Impact of an intelligent chronic disease management system on patients with type 2 diabetes mellitus in a Beijing community

The rapid development of the economy, the arrival of an aging population, changes in the environment, behavioral lifestyles and other factors have led to a continuous increase in the prevalence and the number of patients of type 2 diabetes [1, 2], which not only seriously has threatened the health of our residents, but also caused a heavy economic burden of disease in China [3]. In response to the challenges of chronic non-communicable diseases represented by type 2 diabetes, the Chinese government initiated hierarchical diagnosis and treatment system reforms nationwide for improving the community health service system in 2009 [4]. In recognition of the need for a more patient-centered approach, the Chinese govenment has piloted a similar program of diabetes chronic self-management care: the Happy Life developed in Australia [5, 6]. The ICDMS is an important tool in the implementation of patient-centered programs targeting chronic health issues.

As a National Demonstration Community Health Service Center in China, the Fangzhuang community health service center in Beijing is one of the sites which first adopted the patient-centered model service. The Fangzhuang community is located in the south of Beijing, and 21.4% of its residents are 60 years of age and older. The Fangzhuang community health service center developed and applied ICDMS to optimize management of chronic diseases [7, 8]. On the basis of the ICDMS clinical application and experience sharing, the standard for internet plus community healthcare management model has been drafted as the standard operating guideline for the ICDMS project [9] and authorized by the Department of National Health and Family Planning Commission of the PRC. In this current study, the effect of the ICDMS on T2DM patients was evaluated in order to provide a reference point for the establishment of community healthcare management and service models in China as well as other developing countries.

In total, 2451 cases of T2DM patients were recruited, which included 1016(41.45%) males. Twenty-three patients were 18–39 years old (0.94%), 1489patients were 40–64 years old (60.75%), and 939 (38.31%) were over 65 years of age. Patients diagnosed with primary hypertension, dyslipidemia, stroke, chronic heart conditions, mental health problem and cancer accounted for 2337 (95.35%), 819 (33.41%), 93 (3.79%), 1413 (57.65%), 849(34.64) and 5(0.2%) of total cases respectively (Table 1).

A total of 9629 follow-up visits were recorded in the post-index period, representing a significant increase(22.23%) over the pre-index period (7874 follow-up visits; p < 0.001). In addition, the average visit number per patient was 3.93 visits in post-index period, which was greater than the 3.21 visits in the pre-index period (p < 0.001); the average follow-up visit time interval was 92.88 days in the post-index period and 113.71 days in pre-index period (p < 0.001); A total of 92.5% of patients were followed up regularly (3–5 times per year) in the post-index period, which was greater than 68.7% in the pre-index period (p < 0.001, Fig. 3).

Compared with the pre-index period, the standard detection rate for fasting blood glucose (FPG) increased from 18.93 to 64.71%, while the standard detection rate for postprandial blood glucose (PPG) increased from 10.77 to 30.64%. The standard detection rates for HbA1c, TC, TG, LDL-C, and HDL-C all increased, from 51.08 to 69.28%, from 27.01 to 66.83%, from 21.30 to 51.82%, from 27.25 to 63.20%, and from 26.89 to 64.26%, respectively. The standard detection rates for blood glucose and lipids all significantly improved, as well as absolute levels (p < 0.001; Table 2).

Compared with the pre-index period, the TC mean significantly declined from 5.22 mmol/L to 5.11 mmol/L (p < 0.05); while HDL-C increased significantly from 1.35 mmol/L to 1.48 mmol/L (p < 0.05). The pre-index and post-index means of TG and LDL-C were comparable (Table 3). The mean body weight did not change significantly (66.51 kg in the pre-index period and 66.50 kg in the post-index period). The HbA1c mean increased from 6.09% in the pre-index period to 6.53% in the post-index period. However, Compared with the patients with HbA1C above 7% in the pre-index period, the decrease in the mean HbA1c from 7.84% in the pre-index period to 6.94% in the post-index period was significant (p < 0.001).

Compared to the pre-index period, the control rate of TC increased from 24.86 to 29.76% in the post-index period, and the control rate of LDL-C increased from 12.16 to 13.97%. The control rate of HDL-C increased significantly from 68.60 to 78.77%. The control rate of HbA1c in the patients with HbA1c less than 7% in pre-index period is 100%, and dropped to 80.91% in post-index period, while in the patients with HbA1c above 7% in pre-index period, the control rate of HbA1c was significantly enhanced to 57.43%in post-indexperiod (P < 0.001) (Table 4).

Diabetes, hypertension and heart disease are most common and are often comorbid in most community health service centers in China. Our study found that by applying the intelligent chronic disease management system, the standardized management rate of blood glucose and blood lipids, as well as levels of TC, and HDL-C improved significantly, findings which are consistent with previous reports [13‐16]. These previous reports suggested that application of such systems improve the clinical outcome of patients withT2DM. This improvement in clinical outcome may be attributed to increased efficiency, enhanced cooperation, and greater interaction between doctors and patients, as well as better performance evaluations [8, 16]. This novel intelligent chronic disease management system is guided by providing real time monitoring and early warning feedback to ensure early diagnosis and early intervention for patients with T2DM. For patients with delayed follow-ups, reminders will be added to patient records for family doctors to proactively contact the patients. This is especially important for patients having a poor understanding of disease control. Such reminders help to maintain continuous health management. A dynamic trend chart combining the scientific and comprehensive evaluation reports and the related health indicators can be generated based on data entered during follow-up of patients. This chart assists family doctors to systematically evaluate the outcome of short-term management, identify health problems in real-time, and accordingly adjust the management protocol. Application of these methods helps to control the blood glucose levels as well as other biochemical parameters in patients with T2DM. In addition, the new intelligent chronic disease management system provides a two-way referral visit, promoting highly efficient clinic visits of patients with T2DM as well as whole-process disease management. Methods involving short text messaging and internet information searches inform the patient and his or her family members of risk factors, thus guiding the self-management of patients with T2DM. Finally, a system of professional performance evaluation of family doctors supports continuous improvement of his or her services, and encourages a more standardized working model of family healthcare, more suited to the practical needs of patients with T2DM. All these functions combine to improve the standardization of team service by the family doctor.

Our results showed that the HbA1c mean increased from 6.09% in the pre-index period to 6.53%in the post-index period. However, compared with the patients with HbA1C above 7% in the pre-index period, the mean HbA1c decreased significantly in the post-index period, the control rate of HbA1c among patients with HbA1C below 7% in the pre-index period was 80.91%, and was 57.3% patients with HbA1C among above 7%. This may be explained by the fact that control rate of HbA1c was already at relatively high levels before intervention, compared with the control rate of blood glucose of T2DM patients at 47.7%, and the comprehensive control rate of blood glucose, blood lipids, and blood pressure at only 5.6% [17]. Moreover, the intelligent chronic disease management system was designed to provide more alerts to the abnormal patients with HbA1C above 7% [8, 9], which maybe make the doctor to care these patients, so these abnormal patients could get better HbA1c outcome. So the system could also improve HbA1c management. However, the system need be optimized to care the normal HbA1c patients to keep them long-term to target.

In this study we also found that, despite widespread availability of Guidelines for Prevention and Treatment of Type 2 Diabetes in China [12] and Guidelines for Prevention and Treatment of Dyslipidemia in Chinese Adults [18], most arteriosclerotic cardiovascular disease(ASCVD) patients with T2DM treated in the Fangzhuang community hospital should be regarded as moderate or high-risk groups for ASCVD and should receive strict hypolipidemic treatment; the control rate for TC is less than 33%, and the control rate of LDL-C is only 13%, which is consistent with findings reported in a recent U.S. study [19]. However, this study reported significantly lower control rates for blood lipids than those reported for patients treated in Chinese hospitals, which was up around 40% [17]. This may be attributed to the fact that management of blood glucose has been assessed as a basic indicator of public health in community medical institutions. However, there has been inadequate attention to lipid management by community healthcare staffs, as well as inadequate education of patients concerning blood lipid control. Therefore, in the future, management and control of blood lipids in patients with T2DM should be strengthened, in order to better prevent cardiovascular complications in diabetic patients.

Real-world data were used in the study to include all patients with T2DM covered by the intelligent chronic system management system in our center, thereby reflecting the actual outcome of this updated system. Objective parameters such as blood glucose and blood lipids were used as outcome measures. These data were imported to the chronic diseases management system directly from the laboratory information system, which ensured the objectivity of data. In addition, within patient comparisons were used in the study to exclude the influence of confounding factors caused by varying characteristics of patients.

Some limitations of the study should be recognized. Firstly, a comparison between patients with T2DM covered in the intelligent chronic disease management system and those not included in the system, and a comparison between Fangzhuang sites with other sites, were not done in a case-control manner, side-by-side. Secondly, the data have not been linked with data from other hospital visits. The observed outcome change may be affected by other hospitals visits. Such data are expected to be considered in future studies, to comprehensively evaluate the performance of the intelligent chronic disease management system. Thirdly, the patients included in the study may be representative of sicker patients with more comorbid conditions such as primary hypertension, chronic heart conditions and mental health problem, and may be representative of many urban patients in community health service centers in China, who are elderly and often with more comorbid chronic conditions, and are willing to visit the community health service center where they doesn’t need take much time in hospital visit. Further studies of less sick patients are needed. In addition, the hemoglobin a1c threshold used is 7%, without analyzing variable goals based on the patient’s age, comorbidities, complications, duration of T2DM etc. because the one goal is very easy to implement in the community hospital. Finally, because of the short operating time of the system, long-term effects on clinical end points (such as cardiovascular and cerebrovascular events) have not yet been analyzed and await future study.

The intelligent chronic disease management system provides a scientifically-based and efficient technology platform, providing key functions for multiple links to optimize management of patients with T2DM. In this current study, the system effectively improved the behavior of primary care providers to manage patients with T2DM with better clinical outcomes.