Skip to main content
Download PDF

The impact of interventions on appointment and clinical outcomes for individuals with diabetes: a systematic review

BMC Health Services Research volume 15, Article number: 355 (2015) Cite this article

Abstract

Background

Successful diabetes disease management involves routine medical care with individualized patient goals, self-management education and on-going support to reduce complications. Without interventions that facilitate patient scheduling, improve attendance to provider appointments and provide patient information to provider and care team, preventive services cannot begin. This review examines interventions based upon three focus areas: 1) scheduling the patient with their provider; 2) getting the patient to their appointment, and; 3) having patient information integral to their diabetes care available to the provider. This study identifies interventions that improve appointment management and preparation as well as patient clinical and behavioral outcomes.

Methods

A systematic review of the literature was performed using MEDLINE, CINAHL and the Cochrane library. Only articles in English and peer-reviewed articles were chosen. A total of 77 articles were identified that matched the three focus areas of the literature review: 1) on the schedule, 2) to the visit, and 3) patient information. These focus areas were utilized to analyze the literature to determine intervention trends and identify those with improved diabetes clinical and behavioral outcomes.

Results

The articles included in this review were published between 1987 and 2013, with 46 of them published after 2006. Forty-two studies considered only Type 2 diabetes, 4 studies considered only Type 1 diabetes, 15 studies considered both Type 1 and Type 2 diabetes, and 16 studies did not mention the diabetes type. Thirty-five of the 77 studies in the review were randomized controlled studies. Interventions that facilitated scheduling patients involved phone reminders, letter reminders, scheduling when necessary while monitoring patients, and open access scheduling. Interventions used to improve attendance were letter reminders, phone reminders, short message service (SMS) reminders, and financial incentives. Interventions that enabled routine exchange of patient information included web-based programs, phone calls, SMS, mail reminders, decision support systems linked to evidence-based treatment guidelines, registries integrated with electronic medical records, and patient health records.

Conclusions

The literature review showed that simple phone and letter reminders for scheduling or prompting of the date and time of an appointment to more complex web-based multidisciplinary programs with patient self-management can have a positive impact on clinical and behavioral outcomes for diabetes patients. Multifaceted interventions aimed at appointment management and preparation during various phases of the medical outpatient care process improves diabetes disease management.

Peer Review reports

Background

Diabetes is a complex chronic illness with significant health and financial implications. It has risen to epidemic proportions in the United States affecting approximately 26 million individuals in 2010 [1]. Projections reveal that if the current increase in diabetes incidence persists and diabetes mortality remains relatively low, prevalence will increase from the current level of 8.3 to 33 % of the adult population by 2050 [2]. Estimates indicate that the United States spent $218 billion in costs for pre-diabetes and diabetes care in 2007 [3]. The American Diabetes Association (ADA) and Healthy People 2020 propose guidelines and objectives for effective diabetes care management to reduce the incidence and economic burden of diabetes [4, 5]. These objectives purport routine medical care with goals and treatment plans individualized for each patient, self-management education and on-going support to reduce the risk of diabetic complications [4].

According to ADA guidelines, which may vary from year to year based on evidence, people with diabetes should receive diabetes self-management education (DSME) at the time their diabetes is diagnosed and as needed thereafter. HbA1c test should be performed at least 2 times a year. The fasting lipid profile (total cholesterol, LDL, HDL, triglycerides) should be measured at least annually. A routine urinalysis and microalbuminuria test should be performed annually to assess nephropathy. A comprehensive foot exam should be performed every year to identify risk factors for ulcers and amputations. A dilated eye exam is recommended every year. Flu vaccines should be provided annually to all patients with diabetes. Pneumococcal vaccines are recommended for all patients over 2 years old. Self-monitoring of blood glucose (SMBG) should be performed three or more times a day for patients using multiple insulin injections or insulin pump therapy.

The percentage of United States adults with diabetes who received preventive care practices in 2009–2010 were as follows: ever attended diabetes self-management class, 57.4 %; check HbA1c ≥ 2 times a year, 68.5 %; annual foot exam, 67.5 %; annual eye exam, 62.8 %; annual flu vaccine, 50.1 %, and; daily self-monitor of blood glucose, 63.6 % [6]. Many factors including demographic, psychological, social, disease, treatment, provider, organizational, and care delivery related factors contribute to poor adherence [7]. These low levels of preventive care suggest an opportunity to enhance adherence to guidelines for effective disease management through appointment management and preparation because before diabetes preventive care practices can be instituted, patients must first be scheduled for and attend their provider appointments. Therefore, this study focuses on organizational and care delivery system related factors that relate to appointment management, as well as regular monitoring of relevant patient information integral to disease management.

Routine medical care starts with scheduling the patient with the provider for preventive care services. The patient can be scheduled for the next visit immediately after a provider visit or at a later time when the patient requests an appointment by phone or electronically. Interventions that proactively schedule the patient with their provider are a necessity for timely treatment decisions. Once patients are scheduled for their provider appointments the next step is to ensure that they attend their appointments. Studies show that no-show rates for diabetic patients vary from 4 to 40 % [8]. Literature also indicates that diabetic patients with higher no-show rates have poorer outcomes e.g., higher glycosylated hemoglobin (HbA1c) levels and poorer glycemic control than patients who attend appointments [8]. Without interventions to encourage patients to schedule and attend their provider appointments, other multifactorial interventions to reduce diabetes complications and costs of care cannot be initiated.

Research indicates that diabetes patients actively involved in their self-management experience improved Quality of Life (QOL) and improved HbA1c levels [9, 10]. Currently, most diabetes care is provided in primary care practices. Accomplishing diabetes care objectives during fifteen to twenty minute appointments can be challenging for primary care providers. A provider cannot prepare individualized patient care without important patient information regarding self-monitoring blood glucoses (SMBG), daily diet and nutrition, exercise or physical activity, and medication information and compliance. To aid in the process of effective disease management, patients must take an informed and active role in the process. Interventions that aid the patient in communicating this information to the provider would expedite patient care delivery and allow the provider more time for individualization of the patient’s treatment plan and patient support in self-management.

Literature examining interventions in diabetes care is extensive and offers a wide variability in types of interventions ranging from medication to web-based self-management tools with varying impact on diabetes outcomes. Different from the earlier literature reviews, the purpose of this literature review is to evaluate interventions that apply to appointment management and preparation, and determine their impact on appointment, clinical and behavioral outcomes for diabetic patients. This review examines interventions based upon three focus areas: 1) scheduling the patient with their provider; 2) getting the patient to their appointment, and; 3) having patient information integral to their diabetes care available to the provider. The hypothesis of this study is that interventions, which improve appointment management and preparation, are significantly associated with favorable appointment, clinical and behavioral outcomes.

Methods

Data source

This literature review was completed in February 2014. MEDLINE, the PubMed interface, was the primary database utilized. The following combination of MeSH terms was used for the search: “Diabetes Mellitus”[Mesh] AND (“Intervention Studies”[Mesh] OR “Internet”[Mesh] OR “Reminder Systems”[Mesh] OR “Appointments and Schedules”[Mesh] OR “Patient-Centered Care”[Mesh] OR “Registries”[Mesh] OR “Guideline Adherence”[Mesh]) NOT (“Diabetes, Gestational”[Mesh] OR “Pharmacological Processes”[Mesh] OR “Pharmacological Phenomena”[Mesh] OR “Transplantation”[Mesh] OR “Cardiovascular Surgical Procedures” [Mesh] OR “Heart Diseases”[Mesh] OR “Incidence”[Mesh]). Additionally, the reference lists of included articles and literature reviews were also examined for additional relevant articles. We searched CINAHL and found no additional articles. The Cochrane database was also searched and did not reveal other systematic reviews on this topic.

The search inclusion criteria for the intervention articles were: 1) outpatient diabetes mellitus; 2) adults; and 3) English. The search exclusion criteria eliminated the following types of articles: 1) gestational diabetes; 2) pharmacological processes and phenomena; 3) transplantation (surgery); 4) cardiovascular surgical procedures; 5) heart diseases; and 6) incidence.

Data extraction

The comprehensive literature search generated 4111 articles (See Fig. 1). Studies excluding gestational, pharmacological process, pharmacological phenomena, transplantation, cardiovascular procedures, heart diseases and incidence reduced potential relevant articles to 2810. Articles were limited to those involving adults (19+ per PubMed), written in English and containing an abstract, which further reduced the total to 1308. Two reviewers reviewed the abstracts independently. All possible articles that could not be excluded were recorded in a table. Each study was marked as “relevant”, “not relevant”, or “maybe” based on the provided information in the paper and the goals for this systematic review. Once the reviewers prepared the tables independently, the decisions were compared and discussed in a meeting. Disagreement regarding inclusion of the article was reconciled through discussion with all other authors. Finally, by excluding articles that were not related to evaluation of an implemented intervention, the sample was reduced to 211 articles. Full texts of the 211 articles were retrieved and outcomes were evaluated independently by two reviewers according to structural, process, and outcomes measures [11]. One hundred and thirty four articles were excluded because they did not relate to the three focus areas: 1) on the schedule, 2) to the visit, and 3) patient information; the remaining 77 articles were included in this literature review. Disagreements regarding interpretation of data extracted from articles were reconciled through discussion with the authors. However, description of the types of interventions and outcomes were summarized and trended.

Fig. 1
figure 1

PRISMA flow chart of article selection process

Full size image

Results

The articles included in this review were published between 1987 and 2013, with 46 of them published after 2006. The following is a list of countries and the number of studies from that country included in the review: United States (43); South Korea (15); Netherlands (4); United Kingdom (3); Canada (3); Australia (2); France (1); Finland (1); Iran (1); Italy (1); Norway (1); Taiwan (1) and; Turkey (1). Thirty-five of the 77 studies in the review were randomized controlled studies.

Table 1 is a summary of study designs and interventions used in each article included in this literature review. Appendix 1 provides detailed information about the interventions that focus on three areas of diabetes outpatient care delivery system: 1) scheduling the patient with their provider; 2) getting the patient to their appointment, and; 3) having patient information integral to their diabetes care available to the provider.

Table 1 Summary of study designs and interventions
Full size table

The reviewed articles evaluated the impact of interventions on several outcome measures. We divided the outcome measures into two types: clinical outcomes and behavioral outcomes. Clinical outcomes include the value of laboratory test results such as HbA1c, LDL, HDL, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, triglycerides, fasting plasma glucose, creatinine, 2-hour post meal glucose, and the value of clinical measures such as weight and body mass index (BMI). Given the importance of HbA1c in diabetes care, Table 2 includes only HbA1c results. All other clinical outcomes are provided in Appendix 2. In Table 2 and Appendix 2, we present the difference between the clinical outcome value at baseline and after the intervention (e.g., HbA1c at baseline – HbA1c at m months after the intervention) for both intervention and control groups. Where available, the p-values are presented for the difference between groups and the difference within the groups.

Table 2 Changes in HbA1c
Full size table

The behavioral outcomes, summarized in Appendix 3, include measures related to self-management (SMBG testing, physical activity, foot care, diet, nutrition, self-efficacy, quality of life, and patient satisfaction), attendance to outpatient visits for laboratory tests, vaccinations, primary care and specialty care, adherence to ADA guidelines (annual foot exam, annual eye exam, and processes of care), and acute care utilization (emergency visits, and hospital admissions). Since different measures or tools are used in different studies, we did not provide the numerical values for the changes in outcomes. For example, patient satisfaction is measured using different survey tools. The attendance to laboratory visits are measured using the number of laboratory tests within the next 6 months or 12 months after the intervention, or the percentage of patients who had the recommended laboratory tests within a year. For adherence to recommended laboratory tests, we included the tests considered in that study, and for vaccinations we presented the vaccinations.

In Tables 3, 4, and 5, we summarize the primary outcomes from the studies in Table 2, Appendix 2, and Appendix 3, based on if the interventions were directed at getting patients on the schedule, to the visit, or with the necessary patient information, respectively. Reference numbers of studies with significant outcome findings are bolded. In the following sections, we describe the most notable findings from these studies.

Table 3 Summary of outcomes and statistically significant results relating to getting patients on the schedule
Full size table
Table 4 Summary of outcomes and statistically significant results relating to getting patients to the visit
Full size table
Table 5 Summary of outcomes and statistically significant results relating to collecting patient information
Full size table

On the schedule

For the purpose of this literature review, an intervention that enables a patient to schedule a provider appointment or laboratory test meets criteria for ‘on the schedule’. Review of the literature found limited research studying scheduling interventions as compared to diabetes intervention research pertaining to communication of patient information to the provider. The scheduling interventions, summarized in Table 3, included sending reminders to schedule a provider appointment or laboratory test, scheduling when necessary while monitoring patient information, and open access scheduling to provide same-day access. Although phone reminders were found to be effective for the most part to increase patient attended appointments, impact on clinic outcomes, as with other interventions in this focus group, were mixed and only a few studies discussed proactive appointment scheduling or management.

Grassroots interventions such as letter and phone reminders have been used to remind diabetic patients to schedule a provider appointment or a laboratory test. While the letter reminder, which asked the patients to call and make an appointment, improve the clinical outcomes including HbA1c, and SBP significantly in one study [12], it was not very effective in improving the clinical outcomes in other studies [13]. In a RCT, a letter from the provider was mailed to patients prior to their birthday with a self-care handbook, preventive care checklist and recommendations for routine monitoring and screening resulting in a significantly increased percentage of patients with an HbA1c test, percentage of patients with one diabetes-related provider visit, and percentage of patients with an eye exam within 6 or 12 months after the intervention [14]. In another RCT, patients receiving a phone reminder to schedule an appointment 10 days following a letter reminder had significantly higher return rates for an annual follow-up eye exam than those patients who received only a reminder letter [15]. In a pretest/posttest study, phone calls made by medical assistants to schedule follow-up appointments with the primary care provider significantly improved glycemic control (reduced HbA1c levels) for the patients who returned for their follow-up visit [16]. In another study using RCT, phone calls to schedule an appointment with a pharmacist approximately one week prior to the physician appointment significantly improved compliance to ADA standards of care including percentage of patients who had A1c test, fasting lipid profile, foot exam and vaccinations [17]. An automated outreach call to non-adherent patients advising them to schedule an appointment significantly improved the percentage of patients with a provider visit and with HbA1c test for those patients who were successfully reached [18]. In a multi-center cross-sectional study, a phone call to reschedule after a no-showed appointment for a periodic provider visit resulted in significantly increased patient attendance to annual provider review, and those patients who attended their annual review had significantly lower fasting blood glucose [19].

Different than the studies that consider reminders to patients only, one study combined reminders to the patient with reminders to the provider [20]. In a RCT, faxed reminders were sent to the provider for patient overdue laboratory tests and letter reminders were sent to the patients with a warning of overdue laboratory tests. Even though the decrease in HbA1c and LDL of the intervention group when compared to control group was not significant, the number of emergency visits and number of hospital days per year were reduced significantly [20].

Comprehensive diabetes management programs that are used to monitor patient status can also be used to facilitate scheduling of patients for their provider visits. In a retrospective cohort study, the care coordinator regularly reviewed patient uploaded information such as SMBGs and scheduled provider appointments when appropriate, resulting in significantly decreased percentage of patients with at least one emergency visit and hospital admission [21]. In another RCT, a nurse reviewed self-management by phone at regular intervals, and a multidisciplinary care team provided both group visits every month for 6 months and individual visits after patient self-referral or referral by another care team member. The HbA1c levels and number of hospital admissions significantly reduced for the intervention group [22].

Open access, a scheduling strategy that offers same-day appointments for patients, can aide patients in scheduling a provider appointment and needed laboratory testing [23]. A drawback with this type of scheduling strategy is that the patient has the responsibility to initiate the next appointment at the appropriate time as specified in diabetes practice guidelines. If the patient forgets the timing of laboratory tests and provider visits, and the clinic does not send reminders to the patient for scheduling their appointments, open access scheduling might reduce compliance to diabetes management guidelines. One retrospective cohort study showed that open access scheduling was associated with significant decrease in HbA1c and urine microalbumin testing [23]. Even though HbA1c levels, and the number ED visits and hospitalizations did not change significantly with open access scheduling, the study suggested that scheduling process should be adjusted for patients with diabetes to improve diabetes processes of care (HbA1c, LDL, urine microalbumin testing) [23].

To the visit

Attendance to provider appointments and laboratory testing is a necessary component for implementation of diabetes preventive care. Interventions facilitating patient attendance to the scheduled provider appointments or laboratory testing meet criteria for the focus area ‘to the visit’. Review of the literature found fewer studies discussing interventions to facilitate getting the patients to their provider visits as compared to diabetes intervention research pertaining to communication of patient information to the provider. The interventions that are used to improve attendance to the scheduled visits include letter, phone call, and SMS reminders, and financial incentives, as summarized in Table 4. Phone and mail reminders were the interventions most studied to facilitate patient appointment attendance with positive clinical outcomes. More studies are needed to determine if SMS and web-based appointment reminders and financial incentives can also improve provider visit attendance.

Our literature review showed that letter reminders to patients regarding lab appointment information were associated with significantly increased average number of HbA1c tests within the study period, number of patients who had HbA1c test within 6 months, and percentage of patients who completed the HbA1c test within a certain period after the reminder [24–26]. In a RCT, letters recommending appropriate laboratory testing were automatically mailed quarterly to patients without HbA1c tests in the last six months or without LDL within the last twelve months resulting in significantly increased number of patients who had HbA1c test within 6 months and LDL test within 12 months [26]. Letter reminders one week before the scheduled provider appointment significantly increased the number of provider visits and reduced the number of hospitalizations in another RCT [27].

Phone reminders to patients regarding provider visits and laboratory testing resulted in improved HbA1c levels [16, 28–31]. One study showed that monthly phone reminders to patients in the intervention group regarding laboratory or provider scheduled appointments resulted in significantly decreased HbA1c levels and systolic blood pressure in the intervention group when compared to the control group [28]. Two studies where the medical assistant or the secretary called each patient before their scheduled appointment day to remind them of the appointment were associated with significantly decreased HbA1c [16, 29] and LDL levels [29]. In two studies using a controlled, non-randomized before/after design, a Diabetes Support Service (DSS) called patients in the intervention group to remind them of scheduled appointments for laboratory testing, foot exam, fundus photography and scheduled appointments with the dietician and diabetes nurse. The intervention was associated with a significantly increased percentage of patients with at least four HbA1c tests a year [30] and significantly lower HbA1c levels in the intervention group when compared to the control group [30, 31].

Letter reminders combined with phone reminders of the date and time of the patient’s provider appointment or laboratory test resulted in improved health outcomes [25, 27, 32]. One study showed that a recall card system and phone call reminding patients of their scheduled follow-up appointment resulted in significantly increased the percentage of patients who had HbA1c within the last 6 months and LDL tests within the last 12 months, significantly decreased the percentage of patients hospitalized in the last 12 months, and significantly increased the percentage of patients with foot exams and eye exams in the last 12 months [32].

Web-based programs associated with self-management can successfully remind patients regarding provider appointments or laboratory testing. A RCT used a web-based system to improve self-management education, and used emails combined with short message service (SMS) to send reminders one week before the follow-up visit, and to remind the time of the HbA1c test if it is more than three months overdue [33]. This web-based education management system combined with email and SMS reminders resulted in significantly decreased HbA1c and total cholesterol levels in the intervention group compared to control group [33].

Financial incentives used with other interventions have the potential to improve attendance to scheduled visits or needed lab tests. In a quasi-experimental study, a reminder letter was sent to patients for the completion of lab tests, and were offered and provided a gas card when the tests were completed [24]. The study showed that the reminder letter combined with a financial incentive increased the number of HbA1c tests significantly [24]. In another study, structured group visits facilitated by a diabetes educator were used as the main intervention [34]. A $10-incentive was provided to the patients for each group visit they attended [34]. Group visits combined with financial incentive achieved an overall attendance of 78.4 % to group visits, and significantly reduced SBP levels and number of ED visits per year [34].

Patient information

ADA, Healthy People 2020 and the Chronic Care Model recognize the primary importance and responsibility of the patient in self-managing their diabetes care and collaborating with their providers to set treatment and goals for improved health outcomes [4, 5, 35]. Interventions that aide the patient in communicating important information regarding SMBGs, daily diet and nutrition, exercise or physical activity, medication information and compliance, and patients’ needs to their provider or health care team meet conditions for the focus area ‘with patient information’ (see summary of interventions and findings in Table 5). This focus area of the literature review provided the greatest number of research studies when compared to the other two focus areas, ‘on the schedule’ or ‘to the visit’. Systems with routine monitoring of patient information, managing patient medications and supporting patients’ goals whether web-based, SMS, or Electronic Health Record (EHR) with interfaced registry, consistently showed improved patient clinical outcomes.

This literature review identified multiple studies using web-based diabetes management interventions with care manager feedback. In a RCT study, patients entered SMBG readings, exercise amounts, weight changes, blood pressure, and medication data via a web portal [36]. The study nurse monitored self-management changes, and contacted patients using email or chat to make recommendations [36]. The intervention resulted in significantly decreased HbA1c, systolic blood pressure and total cholesterol levels in the intervention group as compared to the control group who visited their provider for usual care [36]. In another RCT study, a nurse contacted patients biweekly for a 30 min video conference to review biometric data uploaded to the web-based self-management module and discuss patients’ problems in managing the disease [37]. The intervention significantly decreased HbA1c levels in the intervention group [37]. Another study, which used randomized, single-centered, controlled trial with parallel group design, evaluated a web-based program used by patients to review their online medical records, upload their SMBG levels, enter information about their exercise, diet and medication, and send secure emails to the care manager [38]. The care manager reviewed SMBG readings, guided health behavior, adjusted medications, and responded to patients’ messages [38]. This web-based program, which provided ongoing tracking and documentation of patients’ needs and care, decreased HbA1c levels significantly [38]. Seven studies combined web-based diabetes management program with SMS and were associated with significantly decreased HbA1c levels for the intervention group after implementation [39–45]. In six of those studies using quasi-experimental pretest/posttest method conducted by the same research group, the nurse researcher reviewed uploaded patient data on the website, integrated patient clinical information into the patients’ EHRs, provided education for self-management and sent weekly medication adjustment advice to the patient via SMS and internet [39–44].

Two studies showed that patients using a telephone data line to answer care coordinator’s questions regarding daily SMBG readings, medication compliance and symptoms which were forwarded to the provider were associated with significantly increased quality of life (QOL) [46] and significantly decreased the percentage of patients with emergency visits and hospital admissions [21]. One study showed that patients receiving bi-weekly phone calls to review glucose and blood pressure readings had significantly reduced HbA1c and SBP levels [47]. Another study showed weekly phone coaching for goal setting and self-management significantly improved self-efficacy, diet, exercise, and foot care [48].

This literature review showed that the tools enabling decision support at the time of patient contact could improve compliance with preventive care services. A disease management application, which displayed trended electronic laboratory data linked to evidence-based treatment recommendations, resulted in significantly increased average number of HbA1c and LDL tests per year in a RCT study [49]. Patient data entered into Personal Digital Assistant (PDA), which enabled the tracking of evidence-based guidelines and provided reminders of due or overdue tests to providers at each patient visit, improved compliance to eye and foot exams [50]. The Diabetes Questionnaire and Reminder sheet, which is completed by the patient at check-in and reminded providers to check feet and update diabetes care flow chart used to document dates of preventive services in patient’s chart, increased the number of HbAc1 tests, and compliance to eye and foot exams [51].

The utilization of an EHR driven diabetes registry within an integrated delivery system can improve diabetes health outcomes. A multicenter cross-sectional study showed that a computerized registration with templates for recording patient data from quarterly or annual diabetes visits integrated with patient’s EHR resulted in significantly increased percentage of patients with HbA1c tests, and significantly decreased HbA1c, total cholesterol and triglycerides levels [19]. In a RCT study, a laboratory-based registry was used to fax and/or mail laboratory results, reminders of overdue laboratory tests, and quarterly population reports to providers, and to mail reminders for overdue tests and alerts for elevated test results to patients [20]. The integration of registry with patient and provider decision support decreased acute care utilization significantly, but did not decrease HbA1c level significantly [20]. A diabetes registry can be used to generate provider performance audits or provider patient panel reports to provide feedback regarding achievement of diabetes care guidelines including HbAc < 7.0 %. In three studies, these reports were shown to be associated with significantly improved diabetes processes of care (percentage of patients who had HbA1c test in the last six months, annual LDL cholesterol test, annual dilated eye exam, annual foot exam, and annual influenza vaccine) [26, 52, 53].

Personal patient held records summarizing goals, medical and laboratory outcomes for the year can assist both patients and providers as they organize individualized diabetes treatment plans. One study using clustered RCT showed that the intervention group utilizing patient-held health records resulted in significantly decreased HbA1c levels in the intervention group as compared to the control group [54]. However, web-based personal health records that allowed patients to review their medication lists, most recent test results and current treatments before the visit did not improve HbA1c levels in another RCT study [55].

Discussion

ADA and Healthy People 2020 recommended diabetic patients have routine laboratory tests and provider visits at regular intervals [4, 5]. This literature review evaluated diabetes interventions, their effectiveness and resultant health outcomes, focusing upon the areas of scheduling the patient, getting the patient to their provider visit, and having patient information available to the provider. Figure 2 summarizes our findings by illustrating patient flow through the complex medical outpatient care delivery process with all potential interventions identified in this review. More specifically, Fig. 2 shows various components of diabetes outpatient care delivery, identifies phases of the process when interventions could be applied, identifies potential types of multifaceted interventions that could be utilized, and distinguishes whose responsibility it is for successful navigation through each phase of the care delivery system, e.g., provider and health care team versus patient.

Fig. 2
figure 2

Diabetes outpatient care delivery process

Full size image

Identifying gaps and highlighting future research opportunities

Diabetes management requires continuous monitoring and routine provider visits and laboratory tests [4]. This literature review showed that routine visits are either scheduled in advance or reminders are sent to patients to schedule their next appointment. When appointments are scheduled in advance, the attendance to scheduled visits might decrease as the lead time between the time the appointment is scheduled and the actual appointment time increases [56]. Therefore, advanced scheduling should be integrated with other interventions used to improve attendance to scheduled visits. In addition, clinics are moving from advanced scheduling to open access scheduling to reduce waiting times and improve access to care. However, one study showed that open access scheduling negatively affected the process outcomes for diabetes patients [23]. The mixed findings demonstrate the importance of provider or care team initiated interventions such as reminders sent to patients to schedule an appointment, or monitoring of patient information and scheduling when needed. The literature review showed that implementing automated or personalized phone reminders, which are relatively simple interventions and easy to employ by provider practices, are very useful in improving appointment making and attendance behavior.

Web-based diabetes management tools are used to continuously monitor patient information and provide feedback to the patient. The continuously monitored patient information might include SMBG readings, patient medication use, blood pressure, weight, and nutrition or daily calorie intake. The degree of interaction with patients might range from providing feedback about SMBG readings by care manager to online coaching and structured counseling by diabetes specialist or nurse practitioner. The web-based systems can also be used to integrate laboratory testing and clinical information into patient’s EHR. Web-based tools require patient, provider and care team involvement. Although a few of the studies discussed ease of use of the web-based interventions by patients and review patient satisfaction [46, 57, 58], none of the studies in this literature review discussed the ease of use for providers, provider satisfaction, or impact to the provider workload. Most of the web-based interventions using care manager monitoring and feedback used small sample sizes and did not discuss the direct and indirect costs and ease of implementation of the interventions for larger populations. More studies discussing provider workload and information regarding costs of the intervention may aid a practice in determining which inventions are most suited for their practice.

Selective financial incentives can improve quality of health services [59]. Three of the studies in this literature review incorporated financial incentives within the intervention. In one study, a gas card was given to patients after the completion of laboratory tests (HbA1c and LDL) and was associated with significantly increased laboratory testing when combined with a written reminder [24]. Another study discussed monetary incentives to providers for improving diabetes processes of care as demonstrated by significant increases in the percentage of patients with ideal glucose levels (HbA1c < 7.0 %) when combined with provider feedback and computerized reminders [53]. More research is necessary to determine the effect of both patient and provider financial incentives on patient health outcomes.

Tailoring the interventions according to patient population characteristics, needs, capabilities, and skills is an important factor that should be considered while choosing the set of interventions for implementation. For example, a web-based self-management program may not be as appropriate for elderly patients who may not be as comfortable with computer usage as a younger patient. The patients who have cellphones may not answer phone calls, but respond to SMSs. With the increasing use of smartphones, the patients might have regular access to email. However, underserved populations may not even have a regular phone or minutes to answer phone calls/SMSs in their cellphones. Changing technology and patient preferences with regard to contact/communication should be considered when determining the future interventions to improve usage and effectiveness. Research evaluating the usage of interventions tailored for different patient groups is needed. Web-based tools with continuous monitoring can be used to categorize patients according to risk groups. Structured counseling and proactive scheduling of provider appointments might be used for high-risk patients to reduce the acute care utilization.

This literature review identified several interventions that improve appointment management and preparation. While impact of interventions on several clinical and behavioral outcomes is evaluated in these studies, effectiveness of interventions is not evaluated from a systems perspective. In other words, the interventions in the literature we reviewed appeared to be examined in isolation when they may, in fact, have repercussions throughout a provider’s practice and patient population. Other factors such as ease of use by patients and providers, applicability of the intervention for larger populations and across other chronic diseases, and the cost of implementation are important concerns that may influence providers’ decisions about adopting interventions in their practices. Research is needed that includes a more systematic view of the interventions and their implications beyond patient outcomes.

The methodologies used in the reviewed papers vary widely (including RCTs, quasi-experimental, pretest-posttest, retrospective cohort, non-randomized controlled trial, nested randomized trial, etc.). Even though RCT is considered as the best method in terms of strength and validity of the results, the reviewed studies that use other methods usually consider an intervention that can easily be implemented in large patient populations. These interventions include phone, letter/mail and SMS reminders to schedule an appointment or remind a scheduled appointment, and diabetes registries, and decision support systems to improve compliance to diabetes management guidelines. Since these interventions use large sample sizes, the included studies prove the applicability and impact of these interventions. For the studies that consider using a web-based system with care coordinator feedback, RCTs are used with smaller sample sizes. Even though RCTs show the positive impact of such kind of an intervention, the small sample size might be an indicator of the difficulty of implementation due to the cost of the intervention.

Limitations

While the search in this literature review was conducted using several key databases and references were cross-checked, there may be publications not incorporated in the review because of the MESH terms used and inclusion criteria utilized. Only studies published in English were included which may create a chance for potential bias. All studies included in the literature review were peer-reviewed publications. Although some interventions may be dated due to inclusion of studies published as early as 1987, less sophisticated interventions may have the same or better payoff and achieve similar goals at less cost and complexity in implementation.

One limitation of this literature review is that a meta-analysis was not performed due to inconsistency of the reported outcomes [60]. The included studies report a wide range of outcomes. Especially, the behavioral outcomes in Appendix 3, are not consistent. The measures related to self-management use different survey tools to assess patient satisfaction, quality of life, self-efficacy, etc. Other measures such as lab tests completed, vaccinations, provider visits, hospitalizations, and ED visits, are reported as either percentages or numbers (i.e. “percentage of patients who had ED visits” vs. “number of ED visits”). For clinical outcomes, the studies might report time effect, group effect, or time × group effect, which is again not consistent from study to study. Some studies did not use a control group or did not provide enough information before or after the intervention. This inconsistent reporting of wide variety of outcomes, and limited number of studies representing each outcome made the meta-analysis impractical.

Conclusions

The literature review showed that interventions from the simplest phone and letter reminder for scheduling or prompting of the date and time of an appointment to more complex web-based multidisciplinary programs with patient self-management can have a positive impact on clinical and behavioral outcomes for diabetes patients. Multifaceted interventions aimed at appointment management and preparation during various phases of the medical outpatient care process may provide a fail-safe against diabetes patients falling through the cracks of a fissured health care delivery system and maximize patient-provider limited time while obtaining the best possible disease management. While the overall results from this review suggest that interventions associated with appointment management and preparation result in better patient outcomes, an overwhelming absence of financial information in the reviewed studies may inhibit implementation. Indeed, practices may see an increase in costs associated with dedicated care managers and information technology support. Patients, and their insurers, may see an overall decrease in the costs of care when proper disease management is practiced. Unfortunately, these cost offsets may not be within the same cost center, and therefore, the providers paying for the interventions may not realize the cost benefits of enhanced patient well being. Future research must address these cost concerns and new policies may be necessary to ensure that interventions are beneficial for patients and providers.

This literature review also revealed that the trend of diabetes care is moving toward frequent monitoring of patient data and fluid management of patient diabetes care. Complex web-based systems are being overseen by an intermediate care manager, which may be an advance practice nurse, physician assistant or diabetes educator for 1) monitoring of SMBG levels, laboratory tests, medication compliance, diet and nutrition, physical activity and, 2) directing changes in patient care based on patient information. This intermediate care manager also directs the flow of patient information to provider, specialist and other members of the multidisciplinary health care team. The questions are whether the future of diabetes care and this type of continual monitoring will concentrate provider visits more toward those patients whose diabetes are not well-controlled or have a higher severity and what impact this change will have on overall diabetes outcomes. It seems reasonable that with the predicted increases in diabetes incidence and the already overloaded provider schedules that new strategies are needed to ensure access to care for all diabetes patients. Such strategies must include technical innovation that moves beyond the clinic visit, including continuous monitoring and risk assessment using emerging sensor technologies and smart algorithms, (semi) automated selection, execution, and tracking of interventions, learning algorithms to customize patient care plans, and gamification strategies to motivate and engage patient behaviors. Further, comprehensive cost-benefit analysis must become more widely accepted and practiced. The short and long term costs of interventions (capital, operational, maintenance, cyberinfrastructure, etc.) must be balanced against expected benefits from all stakeholder perspectives including patient access, outcomes, and satisfaction, clinic performance and provider utilization, inpatient usage patterns, reimbursement policies, and overall sustainability of the healthcare system. These strategies must be part of the larger, on-going efforts to transform healthcare delivery from being an uncoordinated assortment of specialties and special interests, supported by fee for service, to an integrated and holistic system that provides value to patients through prevention, early diagnosis, avoidance of chronic complications, and excellent therapy.

References

  1. Centers for Disease Control and Prevention. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: U.S. Department of Health and Human Services; 2011.

    Google Scholar 

  2. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metrics. 2010;8:29.

    Article  Google Scholar 

  3. Dall TM, Zhang Y, Chen YJ, Quick WW, Yang WG, Fogli J. The economic burden of diabetes. Health Aff (Millwood). 2010;29(2):297–303.

    Article  Google Scholar 

  4. American Diabetes Association (ADA). Standards of medical care in diabetes--2012. Diabetes Care. 2012;35 Suppl 1:S11–63.

    Google Scholar 

  5. Department of Health and Human Services. Healthy People 2020: goals and objectives-diabetes. Washington, DC: US Department of Health and Human Services; 2012.

    Google Scholar 

  6. Centers for Disease Control and Prevention. Diabetes report card 2012. Atlanta, GA: U.S. Department of Health and Human Services; 2012.

    Google Scholar 

  7. Delamater AM. Improving patient adherence. Clin Diabetes. 2006;24(2):71–7.

    Article  Google Scholar 

  8. Turkcan A, Nuti L, DeLaurentis PC, Tian Z, Daggy J, Zhang L, et al. No-show Modeling for Adult Ambulatory Clinics. In: Healthcare Operations Management: A Handbook of Methods and Applications. edn. Edited by Denton B. New York: Springer; 2013.

  9. Cochran J, Conn VS. Meta-analysis of quality of life outcomes following diabetes self-management training. Diabetes Educ. 2008;34(5):815–23.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM. Self-management education for adults with type 2 diabetes: a meta-analysis of the effect on glycemic control. Diabetes Care. 2002;25(7):1159–71.

    Article  PubMed  Google Scholar 

  11. Donabedian A. The quality of care. How can it be assessed? JAMA. 1988;260(12):1743–8.

    Article  CAS  PubMed  Google Scholar 

  12. Kirsh S, Watts S, Pascuzzi K, O’Day ME, Davidson D, Strauss G, et al. Shared medical appointments based on the chronic care model: a quality improvement project to address the challenges of patients with diabetes with high cardiovascular risk. Qual Saf Health Care. 2007;16(5):349–53.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fischer HH, Eisert SL, Durfee MJ, Moore SL, Steele AW, McCullen K, et al. The impact of tailored diabetes registry report cards on measures of disease control: a nested randomized trial. BMC Med Inform Decis Mak. 2011;11:12.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lafata JE, Baker AM, Divine GW, McCarthy BD, Xi H. The use of computerized birthday greeting reminders in the management of diabetes. J Gen Intern Med. 2002;17(7):521–30.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Anderson RM, Musch DC, Nwankwo RB, Wolf FM, Gillard ML, Oh MS, et al. Personalized follow-up increases return rate at urban eye disease screening clinics for African Americans with diabetes: results of a randomized trial. Ethn Dis. 2003;13(1):40–6.

    PubMed  Google Scholar 

  16. Seto W, Turner BS, Champagne MT, Liu L. Utilizing a diabetic registry to manage diabetes in a low-income Asian American population. Popul Health Manag. 2012;15(4):207–15.

    Article  PubMed  Google Scholar 

  17. Edwards HD, Webb RD, Scheid DC, Britton ML, Armor BL. A pharmacist visit improves diabetes standards in a patient-centered medical home (PCMH). Am J Med Qual. 2012;27(6):529–34.

    Article  PubMed  Google Scholar 

  18. Rai A, Prichard P, Hodach R, Courtemanche T. Using physician-led automated communications to improve patient health. Popul Health Manag. 2011;14(4):175–80.

    Article  PubMed  Google Scholar 

  19. de Grauw WJ, van Gerwen WH, van de Lisdonk EH, van den Hoogen HJ, van den Bosch WJ, van Weel C. Outcomes of audit-enhanced monitoring of patients with type 2 diabetes. J Fam Pract. 2002;51(5):459–64.

    PubMed  Google Scholar 

  20. Maclean CD, Gagnon M, Callas P, Littenberg B. The Vermont diabetes information system: a cluster randomized trial of a population based decision support system. J Gen Intern Med. 2009;24(12):1303–10.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chumbler NR, Vogel WB, Garel M, Qin H, Kobb R, Ryan P. Health services utilization of a care coordination/home-telehealth program for veterans with diabetes: a matched-cohort study. J Ambul Care Manage. 2005;28(3):230–40.

    Article  PubMed  Google Scholar 

  22. Sadur CN, Moline N, Costa M, Michalik D, Mendlowitz D, Roller S, et al. Diabetes management in a health maintenance organization. Efficacy of care management using cluster visits. Diabetes Care. 1999;22(12):2011–7.

    Article  CAS  PubMed  Google Scholar 

  23. Subramanian U, Ackermann RT, Brizendine EJ, Saha C, Rosenman MB, Willis DR, et al. Effect of advanced access scheduling on processes and intermediate outcomes of diabetes care and utilization. J Gen Intern Med. 2009;24(3):327–33.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Austin S, Wolfe BL. The effect of patient reminders and gas station gift cards on patient adherence to testing guidelines for diabetes. WMJ. 2011;110(3):132–7.

    PubMed  Google Scholar 

  25. Derose SF, Nakahiro RK, Ziel FH. Automated messaging to improve compliance with diabetes test monitoring. Am J Manag Care. 2009;15(7):425–31.

    PubMed  Google Scholar 

  26. Thomas KG, Thomas MR, Stroebel RJ, McDonald FS, Hanson GJ, Naessens JM, et al. Use of a registry-generated audit, feedback, and patient reminder intervention in an internal medicine resident clinic--a randomized trial. J Gen Intern Med. 2007;22(12):1740–4.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Smith DM, Weinberger M, Katz BP. A controlled trial to increase office visits and reduce hospitalizations of diabetic patients. J Gen Intern Med. 1987;2(4):232–8.

    Article  CAS  PubMed  Google Scholar 

  28. Holbrook A, Thabane L, Keshavjee K, Dolovich L, Bernstein B, Chan D, et al. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ. 2009;181(1–2):37–44.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lin D, Hale S, Kirby E. Improving diabetes management: structured clinic program for Canadian primary care. Can Fam Physician. 2007;53(1):73–7.

    PubMed  PubMed Central  Google Scholar 

  30. Meulepas MA, Braspenning JC, de Grauw WJ, Lucas AE, Harms L, Akkermans RP, et al. Logistic support service improves processes and outcomes of diabetes care in general practice. Fam Pract. 2007;24(1):20–5.

    Article  PubMed  Google Scholar 

  31. Meulepas MA, Braspenning JC, de Grauw WJ, Lucas AE, Wijkel D, Grol RP. Patient-oriented intervention in addition to centrally organised checkups improves diabetic patient outcome in primary care. Qual Saf Health Care. 2008;17(5):324–8.

    Article  CAS  PubMed  Google Scholar 

  32. McDermott RA, Schmidt BA, Sinha A, Mills P. Improving diabetes care in the primary healthcare setting: a randomised cluster trial in remote Indigenous communities. Med J Aust. 2001;174(10):497–502.

    CAS  PubMed  Google Scholar 

  33. Yeh YT, Chiu YT, Liu CT, Wu SJ, Lee TI. Development and evaluation of an integrated patient-oriented education management system for diabetes. Stud Health Technol Inform. 2006;122:172–5.

    PubMed  Google Scholar 

  34. Edelman D, Fredrickson SK, Melnyk SD, Coffman CJ, Jeffreys AS, Datta S, et al. Medical clinics versus usual care for patients with both diabetes and hypertension: a randomized trial. Ann Intern Med. 2010;152(11):689–96.

    Article  PubMed  Google Scholar 

  35. Wagner EH, Grothaus LC, Sandhu N, Galvin MS, McGregor M, Artz K, et al. Chronic care clinics for diabetes in primary care: a system-wide randomized trial. Diabetes Care. 2001;24(4):695–700.

    Article  CAS  PubMed  Google Scholar 

  36. Bond GE, Burr R, Wolf FM, Price M, McCurry SM, Teri L. The effects of a web-based intervention on the physical outcomes associated with diabetes among adults age 60 and older: a randomized trial. Diabetes Technol Ther. 2007;9(1):52–9.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Carter EL, Nunlee-Bland G, Callender C. A patient-centric, provider-assisted diabetes telehealth self-management intervention for urban minorities. Perspect Health Inf Manag. 2011;8:1b.

    PubMed  PubMed Central  Google Scholar 

  38. Ralston JD, Hirsch IB, Hoath J, Mullen M, Cheadle A, Goldberg HI. Web-based collaborative care for type 2 diabetes: a pilot randomized trial. Diabetes Care. 2009;32(2):234–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kim HS. A randomized controlled trial of a nurse short-message service by cellular phone for people with diabetes. Int J Nurs Stud. 2007;44(5):687–92.

    Article  PubMed  Google Scholar 

  40. Kim HS. Impact of Web-based nurse’s education on glycosylated haemoglobin in type 2 diabetic patients. J Clin Nurs. 2007;16(7):1361–6.

    Article  Google Scholar 

  41. Kim HS, Jeong HS. A nurse short message service by cellular phone in type-2 diabetic patients for six months. J Clin Nurs. 2007;16(6):1082–7.

    Article  PubMed  Google Scholar 

  42. Kim HS, Kim NC, Ahn SH. Impact of a nurse short message service intervention for patients with diabetes. J Nurs Care Qual. 2006;21(3):266–71.

    Article  PubMed  Google Scholar 

  43. Kim HS, Song MS. Technological intervention for obese patients with type 2 diabetes. Appl Nurs Res. 2008;21(2):84–9.

    Article  PubMed  Google Scholar 

  44. Kim HS, Yoo YS, Shim HS. Effects of an Internet-based intervention on plasma glucose levels in patients with type 2 diabetes. J Nurs Care Qual. 2005;20(4):335–40.

    Article  PubMed  Google Scholar 

  45. Kwon HS, Cho JH, Kim HS, Lee JH, Song BR, Oh JA, et al. Development of web-based diabetic patient management system using short message service (SMS). Diabetes Res Clin Pract. 2004;66 Suppl 1:S133–7.

    Article  PubMed  Google Scholar 

  46. Cherry JC, Moffatt TP, Rodriguez C, Dryden K. Diabetes disease management program for an indigent population empowered by telemedicine technology. Diabetes Technol Ther. 2002;4(6):783–91.

    Article  PubMed  Google Scholar 

  47. McMahon GT, Fonda SJ, Gomes HE, Alexis G, Conlin PR. A randomized comparison of online- and telephone-based care management with internet training alone in adult patients with poorly controlled type 2 diabetes. Diabetes Technol Ther. 2012;14(11):1060–7.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Sacco WP, Malone JI, Morrison AD, Friedman A, Wells K. Effect of a brief, regular telephone intervention by paraprofessionals for type 2 diabetes.J Behav Med. 2009;32(4):349–59.

    Article  PubMed  Google Scholar 

  49. Meigs JB, Cagliero E, Dubey A, Murphy-Sheehy P, Gildesgame C, Chueh H, et al. A controlled trial of web-based diabetes disease management: the MGH diabetes primary care improvement project. Diabetes Care. 2003;26(3):750–7.

    Article  PubMed  Google Scholar 

  50. Jones D, Curry W. Impact of a PDA-based diabetes electronic management system in a primary care office. Am J Med Qual. 2006;21(6):401–7.

    Article  PubMed  Google Scholar 

  51. McDiarmid T, Chambliss ML, Koval PB, Houck S. Improving office-based preventive care for diabetes. The beneficial results of a patient questionnaire and a flow chart. N C Med J. 2001;62(1):8–13.

    CAS  PubMed  Google Scholar 

  52. Ciemins EL, Coon PJ, Fowles JB, Min SJ. Beyond health information technology: critical factors necessary for effective diabetes disease management. J Diabetes Sci Technol. 2009;3(3):452–60.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Weber V, Bloom F, Pierdon S, Wood C. Employing the electronic health record to improve diabetes care: a multifaceted intervention in an integrated delivery system. J Gen Intern Med. 2008;23(4):379–82.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dijkstra RF, Braspenning JC, Huijsmans Z, Akkermans RP, van Ballegooie E, ten Have P, et al. Introduction of diabetes passports involving both patients and professionals to improve hospital outpatient diabetes care. Diabetes Res Clin Pract. 2005;68(2):126–34.

    Article  CAS  PubMed  Google Scholar 

  55. Grant RW, Wald JS, Schnipper JL, Gandhi TK, Poon EG, Orav EJ, et al. Practice-linked online personal health records for type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med. 2008;168(16):1776–82.

    Article  PubMed  Google Scholar 

  56. Daggy J, Lawley M, Willis D, Thayer D, Suelzer C, DeLaurentis PC, et al. Using no-show modeling to improve clinic performance. Health Informatics J. 2010;16(4):246–59.

    Article  PubMed  Google Scholar 

  57. Cho JH, Chang SA, Kwon HS, Choi YH, Ko SH, Moon SD, et al. Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care. 2006;29(12):2625–31.

    Article  PubMed  Google Scholar 

  58. Glasgow RE, Nutting PA, King DK, Nelson CC, Cutter G, Gaglio B, et al. A practical randomized trial to improve diabetes care. J Gen Intern Med. 2004;19(12):1167–74.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Conrad DA, Perry L. Quality-based financial incentives in health care: can we improve quality by paying for it? Annu Rev Public Health. 2009;30:357–71.

    Article  PubMed  Google Scholar 

  60. Hedges LV, Ilkin I. Statistical methods for meta-analysis. New York: Academic Press; 1985.

    Google Scholar 

  61. Avdal EU, Kizilci S, Demirel N. The effects of web-based diabetes education on diabetes care results: a randomized control study. Comput Inform Nurs. 2011;29(2):101–6.

    Article  PubMed  Google Scholar 

  62. Bailie RS, Si D, Robinson GW, Togni SJ, D’Abbs PH. A multifaceted health-service intervention in remote Aboriginal communities: 3-year follow-up of the impact on diabetes care. Med J Aust. 2004;181(4):195–200.

    PubMed  Google Scholar 

  63. Benhamou PY, Melki V, Boizel R, Perreal F, Quesada JL, Bessieres-Lacombe S, et al. One-year efficacy and safety of Web-based follow-up using cellular phone in type 1 diabetic patients under insulin pump therapy: the PumpNet study. Diabetes Metab. 2007;33(3):220–6.

    Article  PubMed  Google Scholar 

  64. Bond GE, Burr R, Wolf FM, Price M, McCurry SM, Teri L. Preliminary findings of the effects of comorbidities on a web-based intervention on self-reported blood sugar readings among adults age 60 and older with diabetes. Telemed J E Health. 2006;12(6):707–10.

    Article  PubMed  Google Scholar 

  65. Cavan DA, Everett J, Plougmann S, Hejlesen OK. Use of the internet to optimize self-management of type 1 diabetes: preliminary experience with DiasNet. J Telemed Telecare. 2003;9 Suppl 1:S50–2.

    Article  PubMed  Google Scholar 

  66. Cho JH, Lee HC, Lim DJ, Kwon HS, Yoon KH. Mobile communication using a mobile phone with a glucometer for glucose control in Type 2 patients with diabetes: as effective as an Internet-based glucose monitoring system. J Telemed Telecare. 2009;15(2):77–82.

    Article  PubMed  Google Scholar 

  67. Cho JH, Kwon HS, Kim HS, Oh JA, Yoon KH. Effects on diabetes management of a health-care provider mediated, remote coaching system via a PDA-type glucometer and the Internet. J Telemed Telecare. 2011;17(7):365–70.

    Article  PubMed  Google Scholar 

  68. Farmer AJ, Gibson OJ, Dudley C, Bryden K, Hayton PM, Tarassenko L, et al. A randomized controlled trial of the effect of real-time telemedicine support on glycemic control in young adults with type 1 diabetes (ISRCTN 46889446). Diabetes Care. 2005;28(11):2697–702.

    Article  PubMed  Google Scholar 

  69. Fischer HH, Moore SL, Ginosar D, Davidson AJ, Rice-Peterson CM, Durfee MJ, et al. Care by cell phone: text messaging for chronic disease management. Am J Manag Care. 2012;18(2):e42–7.

    PubMed  Google Scholar 

  70. Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera Jr M. The D-Net diabetes self-management program: long-term implementation, outcomes, and generalization results. Prev Med. 2003;36(4):410–9.

    Article  PubMed  Google Scholar 

  71. Harno K, Kauppinen-Makelin R, Syrjalainen J. Managing diabetes care using an integrated regional e-health approach. J Telemed Telecare. 2006;12 Suppl 1:13–5.

    Article  PubMed  Google Scholar 

  72. Hurwitz B, Goodman C, Yudkin J. Prompting the clinical care of non-insulin dependent (type II) diabetic patients in an inner city area: one model of community care. BMJ. 1993;306(6878):624–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kim SI, Kim HS. Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. Int J Med Inform. 2008;77(6):399–404.

    Article  PubMed  Google Scholar 

  74. Kwon HS, Cho JH, Kim HS, Song BR, Ko SH, Lee JM, et al. Establishment of blood glucose monitoring system using the internet. Diabetes Care. 2004;27(2):478–83.

    Article  PubMed  Google Scholar 

  75. Litzelman DK, Slemenda CW, Langefeld CD, Hays LM, Welch MA, Bild DE, et al. Reduction of lower extremity clinical abnormalities in patients with non-insulin-dependent diabetes mellitus. A randomized, controlled trial. Ann Intern Med. 1993;119(1):36–41.

    Article  CAS  PubMed  Google Scholar 

  76. Lorig K, Ritter PL, Laurent DD, Plant K, Green M, Jernigan VB, et al. Online diabetes self-management program: a randomized study. Diabetes Care. 2010;33(6):1275–81.

    Article  PubMed  PubMed Central  Google Scholar 

  77. McCarrier KP, Ralston JD, Hirsch IB, Lewis G, Martin DP, Zimmerman FJ, et al. Web-based collaborative care for type 1 diabetes: a pilot randomized trial. Diabetes Technol Ther. 2009;11(4):211–7.

    Article  PubMed  PubMed Central  Google Scholar 

  78. McMahon GT, Gomes HE, Hickson Hohne S, Hu TM, Levine BA, Conlin PR. Web-based care management in patients with poorly controlled diabetes. Diabetes Care. 2005;28(7):1624–9.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Mehler PS, Krantz MJ, Lundgren RA, Estacio RO, MacKenzie TD, Petralia L, et al. Bridging the quality gap in diabetic hyperlipidemia: a practice-based intervention. Am J Med. 2005;118(12):1414.

    Article  PubMed  Google Scholar 

  80. Moattari M, Hashemi M, Dabbaghmanesh MH. The impact of electronic education on metabolic control indicators in patients with diabetes who need insulin: a randomised clinical control trial. J Clin Nurs. 2013;22(1–2):32–8.

    Article  PubMed  Google Scholar 

  81. Moorman JM, Frazee LA, Dillon ML, Chomo DL, Myers NA. Utilization of a reminder mailing to improve blood glucose log reporting in an outpatient diabetes clinic. Am J Ther. 2012;19(4):255–60.

    Article  PubMed  Google Scholar 

  82. Musacchio N, Lovagnini Scher A, Giancaterini A, Pessina L, Salis G, Schivalocchi F, et al. Impact of a chronic care model based on patient empowerment on the management of Type 2 diabetes: effects of the SINERGIA programme. Diabet Med. 2011;28(6):724–30.

    Article  CAS  PubMed  Google Scholar 

  83. Nes AA, van Dulmen S, Eide E, Finset A, Kristjansdottir OB, Steen IS, et al. The development and feasibility of a web-based intervention with diaries and situational feedback via smartphone to support self-management in patients with diabetes type 2. Diabetes Res Clin Pract. 2012;97(3):385–93.

    Article  PubMed  Google Scholar 

  84. Piette JD, Weinberger M, McPhee SJ. The effect of automated calls with telephone nurse follow-up on patient-centered outcomes of diabetes care: a randomized, controlled trial. Med Care. 2000;38(2):218–30.

    Article  CAS  PubMed  Google Scholar 

  85. Ryan JG, Schwartz R, Jennings T, Fedders M, Vittoria I. Feasibility of an internet-based intervention for improving diabetes outcomes among low-income patients with a high risk for poor diabetes outcomes followed in a community clinic. Diabetes Educ. 2013;39(3):365–75.

    Article  PubMed  Google Scholar 

  86. Smith KE, Levine BA, Clement SC, Hu MJ, Alaoui A, Mun SK. Impact of MyCareTeam for poorly controlled diabetes mellitus. Diabetes Technol Ther. 2004;6(6):828–35.

    Article  PubMed  Google Scholar 

  87. Song M, Choe MA, Kim KS, Yi MS, Lee I, Kim J, et al. An evaluation of Web-based education as an alternative to group lectures for diabetes self-management. Nurs Health Sci. 2009;11(3):277–84.

    Article  PubMed  Google Scholar 

  88. Stone RA, Sevick MA, Rao RH, Macpherson DS, Cheng C, Kim S, et al. The diabetes telemonitoring study extension: an exploratory randomized comparison of alternative interventions to maintain glycemic control after withdrawal of diabetes home telemonitoring. J Am Med Inform Assoc. 2012;19(6):973–9.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Tang PC, Overhage JM, Chan AS, Brown NL, Aghighi B, Entwistle MP, et al. Online disease management of diabetes: engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial. J Am Med Inform Assoc. 2013;20(3):526–34.

    Article  PubMed  Google Scholar 

  90. Tildesley HD, Mazanderani AB, Ross SA. Effect of internet therapeutic intervention on A1C levels in patients with type 2 diabetes treated with insulin. Diabetes Care. 2010;33(8):1738–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Yoo HJ, Park MS, Kim TN, Yang SJ, Cho GJ, Hwang TG, et al. A ubiquitous chronic disease care system using cellular phones and the internet. Diabet Med. 2009;26(6):628–35.

    Article  CAS  PubMed  Google Scholar 

  92. Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Res Clin Pract. 2008;79(2):256–61.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Regenstrief Center for Healthcare Engineering at Purdue University for supporting this research.

Author information

Authors and Affiliations

  1. Internal Medicine, Harvard Vanguard, Atrius Health, Boston, MA, 02215, USA

    Lynn Nuti

  2. Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, 334 Snell Engineering, Boston, MA, 02115, USA

    Ayten Turkcan

  3. Department of Industrial and Systems Engineering, Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA

    Mark A. Lawley

  4. Department of Statistics and Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Lingsong Zhang

  5. Center for Gerontology, Virginia Tech, Blacksburg, VA, 24061, USA

    Laura Sands

  6. Schools of Nursing and Industrial Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Sara McComb

Authors
  1. Lynn Nuti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayten Turkcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark A. Lawley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingsong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laura Sands
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sara McComb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayten Turkcan.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

LS and ML identified research questions and designed the review. SM supervised the design and execution of the study. LN and AT performed the literature search, selected the relevant papers, prepared all tables that summarize the statistical results. LZ and LS helped interpret the statistical results in the papers. LN and SM drafted the manuscript. All authors reviewed and revised the manuscript. All authors read and approved the final manuscript.

Appendix

Table 6 Detailed information about interventions
Full size table
Table 7 Changes in other clinical outcomes
Full size table
Table 8 Changes in behavioral outcomes
Full size table

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nuti, L., Turkcan, A., Lawley, M.A. et al. The impact of interventions on appointment and clinical outcomes for individuals with diabetes: a systematic review. BMC Health Serv Res 15, 355 (2015). https://doi.org/10.1186/s12913-015-0938-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12913-015-0938-5

Keywords

  • Diabetes
  • Interventions
  • Clinical outcomes
  • Behavioral outcomes

BMC Health Services Research

ISSN: 1472-6963

Contact us