomized, before‐after trial. However, all subjects were prospectively enrolled to improve comparability, and we performed rigorous adjustment for multiple potential confounding factors. Also, this study had limited statistical power to detect differences in hypoglycemia rates. The preintervention arm was smaller than planned due to fewer diabetic patients than expected on the service and a higher number of exclusions; we prolonged the postintervention period to achieve the desired sample size for that arm of the study.

Our study also has several strengths, including electronic capture of many processes of care and a methodology to operationalize them into measures of protocol adherence. Our metrics of glycemic control were rigorously designed and based on a national task force on inpatient glycemic control sponsored by the Society of Hospital Medicine, with representation from the ADA and AACE.25

Potential future improvements to this intervention include modifications to the daily adjustment algorithm to improve its usability and ability to improve glucose control. Another is the use of high‐reliability methods to improve order set use and daily insulin adjustment, including alerts within the CPOE system and nurse empowerment to contact medical teams if glucose levels are out of range (eg, if greater than 180 mg/dL, not just if greater than 350 or 400 mg/dL). Future research directions include multicenter, randomized controlled trials of these types of interventions and an analysis of more distal patient outcomes including total healthcare utilization, infection rates, end‐organ damage, and mortality.

In conclusion, we found a relationship between a relatively low‐cost quality improvement intervention and improved glycemic control in the non‐ICU general medical setting. Such a finding suggests the benefits of the algorithm itself to improve glucose control and of our implementation strategy. Other institutions may find this intervention a useful starting point for their own quality improvement efforts. Both the algorithm and implementation strategy are deserving of further improvements and future study.

Diabetes mellitus and/or inpatient hyperglycemia are common comorbid conditions in hospitalized patients. Recent surveys show that over 90% of hospitalized diabetic patients experience hyperglycemia (>200 mg/dL), and in nearly 1 in 5 of these patients hyperglycemia persists for 3 days or more.1 Hyperglycemia among inpatients without a previous history of diabetes mellitus is also very common.2 Observational studies have shown that hyperglycemia in hospitalized patients is associated with adverse outcomes including infectious complications, increased length of stay, and increased mortality.27 Recent randomized controlled trials have demonstrated that aggressive treatment of inpatient hyperglycemia improves outcomes in surgical and medical intensive care units.8, 9

Based on the available data, the American Diabetes Association (ADA) now advocates good metabolic control, defined as preprandial glucose levels of 90 to 130 mg/dL and peak postprandial glucose levels <180 mg/dL in hospitalized nonintensive care unit (ICU) patients.10 To reach these targets, the ADA and American College of Endocrinology (ACE) suggest that multidisciplinary teams develop and implement hyperglycemia management guidelines and protocols.11 Protocols should promote the use of continuous intravenous insulin infusions or scheduled basal‐bolus subcutaneous insulin regimens. Subcutaneous insulin protocols should include target glucose levels, basal, nutritional, and supplemental insulin, and daily dose adjustments.6 A recent randomized controlled trial of non‐ICU inpatients demonstrated that such a basal‐bolus insulin regimen results in improved glucose control compared with a sliding scale only regimen.12

To date, few published studies have investigated the best ways to implement such management protocols; those that have are often resource‐intensive, for example involving daily involvement of nurse practitioners or diabetologists.13, 14 It is therefore not known how best to implement an inpatient diabetes management program that is effective, efficient, and self‐perpetuating. At Brigham and Women's Hospital (BWH), we have been refining a subcutaneous insulin protocol, focused provider education, and more recently a computerized order set to overcome barriers related to fear of hypoglycemia, delays in insulin prescribing, and unfamiliarity with inpatient glucose management.15 The aims of this current trial were to evaluate the effects of these interventions on a geographically localized general medical service previously naive to these interventions to evaluate their effects on glycemic control, patient safety, and processes of care. We hypothesized that these interventions would improve glycemic control and increase use of basal‐bolus insulin orders without increasing the rate of hypoglycemia.

METHODS

RESULTS

We prospectively identified 248 potential patients for the study. We subsequently excluded 79 patients for the following reasons: no glucose readings beyond hospital day 1 while on PACE service (34 patients); never admitted to PACE service (15 patients); no diabetes or inpatient hyperglycemia (9 patients, mostly patients prescribed an insulin sliding scale prophylactically to avoid steroid‐induced hyperglycemia); type 1 diabetes (13 patients); TPN, DKA, or HHS (5 patients); and palliative care (3 patients). The remaining 169 patients included 63 from the preintervention period(out of 489 total admissions to the PACE service; 13%) and 106 patients in the postintervention period (out of 565 admissions; 19%). These patients had 2447 glucose readings, or an average of 3.6 glucose readings per monitored patient‐day in the preintervention period and 3.3 glucose readings per patient‐day in the postintervention period. Even including the 34 patients who were excluded for lack of glucose readings, glucose data were still available for 717 out of a potential 775 patient‐days (93%). Characteristics for all included patients are shown in Table 2. The mean admission glucose was 197 mg/dL, mean A1C was 8.4%, 54% of the patients were prescribed insulin prior to admission, and 7% had no prior diagnosis of diabetes. There were no significant differences in baseline characteristics between the 2 patient groups except for Charlson score, which was higher in the preintervention group (87% versus 74% with score 2 or higher; Table 2). The top diagnosis‐related groups for the entire cohort included: heart failure and shock (12 patients); kidney and urinary tract infections (12 patients); esophagitis, gastroenteritis, and miscellaneous digestive disorders (11 patients); chronic obstructive pulmonary disease (10 patients); renal failure (10 patients); simple pneumonia and pleurisy (7 patients); disorders of the pancreas except malignancy (6 patients); chest pain (5 patients); and cellulitis (5 patients).

With respect to insulin ordering practices, there was no significant difference in the use of basal insulin in hyperglycemic patients between the preintervention period and postintervention period (81% versus 91%; P = 0.17), nor in the dose of basal insulin prescribed (results not shown), but there was an increase in the use of scheduled nutritional insulin for those patients with hyperglycemia receiving nutrition: 40% versus 75%, P < 0.001 (Table 3). The percent of patients receiving supplemental (sliding scale) insulin by itself (ie, without ever receiving basal or nutritional insulin) was lower during the postintervention period (29% versus 8%, P < 0.001). Nonsignificant differences were seen in the rates of prescribing an appropriate dose and type of nutritional insulin. Notably, there was no difference at all in the proportion of patient‐days in which insulin adjustments were made when 2 or more episodes of hyperglycemia or hypoglycemia were present during the previous day (56% of patient‐days in both groups; P = 0.90).

The primary outcome, the mean percent of glucose readings between 60 and 180 mg/dL per patient, was 59.1% in the preintervention period and 64.7% in the postintervention (P = 0.13 in unadjusted analysis; Table 3). When adjusted for A1C, admission glucose, and insulin use prior to admission, the adjusted absolute difference in the percent of glucose readings within range was 9.7% (95% confidence interval [CI], 0.6%‐18.8%; P = 0.04; Table 3). Regarding other measures of glucose control, the patient‐day weighted mean glucose was 174.7 mg/dL in the preintervention period and 164.6 mg/dL postintervention (P = 0.02), and there was no significant difference in the percent of patient‐days with any hypoglycemia (glucose <60 mg/dL) or severe hypoglycemia (glucose <40 mg/dL; Table 3). There were also no significant differences in the mean number of hypoglycemic events per patient‐day (6.8 versus 6.6 per 100 patient‐days; relative risk, 0.95; 95% CI, 0.541.67; P = 0.87) or severe hypoglycemic events per patient‐day (1.0 versus 1.4 per 100 patient‐days; relative risk, 1.38; 95% CI, 0.355.53; P = 0.65).

We also compared hospital length of stay in hours between the study groups (Table 3). Length of stay (LOS) was shorter in the postintervention arm in unadjusted analyses (112 versus 86 hours; P < 0.001), and this difference persisted when adjusted for patient insurance, race, gender, and Charlson comorbidity score (25% shorter; 95% CI, 6%‐44%). A comparison of LOS among nonstudy patients on the PACE service during these 2 time periods revealed no difference (105 versus 101 hours). When the length of stay analysis was limited to study patients with a known diagnosis of diabetes, the adjusted effect size was a 31% relative decrease in length of stay.

Figure 1A shows the percent glucose readings within range per patient by hospital day. The greatest differences between groups can be seen on hospital days 2 and 3 (11% absolute differences on both days). Similarly, Figure 1B shows the mean glucose per patient by hospital day. Again, the biggest differences are seen on hospital days 2 and 3 (20 and 23 mg/dL difference between groups, respectively). In both cases, only the day 3 comparisons were significantly different between study groups.

Figure 1

DISCUSSION

In this before‐after study, we found that a multifaceted intervention consisting of a subcutaneous insulin protocol, focused education, and an order set built into the hospital's CPOE system was associated with a significantly higher percentage of glucose readings within range per patient in analyses adjusted for patient demographics and severity of diabetes. We also found a significant decrease in patient‐day weighted mean glucose, a marked increase in appropriate use of scheduled nutritional insulin, and a concomitant decrease in sliding scale insulin only regimens during the postintervention period. Moreover, we found a shorter length of stay during the postintervention period that persisted after adjustment for several clinical factors. Importantly, the interventions described in this study require very few resources to continue indefinitely: printing costs for the management protocol, 4 hours of education delivered per year, and routine upkeep of an electronic order set.

Because this was a before‐after study, we cannot exclude the possibility that these improvements in process and outcome were due to cointerventions and/or temporal trends. However, we know of no other interventions aimed at improving diabetes care in this self‐contained service of nurses, PAs, and hospitalists. Moreover, the process improvements, especially the increase in scheduled nutritional insulin, were rather marked, unlikely to be due to temporal trends alone, and likely capable of producing the corresponding improvements in glucose control. That glucose control stopped improving after hospital day 3 may be due to the fact that subsequent adjustment to insulin orders occurred infrequently and no more often than prior to the intervention. That we did not see greater improvements in glycemic control overall may also reflect the fact that 81% of study patients with inpatient hyperglycemia received basal insulin prior to the intervention.

The reduction in patient LOS was somewhat surprising given the relatively small sample size. However, the results are consistent with those of other studies linking hyperglycemia to LOS18, 19 and we found no evidence for a temporal trend toward lower LOS on the PACE service as a whole during the same time period. While a greater proportion of patients on the PACE service were in the study in the post‐intervention period compared with the preintervention period, we found no evidence that the difference in length of stay was due to increased surveillance for nondiabetics, especially because eligibility criteria depended on phlebotomy glucose values, which were uniformly tested in all inpatients. Also, effects on length of stay were actually stronger when limited to patients with known diabetes. Finally, we controlled for several predictors of length of stay, although we still cannot exclude the possibility of unmeasured confounding between groups.

Since ADA and ACE issued guidelines for inpatient management of diabetes and hyperglycemia, many institutions have developed subcutaneous insulin algorithms, educational curricula, and/or order sets to increase compliance with these guidelines and improve glycemic control. Some of these efforts have been studied and some have been successful in their efforts.13, 14, 2023 Unfortunately, most of these programs have not rigorously assessed their impact on process and outcomes, and the most effective studies published to date have involved interventions much more intensive than those described here. For example, Rush University's intervention was associated with a 50 mg/dL decrease in mean blood glucose but involved an endocrinologist rounding twice daily with house officers for 2 weeks at a time.13 At Northwestern University, a diabetes management service run by nurse practitioners was established, and the focus was on the conversion from intravenous to subcutaneous insulin regimens.14 The RABBIT 2 study that demonstrated the benefits of a basal‐bolus insulin regimen used daily rounding with an endocrinologist.12 More modestly, a program in Pitt County Memorial Hospital in Greenville, NC, relied mostly on diabetes nurse case managers, a strategy which reduced hospital‐wide mean glucose levels as well as LOS, although the greatest improvements in glycemic control were seen in the ICU.19 Our findings are much more consistent with those from University of California San Diego, as yet unpublished, which also used an algorithm, computerized order set, education, as well as continuous quality improvement methods to achieve its aims.22

Our study has several limitations, including being conducted on 1 general medicine service at 1 academic medical center. Moreover, this service, using a physician assistant/hospitalist model, a closed geographic unit, and fairly generous staffing ratio, is likely different from those in many settings and may limit the generalizability of our findings. However, this model allowed us to conduct the study in a laboratory relatively untouched by other cointerventions. Furthermore, the use of PAs in this way may become more common as both academic and community hospitals rely more on mid‐level providers. Our study had a relatively low percentage of patients without a known diagnosis of diabetes compared with other studies, again potentially but not necessarily limiting generalizability. This finding has been shown in other studies at our institution24 and may be due to the high rate of screening for diabetes in the community. Another limitation is that this was a nonrandomized, before‐after trial. However, all subjects were prospectively enrolled to improve comparability, and we performed rigorous adjustment for multiple potential confounding factors. Also, this study had limited statistical power to detect differences in hypoglycemia rates. The preintervention arm was smaller than planned due to fewer diabetic patients than expected on the service and a higher number of exclusions; we prolonged the postintervention period to achieve the desired sample size for that arm of the study.

Our study also has several strengths, including electronic capture of many processes of care and a methodology to operationalize them into measures of protocol adherence. Our metrics of glycemic control were rigorously designed and based on a national task force on inpatient glycemic control sponsored by the Society of Hospital Medicine, with representation from the ADA and AACE.25

Potential future improvements to this intervention include modifications to the daily adjustment algorithm to improve its usability and ability to improve glucose control. Another is the use of high‐reliability methods to improve order set use and daily insulin adjustment, including alerts within the CPOE system and nurse empowerment to contact medical teams if glucose levels are out of range (eg, if greater than 180 mg/dL, not just if greater than 350 or 400 mg/dL). Future research directions include multicenter, randomized controlled trials of these types of interventions and an analysis of more distal patient outcomes including total healthcare utilization, infection rates, end‐organ damage, and mortality.

In conclusion, we found a relationship between a relatively low‐cost quality improvement intervention and improved glycemic control in the non‐ICU general medical setting. Such a finding suggests the benefits of the algorithm itself to improve glucose control and of our implementation strategy. Other institutions may find this intervention a useful starting point for their own quality improvement efforts. Both the algorithm and implementation strategy are deserving of further improvements and future study.

Is hospital medicine a bona fide specialty? Do something long enough, and as Justice Potter Stewart said when defining a certain taboo carnal subject many years ago, I know it when I see it. Although working groups may struggle to conceive a master set of core competencies for hospitalists, I will tell you this: no texts are needed, and you know that you are on to something when 2 hospitalists practicing 3000 miles apart shoot each other a knowing glance and, without words, just understand what the other is thinking. After 10 years of practice in several hospitals, I have had enough mind melds to last a lifetime. Who needs science after all? I mean, how many of us can keep a straight face when asked if we have ever heard this line: Ah, yes, Dr. Flansbaum, umm, I am Dr. Smith from the surgical ICU, and we have a patient hospital day 34 status post Whipple that is no longer surgically active. See, you are smiling already. Do I have to finish the sentence for you?

What follows is a collective experience of things that I call the grind: things so small, so inconsequential, that no one will ever cite them individually as the deal breakers of the day. Collectively, they are the fabric of who we are and that little sore on the inside of our cheek that we just have to touch every few minutes in order to remind ourselves of why dermatologists always look so happy. Any accompanying sage lessons are also free of charge.

• 1

  Why at the end of the day, always on a weekend after you have found a comfortable seat as far away from the nurse's station as possible, do you open up the chart and see 1 line of open space left on the last page of the progress notepaper? Even better, why is the note above it a follow‐up from vascular surgery, written in font size 24, in a form of Sanskrit that not even Steven Hawkings would recognize?

• 2

  Okay, how about this: For patients with loooong lengths of stay, how many creative ways can you write Awaiting placement, afebrile, no complaints (in compliance with billing rules of course)? The correct answer is between 16 and 23. A thesaurus helps for word number 1try stable to startand change your pen from fine point to medium point on odd days. Then add tolerated breakfast on Monday, lunch on Tuesday, and dinner on Wednesday. Voila! Who said this was tough?

• 3

  Okay, this one boggles my mind. You wish to auscultate a set of lungs. The patient is sitting on his gown. You attempt to lift the gown, and instead of raising his tush, the patient continues to sit while you tug away. Is this just me? It happens every week.

• 4

  The medical students are great. They are ambitious and make teaching fun. Why though, at 9:59 _AM, with an upcoming meeting with your chief at 10, are there no PC terminals at the station available except for the MSIII on MySpace.com with the chart you could not find (for 5 minutes) underneath his clipboard? Yes, Virginia, make me remember those days.

• 5

  Clearly, this is one of the more helpful lines you can get from a consult: Patient needs to be medically optimized, and consider head MRI. Consider? Is not that why I called the consult in the first place? Let us consider not to use the word consider any more. Consider that. I feel better.

• 6

  While we are on the topic of consultants, a dollar goes to you if this has never happened during your time on the wards: (1) the consultant visits, (2) the consultant evaluates, (3) you speak with the consultant, (4) all of you agree that the patient can go home, and (5) you then read the consult after the patient is dressed and his IV is out. Umm, a head CT before discharge and please have the neurosurgery clear the patient before discharge? Am I working in a parallel universe? Too much caffeine? Lord, give me strength.

• 7

  Your beeper goes off at 2:57 _PM. At 2:57 _PM plus 5 seconds, you call the number you were paged from and no one answers. Does the word ponderous come to mind? This invariably happens every day, of course, typically when I am in the midst of multitasking 4 conversations. However, I extract some form of perverse cosmic revenge when I need to make a call and pick up an open extension from a ringing multiline phone. Invariably, I click the button to engage a line and, oops, good bye caller. I am only kidding; that never happens (is my nose really growing?). Just think, I could have been the one screaming, Is Laverne here?

• 8

  You get an admission, a patient whom you have never met, and his room is listed as 428. You walk in, and the patient nearest to you is a well‐groomed middle‐age individual with a welcoming smile. The patient next to him is breathing fire, screaming at an imaginary executioner, and claiming that you are the guilty party and need to die. Which patient do you think is yours?

• 9

  Those of you who work with housestaff will appreciate this one (file it under systems issue: fix next week): You have a discussion with a patient regarding his _PM discharge at 11 _AM. You arrange the follow‐up, you review the new medications, you discuss who will pick him up after dinner, and so forth. You get that warm and fuzzy feeling that you have done your job and all is right in the universe. Naturally, you also tell the resident that the patient can go home. Lo and behold, you look at your census the following morning, and the name of the aforementioned patient radiates like a beacon from the screen. You then poke your head into the room, feeling assured that it is merely an error, and the aforementioned patient is lying in bed, smiling and happy to see you. You ask, What happened? The reply, I don't know. Didn't your son come last night to pick you up? The response is yes. After the penetrating ulcer in your stomach bores a little deeper, you discover the official discharge order did not occur, and the patient was content eating chipped beef and sleeping on said contoured mattress 1 more night. Serenity now, serenity now.

• 10

  The fifth vital sign? Is that the new black? Heck, number 5, I think we are up to 11 or 12 these days. Need a new metric installed? You guessed it: add it to the list!

• 11

  Do you ever get LOS fatigue with a particular patient that is so severe you go to bed the previous night and have problems falling asleep? Really, what do you say to a person when his hospital stay exceeds, say, 5 months? Yes, I actually think of topics and issues that I can incorporate into the conversation which will spice up the relationship. New bed sheets, a fresh coat of paint? It would make a good Seinfeld episode, no?

• 12

  Is it me, or is having 2 patients in the same room like being a flight attendant wheeling around the beverage cart? Get one the peanuts, and then the other wants the pretzels. For sure, add 10 minutes to your time in room 728 tomorrow.

• 13

  If a patient is unable to leave the hospital for reasons unrelated to discharge planning (locked out of his house, the child is out of town until the next morning, etc.), why do I feel so naughty when I get off the phone with the MCO medical director after offering explanations? I do not get it. You think that the hospital employs a battery of runners to padlock homes and steal patient's clothing. Who wrote this playbook?

• 14

  I love my consultants. Really, I do. I am not picking on them today. The high points of my day are the exchanges that I have with my subspecialty colleagues. However, the myopia that pervades some sign off notes give me pause. For example, a patient admitted for gastrointestinal bleed, s/p EGD, and stable at 72 hours post arrival receives a consultant note as follows: if patient eating and ambulating, can be discharged home as per PMD. Surely, when the level of transferred oversight shifts to the level of caloric ingestion and sneaker use, well, let us just say that I am all for some new E&M codes. They did not tell me about this in hospitalist school.

• 15

  Don't you love the feeling of your beeper going off, and 30 seconds after the first page, boom, it goes off againboth to the same extension? I mean really, I am a nice guy, but do you really want to rile me up this early in the morning?

• 16

  The nurse pages you from 1 floor away10 seconds from where you are standing. You recognize that number, you knew that it was coming, and for sure, waiting on the other end is that family member who hails from a foreboding place. How quickly does your brain do the computationdo I use the phone and let my fingers do the talking, or make that stroll and have that face‐to‐face summit? No sarcastic comment is needed. I see us now, hands joined, joyfully singing Kumbaya in a loving embrace.

• 17

  Gee, it is not busy today. Say that on the wards and you get a leering glance. However, say that in the emergency room and you will meet your death. There is something about that phrase and the emergency department. The nurses there do not forget, although a Starbucks cappuccino does put a nice salve on the wound.

• 18

  On your day off, do you ever notice that your beeper vibrates on your belt and you are not wearing it? I am not kidding.

• 19

  An irony of life: I have developed an immunity to cigarette smoke in hospital bathrooms. Why is that? It is like peanut butter and jelly. They just seem so happy together.

• 20

  Do you want to transfer that patient to psychiatry? No, no, no, you silly hospitalistdid you not notice that abnormal BUN and atypical lymphocyte on the peripheral smear? Hey, Dr. Freud, can you write me for some of that Prozac too!

• 21

  We need to consult a rulebook on chair ownership. Did you ever notice (a tinge of Andy Rooney here) that case managers own their seats? I know that the world is not quite right when a case manager shoots me that dastardly glare, as if to say, Flee, you silly physician, I live at this station, you are merely my guest! As far as that chair is concerned, perhaps if a small plaque is added to the backrest with a suitable donation, my legs will finally get their deserved daily rest.

• 22

  Finally, do you want to become invisible? Go to the reading board and stand behind a radiologist at 10:30 _AM. Do it long enough, and after a few days, you will be saying I'm Good Enough, I'm Smart Enough, and Doggone It, People Like Me! Do you want to disappear completely? Try it on a Friday.

Okay, okay, I will stop there. It is funny, though; this stuff really happens. Despite the aggravation, I see these commonalities as the glue that binds us, assists in building the esprit de corps in our profession, and adds a little levity to the work place. Outside the hospital (not inside, of course), I can confidently state that these routines are part of who I am. After all, it is all about the knowing glance that I mentioned previously. The humorous part is that we are certainly on someone else's list. Probably a nurse, an emergency room doctor, or maybe a physician's assistant is scribing away at this minute, and we are number 7: Those hospitalists really tick me off.

EPILOGUE

Note to selflook in the mirror occasionally; you might learn something.

I apologize to all my nonhospitalist colleagues if you sneered. I love all of you. Today.

Is hospital medicine a bona fide specialty? Do something long enough, and as Justice Potter Stewart said when defining a certain taboo carnal subject many years ago, I know it when I see it. Although working groups may struggle to conceive a master set of core competencies for hospitalists, I will tell you this: no texts are needed, and you know that you are on to something when 2 hospitalists practicing 3000 miles apart shoot each other a knowing glance and, without words, just understand what the other is thinking. After 10 years of practice in several hospitals, I have had enough mind melds to last a lifetime. Who needs science after all? I mean, how many of us can keep a straight face when asked if we have ever heard this line: Ah, yes, Dr. Flansbaum, umm, I am Dr. Smith from the surgical ICU, and we have a patient hospital day 34 status post Whipple that is no longer surgically active. See, you are smiling already. Do I have to finish the sentence for you?

What follows is a collective experience of things that I call the grind: things so small, so inconsequential, that no one will ever cite them individually as the deal breakers of the day. Collectively, they are the fabric of who we are and that little sore on the inside of our cheek that we just have to touch every few minutes in order to remind ourselves of why dermatologists always look so happy. Any accompanying sage lessons are also free of charge.

• 1

  Why at the end of the day, always on a weekend after you have found a comfortable seat as far away from the nurse's station as possible, do you open up the chart and see 1 line of open space left on the last page of the progress notepaper? Even better, why is the note above it a follow‐up from vascular surgery, written in font size 24, in a form of Sanskrit that not even Steven Hawkings would recognize?

• 2

  Okay, how about this: For patients with loooong lengths of stay, how many creative ways can you write Awaiting placement, afebrile, no complaints (in compliance with billing rules of course)? The correct answer is between 16 and 23. A thesaurus helps for word number 1try stable to startand change your pen from fine point to medium point on odd days. Then add tolerated breakfast on Monday, lunch on Tuesday, and dinner on Wednesday. Voila! Who said this was tough?

• 3

  Okay, this one boggles my mind. You wish to auscultate a set of lungs. The patient is sitting on his gown. You attempt to lift the gown, and instead of raising his tush, the patient continues to sit while you tug away. Is this just me? It happens every week.

• 4

  The medical students are great. They are ambitious and make teaching fun. Why though, at 9:59 _AM, with an upcoming meeting with your chief at 10, are there no PC terminals at the station available except for the MSIII on MySpace.com with the chart you could not find (for 5 minutes) underneath his clipboard? Yes, Virginia, make me remember those days.

• 5

  Clearly, this is one of the more helpful lines you can get from a consult: Patient needs to be medically optimized, and consider head MRI. Consider? Is not that why I called the consult in the first place? Let us consider not to use the word consider any more. Consider that. I feel better.

• 6

  While we are on the topic of consultants, a dollar goes to you if this has never happened during your time on the wards: (1) the consultant visits, (2) the consultant evaluates, (3) you speak with the consultant, (4) all of you agree that the patient can go home, and (5) you then read the consult after the patient is dressed and his IV is out. Umm, a head CT before discharge and please have the neurosurgery clear the patient before discharge? Am I working in a parallel universe? Too much caffeine? Lord, give me strength.

• 7

  Your beeper goes off at 2:57 _PM. At 2:57 _PM plus 5 seconds, you call the number you were paged from and no one answers. Does the word ponderous come to mind? This invariably happens every day, of course, typically when I am in the midst of multitasking 4 conversations. However, I extract some form of perverse cosmic revenge when I need to make a call and pick up an open extension from a ringing multiline phone. Invariably, I click the button to engage a line and, oops, good bye caller. I am only kidding; that never happens (is my nose really growing?). Just think, I could have been the one screaming, Is Laverne here?

• 8

  You get an admission, a patient whom you have never met, and his room is listed as 428. You walk in, and the patient nearest to you is a well‐groomed middle‐age individual with a welcoming smile. The patient next to him is breathing fire, screaming at an imaginary executioner, and claiming that you are the guilty party and need to die. Which patient do you think is yours?

• 9

  Those of you who work with housestaff will appreciate this one (file it under systems issue: fix next week): You have a discussion with a patient regarding his _PM discharge at 11 _AM. You arrange the follow‐up, you review the new medications, you discuss who will pick him up after dinner, and so forth. You get that warm and fuzzy feeling that you have done your job and all is right in the universe. Naturally, you also tell the resident that the patient can go home. Lo and behold, you look at your census the following morning, and the name of the aforementioned patient radiates like a beacon from the screen. You then poke your head into the room, feeling assured that it is merely an error, and the aforementioned patient is lying in bed, smiling and happy to see you. You ask, What happened? The reply, I don't know. Didn't your son come last night to pick you up? The response is yes. After the penetrating ulcer in your stomach bores a little deeper, you discover the official discharge order did not occur, and the patient was content eating chipped beef and sleeping on said contoured mattress 1 more night. Serenity now, serenity now.

• 10

  The fifth vital sign? Is that the new black? Heck, number 5, I think we are up to 11 or 12 these days. Need a new metric installed? You guessed it: add it to the list!

• 11

  Do you ever get LOS fatigue with a particular patient that is so severe you go to bed the previous night and have problems falling asleep? Really, what do you say to a person when his hospital stay exceeds, say, 5 months? Yes, I actually think of topics and issues that I can incorporate into the conversation which will spice up the relationship. New bed sheets, a fresh coat of paint? It would make a good Seinfeld episode, no?

• 12

  Is it me, or is having 2 patients in the same room like being a flight attendant wheeling around the beverage cart? Get one the peanuts, and then the other wants the pretzels. For sure, add 10 minutes to your time in room 728 tomorrow.

• 13

  If a patient is unable to leave the hospital for reasons unrelated to discharge planning (locked out of his house, the child is out of town until the next morning, etc.), why do I feel so naughty when I get off the phone with the MCO medical director after offering explanations? I do not get it. You think that the hospital employs a battery of runners to padlock homes and steal patient's clothing. Who wrote this playbook?

• 14

  I love my consultants. Really, I do. I am not picking on them today. The high points of my day are the exchanges that I have with my subspecialty colleagues. However, the myopia that pervades some sign off notes give me pause. For example, a patient admitted for gastrointestinal bleed, s/p EGD, and stable at 72 hours post arrival receives a consultant note as follows: if patient eating and ambulating, can be discharged home as per PMD. Surely, when the level of transferred oversight shifts to the level of caloric ingestion and sneaker use, well, let us just say that I am all for some new E&M codes. They did not tell me about this in hospitalist school.

• 15

  Don't you love the feeling of your beeper going off, and 30 seconds after the first page, boom, it goes off againboth to the same extension? I mean really, I am a nice guy, but do you really want to rile me up this early in the morning?

• 16

  The nurse pages you from 1 floor away10 seconds from where you are standing. You recognize that number, you knew that it was coming, and for sure, waiting on the other end is that family member who hails from a foreboding place. How quickly does your brain do the computationdo I use the phone and let my fingers do the talking, or make that stroll and have that face‐to‐face summit? No sarcastic comment is needed. I see us now, hands joined, joyfully singing Kumbaya in a loving embrace.

• 17

  Gee, it is not busy today. Say that on the wards and you get a leering glance. However, say that in the emergency room and you will meet your death. There is something about that phrase and the emergency department. The nurses there do not forget, although a Starbucks cappuccino does put a nice salve on the wound.

• 18

  On your day off, do you ever notice that your beeper vibrates on your belt and you are not wearing it? I am not kidding.

• 19

  An irony of life: I have developed an immunity to cigarette smoke in hospital bathrooms. Why is that? It is like peanut butter and jelly. They just seem so happy together.

• 20

  Do you want to transfer that patient to psychiatry? No, no, no, you silly hospitalistdid you not notice that abnormal BUN and atypical lymphocyte on the peripheral smear? Hey, Dr. Freud, can you write me for some of that Prozac too!

• 21

  We need to consult a rulebook on chair ownership. Did you ever notice (a tinge of Andy Rooney here) that case managers own their seats? I know that the world is not quite right when a case manager shoots me that dastardly glare, as if to say, Flee, you silly physician, I live at this station, you are merely my guest! As far as that chair is concerned, perhaps if a small plaque is added to the backrest with a suitable donation, my legs will finally get their deserved daily rest.

• 22

  Finally, do you want to become invisible? Go to the reading board and stand behind a radiologist at 10:30 _AM. Do it long enough, and after a few days, you will be saying I'm Good Enough, I'm Smart Enough, and Doggone It, People Like Me! Do you want to disappear completely? Try it on a Friday.

Okay, okay, I will stop there. It is funny, though; this stuff really happens. Despite the aggravation, I see these commonalities as the glue that binds us, assists in building the esprit de corps in our profession, and adds a little levity to the work place. Outside the hospital (not inside, of course), I can confidently state that these routines are part of who I am. After all, it is all about the knowing glance that I mentioned previously. The humorous part is that we are certainly on someone else's list. Probably a nurse, an emergency room doctor, or maybe a physician's assistant is scribing away at this minute, and we are number 7: Those hospitalists really tick me off.

EPILOGUE

Note to selflook in the mirror occasionally; you might learn something.

I apologize to all my nonhospitalist colleagues if you sneered. I love all of you. Today.

Is hospital medicine a bona fide specialty? Do something long enough, and as Justice Potter Stewart said when defining a certain taboo carnal subject many years ago, I know it when I see it. Although working groups may struggle to conceive a master set of core competencies for hospitalists, I will tell you this: no texts are needed, and you know that you are on to something when 2 hospitalists practicing 3000 miles apart shoot each other a knowing glance and, without words, just understand what the other is thinking. After 10 years of practice in several hospitals, I have had enough mind melds to last a lifetime. Who needs science after all? I mean, how many of us can keep a straight face when asked if we have ever heard this line: Ah, yes, Dr. Flansbaum, umm, I am Dr. Smith from the surgical ICU, and we have a patient hospital day 34 status post Whipple that is no longer surgically active. See, you are smiling already. Do I have to finish the sentence for you?

What follows is a collective experience of things that I call the grind: things so small, so inconsequential, that no one will ever cite them individually as the deal breakers of the day. Collectively, they are the fabric of who we are and that little sore on the inside of our cheek that we just have to touch every few minutes in order to remind ourselves of why dermatologists always look so happy. Any accompanying sage lessons are also free of charge.

• 1

  Why at the end of the day, always on a weekend after you have found a comfortable seat as far away from the nurse's station as possible, do you open up the chart and see 1 line of open space left on the last page of the progress notepaper? Even better, why is the note above it a follow‐up from vascular surgery, written in font size 24, in a form of Sanskrit that not even Steven Hawkings would recognize?

• 2

  Okay, how about this: For patients with loooong lengths of stay, how many creative ways can you write Awaiting placement, afebrile, no complaints (in compliance with billing rules of course)? The correct answer is between 16 and 23. A thesaurus helps for word number 1try stable to startand change your pen from fine point to medium point on odd days. Then add tolerated breakfast on Monday, lunch on Tuesday, and dinner on Wednesday. Voila! Who said this was tough?

• 3

  Okay, this one boggles my mind. You wish to auscultate a set of lungs. The patient is sitting on his gown. You attempt to lift the gown, and instead of raising his tush, the patient continues to sit while you tug away. Is this just me? It happens every week.

• 4

  The medical students are great. They are ambitious and make teaching fun. Why though, at 9:59 _AM, with an upcoming meeting with your chief at 10, are there no PC terminals at the station available except for the MSIII on MySpace.com with the chart you could not find (for 5 minutes) underneath his clipboard? Yes, Virginia, make me remember those days.

• 5

  Clearly, this is one of the more helpful lines you can get from a consult: Patient needs to be medically optimized, and consider head MRI. Consider? Is not that why I called the consult in the first place? Let us consider not to use the word consider any more. Consider that. I feel better.

• 6

  While we are on the topic of consultants, a dollar goes to you if this has never happened during your time on the wards: (1) the consultant visits, (2) the consultant evaluates, (3) you speak with the consultant, (4) all of you agree that the patient can go home, and (5) you then read the consult after the patient is dressed and his IV is out. Umm, a head CT before discharge and please have the neurosurgery clear the patient before discharge? Am I working in a parallel universe? Too much caffeine? Lord, give me strength.

• 7

  Your beeper goes off at 2:57 _PM. At 2:57 _PM plus 5 seconds, you call the number you were paged from and no one answers. Does the word ponderous come to mind? This invariably happens every day, of course, typically when I am in the midst of multitasking 4 conversations. However, I extract some form of perverse cosmic revenge when I need to make a call and pick up an open extension from a ringing multiline phone. Invariably, I click the button to engage a line and, oops, good bye caller. I am only kidding; that never happens (is my nose really growing?). Just think, I could have been the one screaming, Is Laverne here?

• 8

  You get an admission, a patient whom you have never met, and his room is listed as 428. You walk in, and the patient nearest to you is a well‐groomed middle‐age individual with a welcoming smile. The patient next to him is breathing fire, screaming at an imaginary executioner, and claiming that you are the guilty party and need to die. Which patient do you think is yours?

• 9

  Those of you who work with housestaff will appreciate this one (file it under systems issue: fix next week): You have a discussion with a patient regarding his _PM discharge at 11 _AM. You arrange the follow‐up, you review the new medications, you discuss who will pick him up after dinner, and so forth. You get that warm and fuzzy feeling that you have done your job and all is right in the universe. Naturally, you also tell the resident that the patient can go home. Lo and behold, you look at your census the following morning, and the name of the aforementioned patient radiates like a beacon from the screen. You then poke your head into the room, feeling assured that it is merely an error, and the aforementioned patient is lying in bed, smiling and happy to see you. You ask, What happened? The reply, I don't know. Didn't your son come last night to pick you up? The response is yes. After the penetrating ulcer in your stomach bores a little deeper, you discover the official discharge order did not occur, and the patient was content eating chipped beef and sleeping on said contoured mattress 1 more night. Serenity now, serenity now.

• 10

  The fifth vital sign? Is that the new black? Heck, number 5, I think we are up to 11 or 12 these days. Need a new metric installed? You guessed it: add it to the list!

• 11

  Do you ever get LOS fatigue with a particular patient that is so severe you go to bed the previous night and have problems falling asleep? Really, what do you say to a person when his hospital stay exceeds, say, 5 months? Yes, I actually think of topics and issues that I can incorporate into the conversation which will spice up the relationship. New bed sheets, a fresh coat of paint? It would make a good Seinfeld episode, no?

• 12

  Is it me, or is having 2 patients in the same room like being a flight attendant wheeling around the beverage cart? Get one the peanuts, and then the other wants the pretzels. For sure, add 10 minutes to your time in room 728 tomorrow.

• 13

  If a patient is unable to leave the hospital for reasons unrelated to discharge planning (locked out of his house, the child is out of town until the next morning, etc.), why do I feel so naughty when I get off the phone with the MCO medical director after offering explanations? I do not get it. You think that the hospital employs a battery of runners to padlock homes and steal patient's clothing. Who wrote this playbook?

• 14

  I love my consultants. Really, I do. I am not picking on them today. The high points of my day are the exchanges that I have with my subspecialty colleagues. However, the myopia that pervades some sign off notes give me pause. For example, a patient admitted for gastrointestinal bleed, s/p EGD, and stable at 72 hours post arrival receives a consultant note as follows: if patient eating and ambulating, can be discharged home as per PMD. Surely, when the level of transferred oversight shifts to the level of caloric ingestion and sneaker use, well, let us just say that I am all for some new E&M codes. They did not tell me about this in hospitalist school.

• 15

  Don't you love the feeling of your beeper going off, and 30 seconds after the first page, boom, it goes off againboth to the same extension? I mean really, I am a nice guy, but do you really want to rile me up this early in the morning?

• 16

  The nurse pages you from 1 floor away10 seconds from where you are standing. You recognize that number, you knew that it was coming, and for sure, waiting on the other end is that family member who hails from a foreboding place. How quickly does your brain do the computationdo I use the phone and let my fingers do the talking, or make that stroll and have that face‐to‐face summit? No sarcastic comment is needed. I see us now, hands joined, joyfully singing Kumbaya in a loving embrace.

• 17

  Gee, it is not busy today. Say that on the wards and you get a leering glance. However, say that in the emergency room and you will meet your death. There is something about that phrase and the emergency department. The nurses there do not forget, although a Starbucks cappuccino does put a nice salve on the wound.

• 18

  On your day off, do you ever notice that your beeper vibrates on your belt and you are not wearing it? I am not kidding.

• 19

  An irony of life: I have developed an immunity to cigarette smoke in hospital bathrooms. Why is that? It is like peanut butter and jelly. They just seem so happy together.

• 20

  Do you want to transfer that patient to psychiatry? No, no, no, you silly hospitalistdid you not notice that abnormal BUN and atypical lymphocyte on the peripheral smear? Hey, Dr. Freud, can you write me for some of that Prozac too!

• 21

  We need to consult a rulebook on chair ownership. Did you ever notice (a tinge of Andy Rooney here) that case managers own their seats? I know that the world is not quite right when a case manager shoots me that dastardly glare, as if to say, Flee, you silly physician, I live at this station, you are merely my guest! As far as that chair is concerned, perhaps if a small plaque is added to the backrest with a suitable donation, my legs will finally get their deserved daily rest.

• 22

  Finally, do you want to become invisible? Go to the reading board and stand behind a radiologist at 10:30 _AM. Do it long enough, and after a few days, you will be saying I'm Good Enough, I'm Smart Enough, and Doggone It, People Like Me! Do you want to disappear completely? Try it on a Friday.

Okay, okay, I will stop there. It is funny, though; this stuff really happens. Despite the aggravation, I see these commonalities as the glue that binds us, assists in building the esprit de corps in our profession, and adds a little levity to the work place. Outside the hospital (not inside, of course), I can confidently state that these routines are part of who I am. After all, it is all about the knowing glance that I mentioned previously. The humorous part is that we are certainly on someone else's list. Probably a nurse, an emergency room doctor, or maybe a physician's assistant is scribing away at this minute, and we are number 7: Those hospitalists really tick me off.

EPILOGUE

Note to selflook in the mirror occasionally; you might learn something.

I apologize to all my nonhospitalist colleagues if you sneered. I love all of you. Today.

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

• Structured subcutaneous insulin order sets (November, 2003).

• An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2

Figure 3

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 _AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Table 2.Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	179 66	170 65	165 58
Median	160	155	151
Uncontrolled patient‐days*	4,372	7,162	3,465
Monitored patient‐days	11,555	21,135	11,531
% Uncontrolled patient‐days	37.8	33.9	30.1
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.89 (0.87‐0.92)	0.79 (0.77‐0.82)	0.89 (0.86‐0.92)
Glycemic control by patient‐stay
Day‐weighted mean SD	177 57	174 54	170 50
Day‐weighted median	167	162	158
Uncontrolled patient‐stay (%)	1,038	1,696	784
Monitored patient‐stay	2,504	4,515	2,295
% Uncontrolled patient‐stays	41.5	37.6	34.2
RR: uncontrolled patient‐stay (95% confidence interval)		0.91 (0.85‐0.96)	0.84 (0.77‐0.89)	0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Table 3.Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	172 65	169 64	163 57
Median	159	154	149
Uncontrolled patient‐days*	3,469	5,639	2,766
Monitored patient‐days	9,304	17,278	9,671
% Uncontrolled patient‐days	37.3	32.6	28.6
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.87 (0.85‐0.90)	0.77 (0.74‐0.80)	0.88 (0.84‐0.91)
Glycemic control by patient‐stay
Day‐weighted mean SD	175 51	169 47	166 45
Day‐weighted median	167	158	155
Uncontrolled patient‐stay (%)	588	908	425
Monitored patient‐stay	1,439	2,659	1,426
% Uncontrolled patient‐stays	40.1	34.1	29.8
RR: Uncontrolled patient‐stay (95% confidence interval)		0.84 (0.77‐0.91)	0.73 (0.66‐0.81)	0.87 (0.79‐0.96)

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

• Structured subcutaneous insulin order sets (November, 2003).

• An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2

Figure 3

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 _AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Table 2.Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	179 66	170 65	165 58
Median	160	155	151
Uncontrolled patient‐days*	4,372	7,162	3,465
Monitored patient‐days	11,555	21,135	11,531
% Uncontrolled patient‐days	37.8	33.9	30.1
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.89 (0.87‐0.92)	0.79 (0.77‐0.82)	0.89 (0.86‐0.92)
Glycemic control by patient‐stay
Day‐weighted mean SD	177 57	174 54	170 50
Day‐weighted median	167	162	158
Uncontrolled patient‐stay (%)	1,038	1,696	784
Monitored patient‐stay	2,504	4,515	2,295
% Uncontrolled patient‐stays	41.5	37.6	34.2
RR: uncontrolled patient‐stay (95% confidence interval)		0.91 (0.85‐0.96)	0.84 (0.77‐0.89)	0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Table 3.Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	172 65	169 64	163 57
Median	159	154	149
Uncontrolled patient‐days*	3,469	5,639	2,766
Monitored patient‐days	9,304	17,278	9,671
% Uncontrolled patient‐days	37.3	32.6	28.6
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.87 (0.85‐0.90)	0.77 (0.74‐0.80)	0.88 (0.84‐0.91)
Glycemic control by patient‐stay
Day‐weighted mean SD	175 51	169 47	166 45
Day‐weighted median	167	158	155
Uncontrolled patient‐stay (%)	588	908	425
Monitored patient‐stay	1,439	2,659	1,426
% Uncontrolled patient‐stays	40.1	34.1	29.8
RR: Uncontrolled patient‐stay (95% confidence interval)		0.84 (0.77‐0.91)	0.73 (0.66‐0.81)	0.87 (0.79‐0.96)

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

• Structured subcutaneous insulin order sets (November, 2003).

• An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2

Figure 3

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 _AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Table 2.Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	179 66	170 65	165 58
Median	160	155	151
Uncontrolled patient‐days*	4,372	7,162	3,465
Monitored patient‐days	11,555	21,135	11,531
% Uncontrolled patient‐days	37.8	33.9	30.1
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.89 (0.87‐0.92)	0.79 (0.77‐0.82)	0.89 (0.86‐0.92)
Glycemic control by patient‐stay
Day‐weighted mean SD	177 57	174 54	170 50
Day‐weighted median	167	162	158
Uncontrolled patient‐stay (%)	1,038	1,696	784
Monitored patient‐stay	2,504	4,515	2,295
% Uncontrolled patient‐stays	41.5	37.6	34.2
RR: uncontrolled patient‐stay (95% confidence interval)		0.91 (0.85‐0.96)	0.84 (0.77‐0.89)	0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Table 3.Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available

Time Period (TP)	Baseline	TP2 Structured Orders	TP3 Orders Plus Algorithm	Relative Risk TP3:TP2
* An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL. P value of <0.005. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.
Patient‐day glucose
Mean SD	172 65	169 64	163 57
Median	159	154	149
Uncontrolled patient‐days*	3,469	5,639	2,766
Monitored patient‐days	9,304	17,278	9,671
% Uncontrolled patient‐days	37.3	32.6	28.6
RR: uncontrolled patient‐day (95% confidence interval)	1.0	0.87 (0.85‐0.90)	0.77 (0.74‐0.80)	0.88 (0.84‐0.91)
Glycemic control by patient‐stay
Day‐weighted mean SD	175 51	169 47	166 45
Day‐weighted median	167	158	155
Uncontrolled patient‐stay (%)	588	908	425
Monitored patient‐stay	1,439	2,659	1,426
% Uncontrolled patient‐stays	40.1	34.1	29.8
RR: Uncontrolled patient‐stay (95% confidence interval)		0.84 (0.77‐0.91)	0.73 (0.66‐0.81)	0.87 (0.79‐0.96)

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

In Part 1, we reviewed normal sleep architecture, and discussed the numerous factors that often disrupt the sleep of hospitalized medical patients. Effective management of sleep complaints among acutely ill patients includes a thorough assessment of medical and psychiatric conditions, medications and other psychosocial factors that may be directly or indirectly impairing sleep. In Part 2, we review and introduce an algorithm for assessing and managing sleep complaints in acutely ill hospitalized patients.

ASSESSMENT AND EVALUATION OF SLEEP COMPLAINTS

Assessment and evaluation of a sleep complaint begins with (Figure 1) an initial review of the medical record for documentation of the signs and symptoms of an underlying primary sleep disorder, which may be exacerbated during an acute medical illness. Common sleep disorders that are often overlooked include obstructive sleep apnea (OSA), restless leg syndrome (RLS), and periodic limb movement disorder (PLMD). Predisposing factors, characteristic clinical features, and differential diagnoses of these disorders are described in Table 1.

Figure 1

Obtain a focused history by using questions listed in Table 2 to characterize the onset, duration, frequency, and specific characteristics of the patient's current sleep patterns. Next, establish whether the onset of the patient's sleep complaint began with the time of hospitalization. Subsequent questions can then focus on factors that may be impairing sleep such as the hospital environment and sleep hygiene behaviors by comparing the patient's home sleep habits with those during hospitalization. Inquire about the use or abuse of substances such as sedatives, antidepressants, sedatives, antiepileptic drugs (AEDs), and opioids. Ask questions about the presence of pain syndromes and other comorbidities that often impact sleep.

MANAGEMENT OF SLEEP COMPLAINTS

Management of sleep disturbance is multifactorial and consists of nonpharmacologic as well as pharmacologic therapies. A stepwise approach is suggested and begins with nonpharmacologic strategies.

CONCLUSION

Hospitalization is often associated with disrupted sleep, which can affect recovery from illness. Understanding the major factors that impair sleep during hospitalization allows clinicians to systemically evaluate and treat sleep problems. More than just prescribing a sedative/hypnotic, the treatment for sleep disruption includes addressing sleep hygiene and hospital environment issues, identifying medications that could disrupt sleep, and treating specific syndromes that impair sleep. We suggest a practical algorithm to guide clinical assessment, treatment options, and selection of appropriate sleeping medications. Critical to optimizing recovery from illness, sleep may be considered as the sixth vital sign, and should be part of the routine evaluation of every hospitalized patient.

In Part 1, we reviewed normal sleep architecture, and discussed the numerous factors that often disrupt the sleep of hospitalized medical patients. Effective management of sleep complaints among acutely ill patients includes a thorough assessment of medical and psychiatric conditions, medications and other psychosocial factors that may be directly or indirectly impairing sleep. In Part 2, we review and introduce an algorithm for assessing and managing sleep complaints in acutely ill hospitalized patients.

ASSESSMENT AND EVALUATION OF SLEEP COMPLAINTS

Assessment and evaluation of a sleep complaint begins with (Figure 1) an initial review of the medical record for documentation of the signs and symptoms of an underlying primary sleep disorder, which may be exacerbated during an acute medical illness. Common sleep disorders that are often overlooked include obstructive sleep apnea (OSA), restless leg syndrome (RLS), and periodic limb movement disorder (PLMD). Predisposing factors, characteristic clinical features, and differential diagnoses of these disorders are described in Table 1.

Figure 1

Obtain a focused history by using questions listed in Table 2 to characterize the onset, duration, frequency, and specific characteristics of the patient's current sleep patterns. Next, establish whether the onset of the patient's sleep complaint began with the time of hospitalization. Subsequent questions can then focus on factors that may be impairing sleep such as the hospital environment and sleep hygiene behaviors by comparing the patient's home sleep habits with those during hospitalization. Inquire about the use or abuse of substances such as sedatives, antidepressants, sedatives, antiepileptic drugs (AEDs), and opioids. Ask questions about the presence of pain syndromes and other comorbidities that often impact sleep.

MANAGEMENT OF SLEEP COMPLAINTS

Management of sleep disturbance is multifactorial and consists of nonpharmacologic as well as pharmacologic therapies. A stepwise approach is suggested and begins with nonpharmacologic strategies.

CONCLUSION

Hospitalization is often associated with disrupted sleep, which can affect recovery from illness. Understanding the major factors that impair sleep during hospitalization allows clinicians to systemically evaluate and treat sleep problems. More than just prescribing a sedative/hypnotic, the treatment for sleep disruption includes addressing sleep hygiene and hospital environment issues, identifying medications that could disrupt sleep, and treating specific syndromes that impair sleep. We suggest a practical algorithm to guide clinical assessment, treatment options, and selection of appropriate sleeping medications. Critical to optimizing recovery from illness, sleep may be considered as the sixth vital sign, and should be part of the routine evaluation of every hospitalized patient.

In Part 1, we reviewed normal sleep architecture, and discussed the numerous factors that often disrupt the sleep of hospitalized medical patients. Effective management of sleep complaints among acutely ill patients includes a thorough assessment of medical and psychiatric conditions, medications and other psychosocial factors that may be directly or indirectly impairing sleep. In Part 2, we review and introduce an algorithm for assessing and managing sleep complaints in acutely ill hospitalized patients.

ASSESSMENT AND EVALUATION OF SLEEP COMPLAINTS

Assessment and evaluation of a sleep complaint begins with (Figure 1) an initial review of the medical record for documentation of the signs and symptoms of an underlying primary sleep disorder, which may be exacerbated during an acute medical illness. Common sleep disorders that are often overlooked include obstructive sleep apnea (OSA), restless leg syndrome (RLS), and periodic limb movement disorder (PLMD). Predisposing factors, characteristic clinical features, and differential diagnoses of these disorders are described in Table 1.

Figure 1

Obtain a focused history by using questions listed in Table 2 to characterize the onset, duration, frequency, and specific characteristics of the patient's current sleep patterns. Next, establish whether the onset of the patient's sleep complaint began with the time of hospitalization. Subsequent questions can then focus on factors that may be impairing sleep such as the hospital environment and sleep hygiene behaviors by comparing the patient's home sleep habits with those during hospitalization. Inquire about the use or abuse of substances such as sedatives, antidepressants, sedatives, antiepileptic drugs (AEDs), and opioids. Ask questions about the presence of pain syndromes and other comorbidities that often impact sleep.

MANAGEMENT OF SLEEP COMPLAINTS

Management of sleep disturbance is multifactorial and consists of nonpharmacologic as well as pharmacologic therapies. A stepwise approach is suggested and begins with nonpharmacologic strategies.