Overview

A safety net clinic implemented a remote glucose monitoring intervention via computerized algorithms for insulin dose adjustments in order to to help clinicians make dosing decisions for insulin-requiring patients.

Organization Name

Mellitus Health Inc.
Venice Family Clinic

Organization Type

• Community health center
• FQHC

National/Policy Context

• Patients diagnosed with type 2 diabetes diagnosis have 50% reduction in insulin secretion, which continues to decline over time despite ongoing hyperglycemic therapy. While non-insulin drugs can often control type 2 diabetes initially, insulin secretion eventually decreases to the point where non-insulin drugs are insufficient and insulin is necessary.
• Ninety percent of diabetic patients receive care from primary care physicians who are reluctant to start insulin. Even if insulin is initiated, it is often discontinued or fails to be uptitrated. Physicians state a major factor in their reluctance to start and subsequently intensify insulin treatment is due to time constraints and the high volume of patients needed to be treated.
• Two thirds of patients on insulin fail to achieve the American Diabetes Association’s (ADA) target HbA1c level of below 7.0%. Yet clinical trials have shown that if insulin doses are adjusted every 1–4 weeks,  more than 85% of patients can achieve this goal.

Patient Population Served and Payor Information

• This research was completed in Venice Family Clinic, a safety net community clinic with a patient population that is 57% Hispanic or Latino, 14% homeless, and 76% below the federal poverty level.

Leadership

• Mayer B. Davidson, MD. and S. Joshua Davidson were leaders of the project.
• Ligaya Scarlett, NP and Jessica M. Goldberg, MPH assisted in carrying out the project.

Funding

• The personnel needs for this project were funded by the Leonard M. Lipman Charitable Trust.
• Funds for the iHealth meter and strips were provided by Mellitus Health, Inc.

Tools or Products Developed

• Computerized Insulin Dose Adjustment Algorithms: The principles for adjusting insulin doses used by Mayer B. Davidson, MD for over 50 years were made into a defined algorithm and computerized by S. Joshua Davidson and a colleague. This was approved by the FDA.
• iHealth Align Glucose Meter: This glucose meter has an associated mobile application. The mobile application automatically transmits each glucose reading to the account of the user in the iHealth cloud system.

Tech Involved

• Electronic medical record
• Glucometer software
• Telephone

Team Members Involved

• NPs
• Program staff

Workflow Steps

• Patients were given an iHealth Align glucose meter with an associated mobile application that attaches to a smartphone. The staff person who holds an MPH degree had only an initial face-to-face visit with patients to give them the meter and strips and to instruct them how they should be used in conjunction with the smartphones. All other subsequent contact occurred via phone.
• Mellitus Health’s server was notified each time that there was a new reading for that patient. Glucose readings were sent electronically to a designated staff person in the clinic.
• Two to three weeks after the last contact with a patient by a staff person, the server requested the glucose values, along with the date and time that they were measured, from the iHealth server, analyzed the results by the computerized insulin dose adjustment algorithms and sent a report containing recommendations for adjusting the insulin doses to the Nurse Practitioner at the Venice Family Clinic who could accept or modify them.
• Collected data were presented as a scattergram in addition to being listed as pre- and post-prandial and before bedtime values.
• Created reports were printed and shown to an assigned NP who decided if any insulin dose adjustments were necessary and returned it to the staff person.
• Following receipt of the NP’s decision, the staff person called the patient to inform him or her of the new insulin doses after ascertaining whether the patient was taking the doses stated in the report for the previous two to three weeks. The staff person also inquired about suspiciously high or low glucose values identified by the NP that did not seem to fit into the general pattern for a study participant.
  • Occasionally, the NP would change his or her decision on insulin dose adjustments following the staff persons’ conversation with the patient.
• Following conversation with the patient and confirmation with the NP, the staff person entered the new insulin doses into the electronic health record.

Budget Details

• Estimated budget includes cost of:
  • NP 
  • Staff personnel 
  • iHealth Align Glucose Meters 

Where We Are

• This project was completed and had a minimum follow up time of three months and a maximum follow up time of six months.

Outcomes

• Enrollment and follow-up
  • Of the 222 eligible patients for the study, 28 were included for final analyses.
  • 17 patients were followed for six months and the 11 patients were followed for three  months. These 28 study participants generated 268 reports.
• Time utilized for patient management
  • NP time: The NP spent about three minutes reviewing each report for a total of 804 minutes (13.4 hours over 6-months). Patient visits at the Venice Family Clinic are typically slotted for 15 minutes each. If these patients had been seen, these 268 visits would have consumed 67 hours. Remote glucose monitoring with reports sent to the NP therefore allowed patients’ providers 268 clinic visits for other patients at a cost of 13.4 hours of the NP’s time.
  • Staff time: The staff person spent about 45 minutes at the initial visit explaining the project, educating the patient on how to use the combined smartphone/glucose meter, detailing the requirements of glucose testing, and describing the process of contacting the patient. Total time spent for these 28 patients was 17.9 hours. This does not include the time spent on unsuccessful attempts to reach patients.
  • Patient time: The average time for a patient visit at the Venice Family Clinic from registration to leaving the clinic is 1.7 hours (this includes picking up medication from the pharmacy). By not having to come for face-to-face visits at the frequency of the generated reports, these 28 patients saved 456 hours. This does not include the transportation time or waiting to be registered as well as any costs (transportation or child care) or inconveniences to family members or friends.
• HbA1c levels: Baseline HbA1c (± SD) levels of 10.0% ± 1.2 fell 1.9% to 8.1% ± 1.0 at 3 months and another 0.5% to 7.6% ± 0.8 at 6 months (P <10-6).
  • The decrease in HbA1c levels was comparable amongst all three insulin regimens (basal alone, basal/bolus, self-mixed/split). Baseline and final values in the 11 patients on basal insulin alone were 10.0 % and 8.2%, in the 14 patients on basal/bolus insulin were 9.8% and 7.8% and the 3 patients on self-mixed/split insulin were 10.9% and 8.0%, respectively.
  • Thirty six percent of the patients achieved a HbA1c level below 7.5% and 50% achieved below 8.0%.
• Number of generated reports, recommendations, and NP acceptance of recommendations
  • There were 765 recommendations in 268 generated reports. The NP accepted 590 and modified 175 of them. 26 of the modifications occurred when the staff person ascertained that the patient was not taking insulin as recommended, 19 when the patient could not be contacted so that dose adjustment recommendations could not take place, and two when the staff person determined that the patients’ meal times were not recorded accurately. This left 718 recommendations with which the NP agreed in 590 (82%) and disagreed in 128 (18%). The most common reason for disagreement was that the NP thought there were enough readings to make an adjustment while computerized algorithms did not.
• Blood glucose reading frequency: These 28 patients recorded 4671 blood glucose readings averaging 1.2 tests per day.
• Episodes of hypoglycemia: These were defined as blood glucose readings below 70 mg/dl. 415 (9%) of blood glucose readings were in this range. 26 of the patients had at least one episode of hypoglycemia. Averaged over all patients, this translates into 35 episodes per year per patient. There were no episodes of severe hypoglycemia nor visits to an emergency department for hypoglycemia.
  • A small number of patients were responsible for the majority of the hypoglycemic episodes; two patients accounted for 30% and five patients accounted for 52% of hypoglycemic readings.
  • The extrapolated yearly rate of hypoglycemia (35 episodes per patient) was slightly less than the yearly rate in the AUTONOMY Study (38-51 episodes per patient), another trial in which insulin intensification was carried out.
• Patient weight change: Baseline and final weights were available for 26 patients. These patients gained 0.7 kg (82.0 vs 82.7 kg).

Benefits

• This intervention assisted in improving glucose control in poorly controlled insulin-requiring type 2 diabetic patients.
• The intervention is more easily implemented than other methods. The vast majority of currently available insulin dosing recommendations calculate doses based on the estimated carbohydrate content of the meal and the prevailing glucose concentrations. This methodology is difficult to learn, taking up to 6 months to allegedly become proficient. Moreover, carbohydrate counting by patients is not that accurate.
• This intervention is able to handle high levels of insulin regimen complexity, incorporating all approved insulin preparations and eight different standard insulin regimens.
• The dosing recommendations of this intervention 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.
• For patients, there is a large amount of time saved and added convenience via remote glucose monitoring.
• For providers, the intervention frees up time to see other patients, increases the frequency of interactions with insulin-requiring patients, facilitates decision-making for the provider.

Unique Challenges

• Study limitations:
  • It is difficult to extrapolate these results to the general population, as it was carried out in a safety net clinic whose population can be difficult to contact and is often reluctant to perform ongoing glucose testing.
  • A large number of eligible patients were not interested in the study or could not be contacted. Many eligible study participants did not own a smartphone or were unable to use the combined smartphone/iHealth Align glucose meter. The main reasons for dismissal at early stages of project implementation were too few tests or inability to contact the patient.
  • This was an observational project without a control group. The improved glycemia therefore necessarily cannot be due only to the use of the computerized algorithms as factors including more frequent patient-clinician interactions could have played a role.

Sources

• Davidson MB, Davidson SJ. Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project. Diabetes Ther.  2019 Feb 5. doi: 10.1007/s13300-019-0565-y. [Epub ahead of print] PubMed PMID:
  30721451.