Towards Developing Online Compliance Index for Self-Monitoring of Blood Glucose in Diabetes Management

Transcription

1 2016 9th International Conference on Developments in esystems Engineering Towards Developing Online Compliance Index for Self-Monitoring of Blood Glucose in Diabetes Management Ahmad M. Al-Taee 1, Anas Al-Taee 2, Zahra J. Muhsin 2, Majid A. Al-Taee 3, Waleed Al-Nuaimy 3 1 Saint Louis University School of Medicine, Saint Louis, Missouri, USA 2 The University of Jordan, Amman, Jordan 3 Department of Electrical Engineering and Electronics, University of Liverpool, UK altaeeam@slu.edu; altaee.anas@gmail.com; muhsinzj@gmail.com; {altaeem, Abstract Self-monitoring of blood glucose (SMBG) is a key component of modern therapy for diabetes mellitus in which the patients and their healthcare providers can play a major role. In this paper, a rule-based decision support module is proposed towards developing online compliance index for SMBG in diabetes management in patients with type 1 and insulin-using type 2 diabetes. The proposed decision-support application is developed and embedded in a mobile health (mhealth) scenario in which the patients data are collected over a distance and stored in a remote disease management hub for further processing and monitoring purposes. The decision support module utilizes the collected evidences of the day-to-day SMBG and historical data to generate a personalized online compliance index. Patient s attributes relevant to SMBG are categorized into four main groups; (i) glycemic-control attributes, (ii) lifestyle attributes, (iii) mood and well-being attributes, and (iv) complications prevention attributes. A functional prototype of the proposed system is developed and its end-to-end functionality is tested successfully. The proposed system is expected to improve patients follow up through prioritizing delivery of healthcare services based on their compliance index, especially in situations where available caregivers are insufficient to meet the demands for health care services. Keywords decision support; diabetes management; mobile health (mhealth); patient compliance; self-monitoring blood glucose (SMBG) I. INTRODUCTION The worldwide prevalence of diabetes mellitus (DM) has been increasing over the past few decades. A total of 415 million people have diabetes worldwide and this number is expected to rise to 642 million by Furthermore, in 2015, it was estimated that $673 billion or 12% of global health expenditure is spent on individuals with DM [1]. There are two broad categories of DM, type 1 (T1DM) and type 2 DM (T2DM). T1DM (sometimes referred to as juvenile diabetes) that mainly develops in children and young adults is the result of complete or near-complete absence of insulin secretion from the pancreas. People with T1DM therefore need insulin treatment to control their blood glucose (BG) levels [2]. T2DM, which mainly develops in adults, results from a combination of insulin resistance and impaired insulin secretion [3]. Among adults, DM is the leading cause of end-stage renal disease, nontraumatic lower extremity amputations and blindness. Although hemoglobin A1c (HbA1c) is widely considered the gold standard for periodic monitoring of glycemic control and potential complications related to diabetes [4], it does not provide continuous information about day-to-day changes in BG levels [5], [6]. Unlike HbA1c tests, continuous BG monitoring can distinguish among fasting, pre- and postprandial hyperglycemia, and more importantly provide immediate feedback to patients about the effects of food choices, activity, and medication on glycemic control [7]. HbA1c testing cannot make these distinctions or provide such information. This is particularly important in light of the findings of recent studies which reports that postprandial hyperglycaemia is a risk factor for macrovascular disease including heart diseases, independent of HbA1c levels [8]. Furthermore, it was reported in [9] that the influence of postprandial glucose levels becomes even more significant at lower HbA1c levels. Therefore, selfmonitoring of blood glucose is an important adjunct to HbA1c testing in the management of diabetes mellitus. DM is a lifelong disease that requires active participation by patients and/or their healthcare providers in the treatment of the disease. A growing body of medical literature indicates that patients with diabetes mellitus and comorbid depression have greater morbidity, higher mortality, and poorer quality of life than those with diabetes mellitus but no comorbid depression [10]. Studies also revealed that depression in patients with diabetes mellitus is associated with worsening glycemic control as well as a significantly higher risk of complications over time [11]. The rising incidence as well as the healthcare costs related to DM is further compounded by multiple reports revealing a shortage of physicians. Therefore, innovative solutions, such as those that incorporate electronic/mobile health (e/mhealth), are needed to address these issues. This kind of technology support has been effective in improving connectivity between the patients and their healthcare providers through remote patient monitoring [12] - [16], patient empowerment [17] - [19] and decision support [20] - [24]. However, none of these platforms tackled the challenges of developing a real-time adherence index that is a key requirement for effective follow up and prioritization of healthcare services, especially in situations where available healthcare providers are insufficient to meet the demand. This paper presents the design and development of a decision support system that is capable of providing an online compliance index for patients who are administrating daily subcutaneous insulin injections (i.e. patients with type 1 and insulin-using type 2 diabetes). The proposed system utilizes evidences of the SMBG data and other health indicators existing in their electronic medical record (EMR) including logbook statistical summaries, wellbeing, HbA1c /17 $ IEEE DOI /DeSE

2 and other health signs to decide the adherence level of patients on individual basis. The remainder of this paper is organized as follows. Section II presents an abstract view for the mhealth system under study. Section III describes the SMBG and the relevant patient attributes. Next, the development lifecycle and architecture of the proposed application is described in Section IV. Examples for three medical scenarios are presented and discussed in Section V. Finally the presented work is concluded in Section VI. II. SYSTEM OVERVIEW Architecture of the system under study was previously reported in [25], [26]. It comprises two main layers, a patient hub and a web-based disease management hub (DMH). An existing GSM network and/or a Wi-Fi network linked to the Internet links these layers, as shown in the abstract network architecture of Fig. 1. The patient s hub comprises a set of medical sensors linked to a smartphone via Bluetooth, as illustrated. The smartphone acts as a master Bluetooth node for one piconet of medical sensors. It plays a key interface role between the patient and his or her medical sensors from one side and the remote disease management hub from the other. It also acts as an access terminal for the patient s interactivity with the system. The DMH, which hosts the main system intelligence and storage, represents the main application/ service layer for the entire system. It interfaces and interacts with the human objects (i.e. patients, physicians, nurses, dietitians, etc.) as well as the device (i.e. smartphones and other mobile devices) objects of the patient hub. It also collects, processes and monitors the patient s data, and makes appropriate decisions based on constraints specified by the individual treatment plans of patients. Among the numerous healthcare decision support services offered by this DMH [20], [21], the generation and monitoring of online compliance index for diabetes selfmanagement is of particular interest in this study. The proposed application utilizes evidences of the SMBG data and other health indicators existing in the EMR including logbook statistical summaries, wellbeing, HbA1c and other data to decide the adherence level of patients on an individual basis. III. SMBG AND PATIENT ATTRIBUTES A. SMBG Blood glucose (BG) patterns analysis is a systematic approach to identifying glycemic patterns within selfmanagement BG (SMBG) data and then taking appropriate action based upon those results. Recent studies reported that continuous BG monitoring has been recognized as an important guide to glycemic self-management strategies for both patients and their healthcare providers. Furthermore, the availability of electronic BG measurements and efficient pattern recognition algorithms allow health care professionals and patients to quickly identify glycemic patterns and make more informed decisions about therapeutic adjustments that may be required to maintain blood sugar level within normal range. Management of DM aims at improving patient s qualityof-life (QoL) and preventing or delaying the complications and therefore improving morbidity and mortality rates. For this purpose, management has focused on lifestyle modifications (e.g. weight loss, exercise and dietary changes) as well as medications such as insulin. Therefore, SMBG as well as a set of attributes can be incorporated into an online adherence index, which can assess patients adherence to their disease management plan. Moreover, this system can be of great value in classifying patients into separate categories according to their level of compliance. Such models aim to provide more individualized care, reduce healthcare costs, and ultimately assist in addressing the growing problem of physician shortage. B. Patient Attributes Patient s health attributes relevant to SMBG and diabetes care are elicited through numerous meetings and discussions with domain knowledge experts. Table I shows a key set of 10 health-attributes that are identified with the aid of consultant physicians. These attributes are divided into four categories, defined briefly as follows. 1) Glycemic control attributes: It includes hemoglobin A1c (HbA1c), average blood glucose (BG) measurments, and average BG tests per day. 2) Lifestyle-modifications attributes: It includes average physical activity level, body mass index (BMI), and blood pressure (optional for young diabetics). 3) Mood and well-being attributes: It represents an initial screening of patients for depression, which is a well-known risk factor of chronic conditions including diabetes. The most popular screening scale, PHQ-9 [27] is adopted for this purpose. 4) Complication-prevention attributes: It tracks the patient s compliance to periodic clinical exams for eye and foot exams as well as the urine microalbumin test. Figure 1. Abstract network architecture of the mhealth system [25] 46

3 No Attribute Glycemic control attributes 1 TABLE I LIST OF PATIENT S HEALTH ATTRIBUTES Numerical Weights (3) (2) (1) HbA1c < > 9.0 Average BG values (before meals) mmol/l > mmol/l Average BG tests/day > 3 3 < 3 Lifestyle modifications attributes 2 >10 mmol/l Physical activity level > 6 METS 3-6 METS <3 METS Body mass index (BMI) Average blood pressure Mood and wellbeing attributes 3 Depression and wellbeing screening Complication-prevention attributes 4 SBP < 120 mmhg OR DBP < 80 mmhg <18.5 or SBP mmhg OR DBP mmhg 30 SBP 140 mmhg OR DBP 80 mmhg Notes If (glucose within target)à good (3) else if (glucose above targets and no. BG readings >= 3) à acceptable (2) else if (glucose above/below targets and no. of BG readings < 3) or (glucose < targets) à poor (1) > 9 Utilizing the patient health questionnaire-9 (PHQ-9) Most recent eye exam < 1 year 1-2 years > 2 years Most recent foot exam < 1 year 1-2 years > 2 years Most recent urine microalbumin test < 1 year 1-2 years > 2 years Abbreviations: BG: blood glucose, HbA1c: hemoglobin A1c, METS: metabolic equivalent, SBP: systolic blood pressure, DBP: diastolic blood pressure. The compliance level to each of these health attributes is assessed by applying pre-specified thresholds based on which the patient s compliance is categorized into three levels: good, acceptable and poor. The system calculates a score based on which patient s level of compliance is assigned to one of three categories; good, acceptable and poor. The maximum available score is 30 and the minimal score is 10. Patients with a score of >22 are considered of a good SMBG performance while performance of those with a score between 17 and 22 are considered acceptable. Performance of patients who score <17 is considered poor and an immediate action needs to be taken to regulate their performance. IV. THE PROPOSED SYSTEM A. Development lifecycle Development lifecycles of the proposed application has passed through several phases; feasibility assessment, knowledge acquisition, design and programing, and system testing. The main tasks carried out in each phase are described briefly as follows. 1) Feasibility assessment: The goal, scope, and requirements of the proposed adherence index are defined in this phase including the source of knowledge. This is achieved by gathering expertise of domain knowledge experts and software engineers. 2) Knowledge acquisition: In this phase, which is considered the most critical and challenging stage in the entire development lifecycle, the required knowledge and methods are extracted from the domain knowledge experts and related literature. It involved numerous meetings and discussions between the consultant physicians and engineers who developed the mhealth system. 3) Design and programing: The developed form of the acquired knowledge is programmed and integrated in the DMH of the mhealth system under study using front-end cloud programing languages/technologies. 4) System deployment and testing: This phase involved deployment of the developed application in the DMH server and conducting pilot end-to-end testing and refinements that were carried out jointly by the technical and medical team to ensure correctness and validity of the generated results. B. Architecture The main sub-modules making up the decision support application are shown in Fig. 2 and are briefly described as follows. These sub-modules, which form the system s repository model, exchange information so that they can work together effectively. 1) Inference engine: It acts as an interpreter for the knowledge base. It compares values of the collected SMBG data against the conditions given in Table 1 as well as a series of pre-specified premises to find out which condition(s) can be matched. The explanations specified by the winning rules at all premises are then concentrated together to form a final index. This index is generated by applying a series of if then else blocks that may also involve some logical operators (AND, OR, and NOT). 47

4 Figure 2. Architecture of the proposed application 2) Explanation system: It explains the identified SMBG deviation from the treatment plan and possible causes. It also suggests some actions that need to be taken by the patient and/or his health care professional. 3) Knowledge base editor: A database of rules (domain knowledge) that forms the most important part of the application. It contains both general and a case-specific data acquired from the health care experts and other resources (i.e. patients health profile and the collected SMBG data). This editor allows the health care professionals to edit and maintain the information in the knowledge base. 4) User Interface: A graphical interface by which the healthcare professionals can interact and monitor the compliance index for each of their patients through the dashboard of the DMH. C. System implementation The proposed application is implemented in Java and embedded in the business logic layer of the DMH. Software architecture of DMH follows the MVC pattern [26] in which the controller and view of all applications are represented by a core functionality module called service request manager. The controller handles all business logic processes and performs all tasks relevant to service creation and management. It also acts as a coordinator between the models and views for non-asynchronous JavaScript and XML (AJAX) requests (i.e. first page loading) of the browser-based interface. The view is a set of HTML templates that are used to monitor the patient s collected data in various tabular and graphical charts. The model is created for each object (human or device) registered in the central database and used to provide Create, Read, Update, and Delete (CRUD) functionality to active entities. V. EXAMPLE SCENARIOS AND DISCUSSION A. Example Scenarios In this section, three medical scenarios that reflect different compliance categories are outlined as follows. Scenario 1: Good-compliance index A 26-year-old male with a past medical history significant for type 1 DM diagnosed at the age of 13 whose electronic medical record is being reviewed by his primarycare physician. Data reveals a BMI of 21.4 and a hemoglobin A1c of 6.6%. Records also indicate that he has been performing finger-stick glucose 4 times daily (3 times before meals and once before bedtime) with numbers ranging between mmol/l. He had his eye and foot exams performed three months ago and his last urine microalbumin test 3 years ago was within normal limits. He jogs for 30 minutes four times a week and his PHQ-9 score has been consistently below 10 points. This patient scores a compliance index of 28 and therefore he has a good SMBG performance. The application will generate an alert message, notifying the physician about the delay in microalbumin testing and the system. It also sends a reminder to the patient to be prepared to provide a urine sample for a urine microalbumin test. Scenario 2: Acceptable-compliance index A 33-year-old female with the past medical history of thyroid disease, type 1 DM and major depressive disorder presented to her endocrinologist for a follow-up visit. Vital signs as well as her physical examination were within acceptable limits. Her BMI is 26.3 and last hemoglobin A1c was 8.2%. She is a current smoker and rarely exercises. Review of her compliance index attributes shows average finger-stick glucose readings of mmol/l when measured twice daily. Her last eye exam and urine microalbumin test were performed 10 months ago while her last foot exam was performed 4 years ago. Her PHQ-9 screening test average scores 7.3. This patient scores a compliance index of 18 and she has an acceptable compliance level. In this case, the application generates an alert message and sends it to the physician, reporting the need for a follow-up appointment, preferably within 1-2 months to address the management plan. Scenario 3: Poor-compliance index A 17-year-old male with the past medical history of type 1 DM diagnosed 7 years ago whose electronic medical record is being reviewed by his primary care physician after being recently hospitalized for diabetic ketoacidosis. Vital signs were notable for a blood pressure of 145/90. Physical examination reveals obesity as well as loss of sensation over both feet. His BMI is 28.6 and implements a sedentary lifestyle. Review of the patient s compliance index reveals the following: Patient measures his finger-stick glucose 1-2 times daily and the resultant numbers are between mmol/l. His most recent hemoglobin A1c is 9.6%. In addition, patient denies having any eye, vision or urine microalbumin testing prior to this clinic visit. His PHQ-9 screening test score is consistently above 14. The compliance index of this patient scores 11 points and satisfies the criteria of the poor category of the compliance index. As the patient s PHQ-9 is in the moderate-severe range, the system will alert the physician, reporting the need to contact the patient as soon as possible to make sure the patient is not expressing suicidal thoughts or plans. If this was the case, the patient is immediately referred to a suicide emergency hotline to speak to a trained counselor. 48

5 Figure 3. Example screenshot for the blood glucose logbook and summary histogram B. Discussion The developed application gathers all effective compliance-related attributes into a single neatly designed compliance index that can be easily incorporated into modern electronic medical records and e/mhealth systems. For example, it can provide the physicians with an important screening index for patient s SMBG performance and a pre-visit summary. It can also serve as a quality care indicator for the patients population. For example, percentage of patients who maintain regular interactions with the system is a process indicator. An example screenshot for the BG logbook and summary page for the patient s SMBG performance is shown in Fig. 3. Other examples for outcome indicators are the percentages of depressed patients with normal blood pressure and BMI, and percentages of patients with HbA1c and SMBG (above, below, and within the target ranges), frequency of testing by day-period, etc. These quality indicators and others can provide a valuable feedback to assess quality of care and suggest further improvements, as required, in a timely manner. In addition, incorporation of attributes that assess mood and general well-being can be considered of a particular clinical importance, taking into account the increasing incidence of depression and other psychiatric disorders in patients with chronic diseases including DM. Patients who satisfy the PHQ-9 criteria for severe depression will have their physician notified and the appropriate measure can then be taken in a timely manner. Functional testing was carried out using numerous medical scenarios to ensure correctness and validity of the generated explanations. The explanations suggested by the developed application demonstrated high correlation with those assessed manually by a team of three consultant physicians specialized in diabetes and endocrinology. It is worth noting here that the actual clinical evaluation and impact of the developed application is considered beyond scope of this paper at this stage. VI. CONCLUSIONS A fully functional decision support application for evaluation of compliance in patients with DM has been designed, developed and tested successfully. The developed application combines all effective health indicators that are relevant to DM care including depression screening into a single mhealth platform. Patients who satisfy the PHQ-9 criteria for severe depression will have their professional healthcare providers notified and an appropriate measure can therefore be timely taken. For example, this intervention can be life saving for patients who display suicidal ideation or plans. For healthcare professionals, the application can also help in prioritizing care needs of their patients based on the individual compliance index of the patients. This can be of a particular importance in situations where available health care providers are insufficient to meet the demand for health services. The developed prototype however is still open for further improvements through extending the utilized set of health attributes as well as improving its inference engine and the explanation module by considering more complex medical scenarios. In addition, further studies are also needed to assess its acceptability by users taking into consideration the security and privacy concerns, as reported by the authors in [28], as well as its impact on the SMBG outcomes within the context of the modern e/mhealth systems. 49

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