The large amount of time saved and the convenience for patients who use remote glucose monitoring are obvious. The advantages for providers are threefold. First, it frees up time to see other patients. Second, it increases the frequency of interactions with insulin-requiring patients. Third, the organization of the glucose readings into pre- and post-prandial and before bedtime values (and possibly the scattergram) facilitates decision-making for the provider. For those who utilize the algorithms, the analysis of the values and the recommendations that can be modified or accepted further facilitate the provider’s ability to adjust insulin doses. With the exception of the first advantage, all of the rest should improve glycemia in insulin-requiring patients.
Remote glucose monitoring utilizing computerized insulin dose adjustment algorithms in these insulin-requiring patients lowered HbA1c levels from 10.0% to 8.1% after 3 months and to 7.6% after 6 months (Fig. 2) in this mostly minority, safety net clinic population. An earlier version of these computerized insulin dose adjustment algorithms was used by a registered nurse trained on them who lowered HbA1c levels from 8.4% to 7.7% over 20 weeks in 29 remotely monitored, insulin-requiring patients, 10 of whom had type 1 diabetes . Another registered nurse trained to use these dosing algorithms (non-computerized) and supervised by primary care physicians in a family medicine clinic serving a similar safety net population decreased baseline HbA1c levels in 111 insulin-requiring patients referred to her by these physicians from 11.0% to 7.2% in 9–12 months of clinic visits .
The vast majority of currently available insulin dosing recommendations calculate pre-prandial (bolus) doses based on the estimated carbohydrate (carb) content of the meal and the prevailing glucose concentrations. Besides being difficult to learn, taking up to 6 months to allegedly becoming proficient, carb counting by patients is not that accurate. Compared to a registered dietitian, patients are off by 20–40% [22, 23]. Furthermore, recent studies have shown that the protein and lipid content of a meal could increase a bolus insulin dose based on just the carb content of the meal by up to 65% [24, 25]. A systemic assessment of 46 smartphone apps for calculating bolus doses concluded that the majority of them provided no protection against and may actively have contributed to incorrect or inappropriate dose recommendations .
The use of these clinician-involved computerized insulin dose adjustment algorithms follows a different approach. Although stress on improving dietary and exercise lifestyles should continue, the dosing recommendations are based on analyzing the pattern of glucose values obtained during patients’ current lifestyles. If the lifestyle should improve (or worsen), the glucose patterns would change accordingly and the appropriate changes in insulin doses would be recommended. These computerized insulin dose adjustment algorithms incorporate all approved insulin preparations and cover eight different standard insulin regimens: basal insulin alone, bedtime NPH insulin alone, basal/bolus, self-mixed/split, pre-mixed, U-500 regular, and the unusual delayed response to NPH [27, 28] and U-500 regular  insulins. Additionally, they recommend intensification of insulin when appropriate. For instance, if the glucose values during a particular period are high and there is no insulin preparation in the regimen to cover that period, the recommendations will suggest adding an injection of an appropriate insulin type that will lower those elevated glucose readings.
A crucial reason for the effectiveness of this remote glucose monitoring pilot project is the increased interactions between the patient and clinician so that more frequent insulin dose adjustments can be made. In contrast to only one-third of insulin-requiring patients achieving the ADA HbA1c target of below 7.0% under usual care, 88% reached that target when insulin dose adjustments were made every 1–4 weeks . In addition to increased effectiveness, frequent evaluations for insulin dose changes are necessary to avoid hypoglycemia. Over the course of a year, two-thirds of patients required a more than 20% decrease in their insulin requirements . The period of decreased insulin doses lasted 10 weeks and the total insulin dose was decreased by 41%. The usual frequency of insulin dose adjustments of 2–4 times per year places insulin-requiring patients at an increased risk for hypoglycemia.
Our rate of hypoglycemia was similar to others who intensify insulin treatment. In the AUTONOMY study  in which a basal/bolus regimen was initiated in patients who had failed basal insulin alone, 86–98% of patients experienced at least one episode of hypoglycemia. The yearly rate of hypoglycemic episodes in that study ranged from 38 to 51 per patient per year, compared to our rate of 35. In contrast to the weight gain seen soon after insulin therapy is initiated, our 0.7-kg increase in weight is consistent with the minimal weight gain occurring after the first year of insulin therapy .
If remote glucose monitoring is not feasible, the computerized insulin dose adjustment algorithms can be used at office visits. A program is placed on an office computer, or if possible, into the electronic health record. The glucose meter is downloaded into that computer and within 15 s the same report is generated. An additional feature of this approach is that a “day view” of the glucose readings is available to the clinician who can delete outlier values (after talking with the patient about the reason for them) before the analysis. Although these interactions would usually be less frequent than the remote monitoring ones, and therefore would take longer to bring HbA1c levels to target, there would be large savings of time, especially if front office staff downloaded the glucose meter and presented the report to the clinician. The use of these computerized insulin dose adjustment algorithms at office visits would give the clinician more time to focus on other aspects of patient care in the limited time available.
Although the principles underlying these insulin dose adjustments have been used successfully for many years by mid-level providers, their computerization requires documentation of their safety, i.e., no increased risk of hypoglycemia, as well as their effectiveness. The results of this pilot project attest to their effectiveness. The algorithms limit the amount that each insulin dose can be increased  which protects against excessive hypoglycemia. The slightly decreased number of episodes of hypoglycemia in this pilot project compared to the AUTONOMY study that also intensified insulin treatment attests to their safety. Of course, further studies will be necessary for confirmation.
A limitation of this project is that it will be difficult to extrapolate these results to the general population since it was carried out in a safety net clinic whose challenging population can be difficult to contact and is often reluctant to perform ongoing glucose testing. Many of them did not have smartphones, and of those who did, a number found it difficult to use the combined smartphone and attached glucose meter. Of the 120 patients eligible for enrollment, over half could not be enrolled because of smartphone issues. Of the 47 who were enrolled, only 17 successfully completed the 6-month pilot project (Fig. 1). On the other hand, this pilot project demonstrated that if patients were knowledgeable in using their smartphones, tested routinely, could be contacted, and followed recommended insulin dose changes, HbA1c levels could be markedly lowered in poorly controlled patients. It should be noted that lifestyle education was not emphasized so that any HbA1c changes could be more likely ascribed to this telehealth approach. Any lifestyle improvements would be reflected in subsequent glucose readings and taken into account by the algorithms. Finally, since it was an observational project without a control group, the improved glycemia cannot necessarily be due only to the use of the computerized algorithms. More frequent interactions could have played a role.
There is a compelling need to overcome the clinical inertia that impedes appropriately initiating and intensifying insulin. The reluctance to initiate insulin therapy leads to long delays of up to 7.7 years after patients fail combinations of non-insulin drugs before insulin was started. Even then, many patients did not receive insulin [12, 33,34,35,36,37]. When insulin was started in type 2 diabetic patients, HbA1c levels ranged from 8.9% through 9.8%, averaging 9.3% [33, 34, 37,38,39,40], and was 9.7% when the insulin regimen was intensified [12, 36]. The HbA1c levels of patients receiving insulin ranged from 7.9% through 9.4%, averaging 8.5% [37, 38, 41, 42]. Even though only two patients achieved the ADA HbA1c goal of below 7.0% (data not shown), the mean level of those successfully completing the 6-month pilot project was 0.9% less than the average value in the less challenged general population of insulin-requiring diabetic patients. A telehealth approach is very likely to be effective in dealing with issues involving insulin therapy.
The direct medical cost of diabetes care in the USA in 2017 was 237 billion dollars , a sizeable proportion of which was related to the microvascular complications of diabetes. Since achieving, or nearly reaching, the ADA HbA1c target of below 7.0% prevented the development or markedly slowed the progression of these microvascular complications , use of effective computerized insulin dose adjustment algorithms should decrease the morbidity and mortality related to these complications in insulin-requiring diabetic patients. Furthermore, this improvement in glycemia will save the medical care system large sums. For instance, a 1.0% drop in HbA1c levels is associated with a reduction of at least approximately US$1300 per patient per year in medical care costs. These savings noted in 1997  were adjusted by the yearly consumer price index (CPI) to 2014. Since the rate of increase of medical care costs is more than the CPI, the yearly savings are likely higher. Thus, the use of computerized insulin dose adjustment algorithms would have both clinical and financial benefits.