Development of the Diabetes Complication Surveillance System (DCSS) · 2013. 9. 27. · Development...

Development of

the Diabetes Complication Surveillance System (DCSS)

by

SHUO WANG

A thesis submitted in conformity with the requirements for the

Degree of M.Sc. Health Services Research

Graduate Department of Health Policy, Management and Evaluation

Faculty of Medicine University of Toronto

Copyright 2010

© Copyright by Shuo Wang 2010

Development of Electronic Tool “DCSS” by Shuo Wang Abstract

ABSTRACT

Development of the Diabetes Complication Surveillance System (DCSS)

Shuo Wang

Degree of M.Sc. Health Services Research

Graduate Department of Health Policy, Management and Evaluation

University Of Toronto

2010

Abstract Information technology [IT] that enables electronic access to patient health records has been widely

recognized as a promising means to improve the quality of care for patients with chronic diseases, and

reduce health care costs through better health information delivery and encouragement of self-

management. IT applied to assist chronic disease management is inadequately studied in Canadian

health care settings. This thesis describes the development and modest pilot implementation of an

electronic tool, the Diabetes Complication Surveillance System [DCSS]. The DCSS was conceived as

a self-monitoring tool that facilitates regular checks on conditions of diabetes patients, including acute

and long-term complications. The DCSS is relatively unusual, as it facilitates glycemic control and

also allows patients to address the long-term complications of diabetes. The development of the DCSS

involved literature reviews and consultations with clinician experts. Questionnaire results from the

pilot provided positive feedback.

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Development of Electronic Tool “DCSS” by Shuo Wang Acknowledgements

ACKNOWLEDGEMENTS

Having successfully accomplished the study, I would like to express my gratitude to all the following

people for their contribution to this research project: Ann Jones and Dr. Phil McFarlane from St.

Michael’s Hospital and Dr. Matthew Oliver from Sunnybrook Health Sciences Centre.

Especially, I would like to express appreciation to my Supervisor, Dr. Sandra Donnelly, for her

innovative idea of building such a system to improve diabetes care based on her many years of front

line clinical experience, as well as her strong support to complete the entire project including the

development of the DCSS, the questionnaire survey, and patient investigation.

Finally, my thanks to Dr. Whitney Berta, Associate Professor at the Department of Health Policy,

Management and Evaluation in the Faculty of Medicine at the University of Toronto, for her

contributions as a Committee Member.

NOTE: No specific grant was obtained for any of the project activities. The project was not funded by

a funding agency. Dr. Sandra Donnelly made personal investments in hardware, software, and funded

Web application development.

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Development of Electronic Tool “DCSS” by Shuo Wang Table of contents

TABLE OF CONTENTS ABSTRACT ............................................................................................................................................. ii

ACKNOWLEDGEMENTS.....................................................................................................................iii

TABLE OF CONTENTS ........................................................................................................................ iv

EXECUTIVE SUMMARY ....................................................................................................................vii

CHAPTER 1: INTRODUCTION ........................................................................................................ 1

1.1 Issues Relating to Chronic Disease in Canada ......................................................................... 1 1.2 The Burden of Diabetes ............................................................................................................ 2 1.3 Information Technology and Diabetes Management ............................................................... 3 1.4 Diabetes Surveillance to Support Self-Management................................................................ 6 1.5 Research Questions................................................................................................................... 6

CHAPTER 2: LITERATURE REVIEW ............................................................................................. 8

2.1 What Is Known about the Self-Management of Chronic Disease............................................ 8 2.2 Self-Management of Diabetes via Information Technology (IT)........................................... 11

2.2.1 Telemedicine................................................................................................................... 11 2.2.2 eTools Designed to Improve Diabetes Mellitus Management ....................................... 12 2.2.3 Linking Diabetes Guidelines to eTools .......................................................................... 14

2.3 Electronic Tools: Components and Factors That Impact Access ........................................... 15 2.4 Study Objectives..................................................................................................................... 18

CHAPTER 3: METHODS................................................................................................................. 20

3.1 Overview................................................................................................................................. 20 3.1.1 Timeline.......................................................................................................................... 21

3.2 Initiation.................................................................................................................................. 21 3.3 Development and Implementation of Electronic Tool “DCSS”—Months 1–6...................... 21

3.3.1 System Overview............................................................................................................ 22 3.3.2 User Profile Data Fields Selection.................................................................................. 22 3.3.3 Surveillance Benchmarks Selection ............................................................................... 22 3.3.4 System Design ................................................................................................................ 25 3.3.5 DCSS Development / Implementation ........................................................................... 25

3.4 Pilot of DCSS—Months 7–12 ................................................................................................ 28 3.4.1 Development of Brief Questionnaire “Patients' Views of DCSS Utility”...................... 29 3.4.2 Participant Recruitment / Data Collection...................................................................... 30 3.4.3 Data Analysis.................................................................................................................. 30 3.4.4 Privacy and Confidentiality ............................................................................................ 31

CHAPTER 4: RESULTS................................................................................................................... 32

4.1 Development of Electronic Tool “DCSS”.............................................................................. 32 4.1.1. User Profile Data Fields.................................................................................................. 32 4.1.2. Surveillance Benchmarks ............................................................................................... 32 4.1.3. Application Features....................................................................................................... 33 4.1.4. DCSS Development........................................................................................................ 35 • DCSS Workflow..................................................................................................................... 35 • DCSS Database Design .......................................................................................................... 37 • DCSS Screenshots (Details in Appendix) ............................................................................ 39

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4.1.5. System Implementation .................................................................................................. 40 4.2 Pilot of DCSS ......................................................................................................................... 41

4.2.1 DCSS Pilot Results ......................................................................................................... 41 CHAPTER 5: DISCUSSION............................................................................................................. 43

5.1 Development of the Electronic Tool "DCSS" ........................................................................ 43 5.1.1. User Profile Data Fields.................................................................................................. 43 5.1.2. Surveillance Benchmarks / Electronic Tool Indicators .................................................. 43 5.1.3. System Technology Selection......................................................................................... 45 5.1.4. System Functionality ...................................................................................................... 46 5.1.5. System Implementation .................................................................................................. 47 5.1.6. Maximizing Access to an Electronic Self-Management Tool........................................ 48

5.2 Pilot of DCSS ......................................................................................................................... 49 5.3 Limitations.............................................................................................................................. 51 5.4 Implications and Recommendations....................................................................................... 51

CHAPTER 6: CONCLUSIONS AND FUTURE DIRECTIONS ..................................................... 53

6.1 Development of the Electronic Tool "DCSS" ........................................................................ 53 6.2 Implications ............................................................................................................................ 54 6.3 Future Research ...................................................................................................................... 54 6.4 Summary................................................................................................................................. 55

REFERENCES ....................................................................................................................................... 57

APPENDICES ........................................................................................................................................ 63

APPENDIX 1: DCSS Database Design ............................................................................................. 63 APPENDIX 2: Diabetes Complication Surveillance System Questionnaire ..................................... 66 APPENDIX 3: Survey Detailed Results............................................................................................. 67

1. Subject enrollment and characteristics ................................................................................... 67 2. Internet and accessing health information on Internet............................................................ 67 3. Patient right to access health records...................................................................................... 68 4. Patient response to electronic health records.......................................................................... 68 5. Patient concern about security and privacy of EHR............................................................... 69 6. Patient issues around understanding & updating EHR, & errors ........................................... 70 7. Patient response to Diabetes Complication Surveillance System – usefulness...................... 70

APPENDIX 4: Acronyms................................................................................................................... 72 APPENDIX 5: Glossary Information Technology Terms ................................................................. 73

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Table Index Table 1 eTool Indicators and Best Accessed.................................................................................16 Table 2 DCSS Project Timeline ....................................................................................................21 Table 3 Surveillance Benchmarks and Sources.............................................................................22 Table 4 Demographic Information in DCSS .................................................................................32 Table 5 Clinical Benchmarks in Diabetes Complication Surveillance (based on data in 2005) ...33 Table 6 Patient Demographic Information (Age, Gender, Education, English as 1st language) ...41 Table 7 Study Results in Themes ..................................................................................................42 Figure Index Figure 1 Business Logic Workflow...............................................................................................26 Figure 2 DCSS Workflow Diagram ..............................................................................................36 Figure 3 Patient’s Demographics Modification Page....................................................................38 Figure 4 Add New Medical Record for Patients ...........................................................................39 Figure 5 DCSS Screenshot Sample – Patient List .........................................................................39

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Development of Electronic Tool “DCSS” by Shuo Wang Executive Summary

EXECUTIVE SUMMARY

OBJECTIVE

This study focused on the development of an evidence-based electronic tool, the Diabetes

Complication Surveillance System [DCSS] and a modest implementation pilot of the tool. The DCSS

was conceptualized as a surveillance tool that facilitates the management of a diabetes patient’s

condition, including acute and long-term complications. Most programs for diabetes management have

focused on achieving good glycemic control and managing the acute complications of diabetes. As an

electronic tool, the DCSS is unique in that it helps achieve both purposes: it facilitates glycemic

control while allowing patients to address the long-term diabetes complications.

DESIGN AND MEASUREMENT

The DCSS was designed as a Web-based system for providing opportunities for patients and providers

to access patients’ records through the Internet. Under a defined timeline, the DCSS development and

the pilot patient survey were completed.

DCSS development included two steps: data field selection and development procedures. Data field

selection included choosing fields relating to two sections, the “user profile” and the “surveillance

benchmark” sections. Four resources were used to guide the selections of data fields: Clinical Practice

Guidelines [CPG] 2003 developed by an expert committee of the CDA’s clinical and scientific section,

review of diabetes literature and other eTool publications related to diabetes, the input of clinical

experts, and technical specifications of the system including MS Window Server 2000, MS Access

2000 Database, Java Server Page, and Tomcat as Web Server.

In the pilot, I engaged a few patients to view the DCSS, and asked them to provide their feedback via a

questionnaire I developed. Data collected from the pilot included patient demographics. (i.e., age,

gender, education, first language) and six additional domains including (1)use of the Internet to access

health information; (2) patient’s right to access records; (3)response to EHR; (4)security and privacy

concerns; (5)EHR help managing disease; and (6)DCSS usefulness.

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RESULTS

The DCSS ultimately contained the following data fields:

Demographics

MRN /OHIP# + version /Name /Date of birth /Marital status /Gender

/Address /Phone numbers /Email /Payment program

Surveillance

Benchmarks

For acute care: HbA1C /Blood glucose /Lipids, LDL-C /Weight /Height /

For complications: Urinary albumin /Blood pressure /Exam on eye, feet, heart

Application

Features

Doctor profile / Patient profile / Patient medical record information in 9 key

benchmarks.

Actions: View /Add /Modify/Delete

In the pilot, I enrolled 12 patients to view the DCSS. I analyzed patient demographics on age, gender,

education, and English as first language. I also analyzed patient feedback through 19 questions

addressing the six domains. Overall, the feedback was positive.

DISCUSSION

The user profile is an essential component of an eTool applied in a health care setting. This

information can be used for many purposes: i.e., to identify an individual, to link doctors and patients,

to link a patient to his/her medical record, and to provide contact information for message delivery.

While seemingly straightforward, careful selection of the fields for this section of the DCSS, and any

eTool, are important.

Similarly, judicious and parsimonious selection of surveillance benchmarks is important and

challenging. These benchmarks provide key checking indicators for monitoring diabetes

complications. Including excess benchmarks will mandate the investment of more resources for

development than are really needed; including fewer may lead to a failure to collect key information.

With careful consideration and research, and through consultation with clinical experts, I selected 9

benchmarks which have been demonstrated to be useful in diabetes complication surveillance: 4

benchmarks relate to the management of acute complications, and 5 relate to the management of long-

term complications.

In addition, selecting the right technology will enable system scalability. Under a limited budget and

timeline, I undertook an efficient approach to development of the DCSS, in terms of technology

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applied and functions included. I concluded my study with a modest pilot involving 12 diabetes

patients, and summarized success factors and challenges that emerged. Overall, patient reception of the

DCSS was positive. The pilot was facilitated mainly by effective leadership, adequate knowledge in

health informatics, cooperation from some clinic staff and good project management. The main

challenges identified were lack of funding and lack of support from the hospital IT department.

CONCLUSION

Applying the DCSS in practice may require a shift in procedure, extracting the surveillance of the

long-term complications of diabetes from the current hospital-centered care model to one with

additional self-management by patients. This innovation represents a reformatting of currently

supported activities that create efficiencies for the health care system and conveniences for health care

providers and patients.

Choosing appropriate technology and designing a useful system are the keys to designing adoptable

systems for end users. Providing training and support to eTool users, including patients and clinicians,

will increase users’ interest and ensure data quality in the system.

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Development of Electronic Tool “DCSS” by Shuo Wang Chapter 1 - Introduction

CHAPTER 1: INTRODUCTION

This chapter introduces background information that led to the study. It describes issues in Canadian

health care related to the prevalence and management of chronic disease, discusses the potential for

information technology to assist in the management and self-management of chronic disease, and

states the objective of the study.

1.1 Issues Relating to Chronic Disease in Canada Since its founding in 1957, the Canadian Medicare system’s enduring purpose has been to ensure

timely access to quality care, the sustainability of the system, and wellness for Canadians. While the

health care system was designed to address acute diseases through hospital-based care

delivery(Wagner et al. 01a), today’s health problems are predominantly chronic in nature and include

heart disease, cancer and diabetes(Kirby, 02) [p243-246]. Thousands of Canadians die every year and

tens of thousands are hospitalized because of complications related to their chronic illnesses(Rachlis

M, 03).

The publicly funded Canadian health care system is based on the five principles of universality,

accessibility, portability, comprehensiveness and public administration(Canada, 04;COACH, 03). The

continued rise in health care expenditures(CIHI, 04b) has generally been attributed to the costs of

caring for a growing elderly population and a growing proportion of the population suffering chronic

disease, which certainly includes elderly Canadians(Gordon, 08;National Physician Survey, 08). In

addition to costs incurred by hospitalization, doctor visits and medication, there are societal costs

including those related to work absences and the provision of informal care. In light of these tensions,

the sustainability of the current health care system has emerged as one of the most urgent national

issues of the 21st century(COACH, 03).

According to a report released by the Canadian Institute for Health Information [CIHI], in 2003

Canada spent $121.4 billion on health care, or an average of $3,839 per person. After inflation, this is

an increase of 30% from 1993 and 62% from 1983. Health care costs are now responsible for 10% of

the nation’s total economy (gross domestic product), a historic high first reached in 1992. Hospitals,

drugs, and doctors’ services account for the bulk of health spending(CIHI, 04a;CIHI, 04d;CIHI,

04b;CIHI, 04c). In terms of the cost of chronic disease to the Canadian economy, it differs depending

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upon the disease. A study of eight types of chronic disease (cancer, musculoskeletal disease,

cardiovascular diseases, diabetes, hypertension, neuropsychiatric diseases, respiratory diseases, and

other miscellaneous diseases) compared costs across provinces. The authors found that, “In Canada,

costs estimated in 1999 for diabetes were translated into $9.9 billion both direct and indirect costs in

present 2005 $ value.”(Jayadeep Patra et al, 07).

1.2 The Burden of Diabetes The increasing prevalence of chronic disease and multiple co-morbidities among Canadians and North

Americans in general, is exerting unprecedented demands on the health care system(Debra Black, 05),

and has led to concerns about the escalating costs of care. Increasingly, attention is given to

mechanisms, structures, and systems that may reduce the costs of chronic care.

According to the Canadian Diabetes Association(Canadian Diabetes Association, 08), over two million

Canadians have been diagnosed with diabetes, and one third of diabetes cases are not yet diagnosed.

People with diabetes are at a greater risk of heart attacks than the general population(Catherine Zahn,

06). Diabetes is the leading cause of adult blindness and amputations, and is also the leading cause of

kidney disease in Canada.

Black reported that 50% of Canadian Type 2 diabetes patients do not have historicized records of

blood-sugar levels, indicating that their diabetes is not being controlled. As a result, they are

increasingly susceptible to complications including heart attack, stroke, kidney disease and blindness.

Furthermore, 600,000 Canadians are not aware that they have diabetes because they do not yet display

symptoms(Debra Black, 05).

Diabetes is the seventh-leading cause of death in Canada. Over the next five to ten years, the number

of Canadians affiliated with diabetes is expected to jump to three million, which will add to the already

heavy burden of this disease on the Canadian health care system(Debra Black, 05).

The increasing prevalence of diabetes has been described as an epidemic, and its negative economic

impact has been documented. Costs include those related to prescription drugs, lost productivity,

direct health care services, and personal expenditures related to seeking care. It is estimated that in

1998, “diabetes accounted for $0.4 billion for hospital care and drugs, and $1.2 billion in indirect

costs…”(Stewart, 05).

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Health Canada reports spending over $9 billion annually on diabetes. Black estimated that the disease

costs the health care system $13.2 billion per year, and that this number is rising exponentially. By

2020, the disease and its complications could cost the system as much as $19.2 billion annually(Debra

Black, 05). Comparable statistics and estimates are cited for the United States, where diabetes is the

fifth-leading cause of death.

1.3 Information Technology and Diabetes Management Electronic access to patient health records has been widely recognized as a particularly promising way

to reduce the costs of care. It is thought to provide better and more timely delivery of health

information among care providers, and to enable the self-management of chronic care(Wagner et al.

01a;Kerkenbush and Lasome, 03;Goldberg, Ralston, Hirsch, Hoath, and Ahmed, 03;Bodenheimer,

Lorig, Holman, and Grumbach, 02b;Bodenheimer, Wagner, and Grumbach, 02a). While self-

management eTools have caused a great deal of discussion, little has been done to date to develop and

trial them.

This study focuses on the development of a technology-based management system to assist in the self-

management of diabetes among adults. Diabetes Mellitus [DM] is a serious chronic disease that is

costly to affected individuals and society. If untreated or improperly managed, it can result in a variety

of complications, which contribute to significant morbidity and early mortality. Diabetes is one of four

chronic “Ambulatory Care Sensitive Conditions” [ACSC] associated with hospitalizations that are

deemed avoidable when patients have timely access to high-quality care in their communities. Such

care includes disease prevention programs and appropriate primary health care(CIHI, 03). As with

other ACSC, the quality of care relating to these conditions is strongly influenced by the coordination

of care and by the timeliness, appropriateness and quality of patient information that is brought to bear

in care decision-making(Statistics Canada and CIHI, 03;Romanow RJ., 02). There appears to be

significant potential for technology-facilitated information management to improve the quality and

coordination of diabetes care. As noted in earlier sections, diabetes programs have mostly focused on

achieving good glycemic control and managing the acute complications of diabetes. However, the

importance of observing and monitoring long-term diabetes complications to decrease acute

complications and associated mortalities should be recognized. Therefore, in this study, DCSS was

designed as a self-management/monitoring tool to facilitate the tracking and monitoring of benchmarks

associated with long-term complications, allowing for their timely treatment.

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The Internet and Health Information Access

Reliance on information technology requires that patients are somewhat comfortable with using the

Internet. A survey conducted by the University Health Network [UHN] showed that 68% (693/1019)

of UHN patients had Internet access. Of the patients who had Internet access, 75% (520/693) wanted

access to medical information over the Internet(Chiu, Rizo, and Wang, 04). According to Statistics

Canada(Stat Canada, 08), 73% of the population in 2007 used the Internet, compared with 68% in

2005. It is felt that citizen use of the Internet for gathering health information is increasing.

The Web is now widely accessed by diabetes patients seeking relevant health information. In a study

by Dedell (2004), three criteria – usability, content, and reliability – were used to assess the medical

information relevant to diabetes mellitus management available through Google, Yahoo and the

Mendosa directory. Dedell studied 47 websites, giving only five a usability ranking of ‘high’. The

remainder were cluttered or inundated with distracting information. Content was generally excellent,

but limited by an absence of specific advice, and only 17% of the websites met all of the criteria for

reliability.

Electronic Health Records [EHR] for Diabetes

While the tool developed around the present study is not a full electronic health record, it is important

to discuss the DCSS in the context of other electronic tools like the EHR that are used to manage

patient information and chronic conditions.

According to HIMSS [The Healthcare Information and Management Systems Society], “The

Electronic Health Record (EHR) is a longitudinal electronic record of patient health information

generated by one or more encounters in any care delivery setting. Included in this information are

patient demographics, progress notes, problems, medications, vital signs, past medical history,

immunizations, laboratory data and radiology reports.” 1

Electronic Health Records [EHRs] have been seen as promising mechanisms by which to improve

chronic care including diabetes by promoting health information delivery(Canada, 04;COACH,

03;Kirby, 02;Romanow RJ., 02). Electronic decision support promises to improve evidence-based

1 Definition of Electronic Health Record (EHR) - http://www.himss.org/ASP/topics_ehr.asp

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http://www.himss.org/ASP/topics_ehr.asp


practice in chronic disease management. The role of information systems has become increasingly

important in the evolution of new forms of medical records and communication structures. EHRs can

provide visible monitoring for medical records and shareable data among doctors, nurses and patients

in hospitals and primary care.

EHRs have been available to health care practitioners for over a decade. Different EHRs provide

various functions in different formats, and are built for different purposes. Canada's Health Informatics

Association discusses EHRs as part of the solution to Canada’s health care sustainability

issues(COACH, 03). In particular, EHRs promise to facilitate quality and coordination of care, and the

timeliness of patient information transfer in the area of chronic disease management(Romanow RJ.,

02). EHRs are also recommended as a solution in three major provincial and national health reports to

ensure accountability and sustainability, including the Romanow Commission (Romanow RJ., 02) and

Kirby Committee reports(Kirby, 02). Mazankowski (2001) contends that EHRs stand to improve the

health of individuals and the quality of our health care system through data sharing, more efficient data

access(Mazankowski D., 01). Gorman et al. argue that EHRs represent a reduction of errors in record

keeping(Gorman et al. 96). In the Kirby report, EHRs were depicted as the cornerstone of an efficient

and responsive health care delivery system with improved quality and accountability(Kirby, 02). The

Romanow report stated that EHRs are key to modernizing Canada's health system and to improving

access and outcomes for Canadians. The Romanow report also cited improvements in diagnoses,

treatment results, efficiency, security, and the accuracy of personal health records. In addition, EHR

data can be used for health research and surveillance, as well as for tracking disease trends and

monitoring health status(Romanow RJ., 02). The concept of applying electronic information

technology in health care to improve the quality of care continues to be widely promoted by the

Canadian government. Most recently, Canada Health Infoway CEO Richard Alvarez announced at the

eHealth 2009 conference that the agency would spend $500 million funding physician EMR systems,

interoperability among electronic solutions, and other eHealth initiatives(Jerry Zeidenberg, 09).

Despite widespread support for the concept, EHR implementation faces many challenges in terms of

privacy, fragmented eHealth authorities, a lack of data standards, barriers to physician adoption, and

knowledge barriers on the part of consumers relating to the use of Personal Health Records(COACH,

03). To some extent, challenges associated with standards and uptake relate to a general lack of

rigorous consideration of content and consumer perspectives in the design of many systems.

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1.4 Diabetes Surveillance to Support Self-Management With respect to diabetes care specifically, studies have shown that intensive diabetes monitoring with

diet control and the maintenance of blood glucose, LDL, and urine albumin lead to a substantial

reduction in the rate of complications (e.g. blindness, kidney failure, amputations and circulatory

diseases), which can be difficult to achieve through conventional means(Hejlesen, Plougmann, and

Cavan, 00).

Management of chronic disease by care providers and self-management by patients are increasingly

emphasized as ways to address the emerging issues discussed above. Self-management stands to

improve chronic disease outcomes by reducing complications through diabetes education or detecting

issues early through the monitoring of key benchmarks(Stewart, 05).

Electronic access and sharing of patient (health/medical) information are seen as mechanisms to assist

self-management, or through which a self-management program or regimen can be delivered. A Web-

based approach allows patients to access their health care information through the Internet anytime and

anywhere.

Prior studies show that patients consider home access to their records convenient(Canhealth, 08).

Benefits to patients include reduced costs and time associated with travel, and possible improvements

to the quality of care. Patients do not need to go to the hospital to get a lab result, but can access their

records online. Further, patients may be able to obtain health care information prior to a routine

scheduled visit to the doctor; clinic visits may be more efficient for patients who really need physician

consultation.

With electronic self-management tools, extra care information available to doctors can lead to better

monitoring. Patients can enter self-measured data into such tools from home. The tools can act as

reminders to patients, leading to better self-management of diabetes.

1.5 Research Questions Research Questions

While work done to date on EHRs and self-management tools consistently supports the concept of

EHRs improving self-management, few studies rigorously combine considerations of the content of

self-management eTools and adherence to the principles of IT development.

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This paper describes the development of an evidence-informed self-management tool, the DCSS, for

adults with diabetes mellitus. The principles of IT development were applied in the development of the

DCSS. The following questions were addressed during the tool’s development and implementation:

- What are the key components and design features that should be included in a self-management

tool intended to facilitate surveillance of diabetes complications, such as clinical indicators,

workflow, and functionalities?

- What are potential facilitators of, and challenges to the use of electronic self-management tools

from the perspective of patients?

The design of the DCSS was informed by extant literature, reviewed in Chapter 2, on chronic disease

management models and information technology including telemedicine and EHR. Chapter 3 describes

the process of developing the tool, including the involvement of clinical experts.

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Development of Electronic Tool “DCSS” by Shuo Wang Chapter 2 – Literature Review

CHAPTER 2: LITERATURE REVIEW

This chapter describes literature review on Chronic Disease Management models and chronic disease

self-management, as well as related topics involving information technology in chronic disease

management. The literature review guided us with respect to the content of the DCSS, and in the

minutiae of the development process.

2.1 What Is Known about the Self-Management of Chronic Disease Self-management programs have been widely reported as successful in assisting patients in managing

their chronic conditions(Foster, Taylor, Eldridge, Ramsay, and Griffiths, 07;Lorig et al. 99;Lorig,

Sobel, Ritter, Laurent, and Hobbs, 01;Lorig, Ritter, Laurent, and Plant, 06). In late of the 20th century2,

new concepts have been introduced, including the Chronic Care Model [CCM], Chronic Disease

Management, Chronic Illness Management, and Chronic Condition Improvement.

It is important to achieve optimum therapy goals through educating and supporting patients in

managing their chronic diseases. Self-management is an essential component for clinical outcomes of

diabetes. Planned care is recommended as a redesigned model for chronic disease care that involves

utilizing clinical information systems and implementing guidelines to support of self-management.

In a 2001 publication, Wagner et al. developed a guide to improve chronic care, called the

Chronic Care Model. Their study indicated that chronic disease patients have difficulties

managing their condition, due to the mismatch that exists between the encouragement given

patients to undertake self-management and the traditional approach of the medical system. “Our

care systems were organized historically to respond rapidly and efficiently to any acute illness or

injury that came through the door. The focus was on the immediate problem, its rapid definition

and exclusion of more serious alternative diagnoses, and the initiation of professional treatment.”

Elements of CCM include health care organization, community resources, self-management

support, delivery system design, decision support, and a clinical information system(Wagner et

al. 01a). A subsequent study conducted by Wagner et al. examined the impact of organized

periodic primary care sessions in meeting the complex needs of diabetic patients, and improving

2 Chronic Care Model - http://en.wikipedia.org/wiki/Chronic_care_management

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http://en.wikipedia.org/wiki/Chronic_care_management


diabetes care and outcomes. The results of the study showed improved outcomes for diabetes

care, including more recommendations on preventive procedures and helpful education, fewer

visits to specialty and emergency rooms, and better HbA1c levels among intervention group

participants(Wagner et al. 01b).

Published in 2001 as well, Glasgow et al. conducted a research study focusing on the chronic care

model and diabetes. In the study, the authors identified key characteristics of effective diabetes

management programs, such as using a population-based systems approach, incorporating active

patient participation, and using patient-centered collaborative goal setting. The paper also mentioned

using clinical information systems to improve quality of care, such as diabetes registries and electronic

medical records. Seven top-rated barriers (from a total of 37 submitted) to establishing a chronic care

model for diabetes care were identified, including a lack of appropriate health care policies, poor

understanding of population-based chronic disease management, and inadequate integration of

information systems to enable the sharing of information across providers(Glasgow et al. 01). In

addition, the authors concluded that barriers are generally due to systems issues arising from the acute

illness model of care, where the “vast majority of physicians have been trained, and most of our health

care systems have been established, to treat acute illness.” Finally, the paper suggested that future

research is needed to overcome these barriers, including “research on the Internet and other interactive

technologies to inform patient-provider interactions, deliver self-management support, and coordinate

health care team efforts.”(Glasgow et al. 01).

In 2002, a systematic review of studies of chronic disease management confirmed the utility of the

CCM for improving chronic care (using diabetes as an example) and reducing health care costs. The

majority of studies included in the review (32 out of 39) showed positive clinical outcomes. Further, 18

of 27 studies focused on 3 chronic conditions (congestive heart failure, asthma, and diabetes) and

demonstrated reductions in health care costs(Bodenheimer et al. 02a).

In 2004, based on reviewing 71 trials of self-management education programs, Warsi’s study found

that “self-management education programs resulted in small to moderate effects for selected chronic

diseases.” Diabetes patients demonstrated reductions in glycosylated hemoglobin levels and

improvement in systolic blood pressure. Asthmatic patients experienced fewer attacks, but no

statistically significant effects were observed for arthritis (Warsi, Wang, LaValley, Avorn, and

Solomon, 04).

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Another study by Bodenheimer and colleagues encouraged the partnership between patients and

providers through collaborative care and self-management education. The paper indicated that, by

providing problem-solving skills, self-management education supports better quality of life for patients

with chronic conditions. Compared with traditional patient education which offers information and

technical skills, self-management education programs may improve clinical outcomes and reduce costs.

In the paper, the authors indicated that a central concept in self-management is self-efficacy, where

patients believe in their abilities to solve problems and to reach a desired goal through changes in their

behaviour (Bodenheimer et al. 02b).

With 791 participants enrolled, Meyer studied the concept that “technology was to improve health

status, increase program efficiency, and decrease resource utilization” when suitable technology was

chosen to enhance the care coordinator role. The evaluation data showed positive results. There were

reductions of emergency room visits (40%), hospital admissions (63%), hospital bed days of care

(60%), VHA nursing home admissions (64%), and nursing home bed days of care (88%). These results

found that long-term care services requests (nursing care) decreased more than acute care services (ER

visits). Finally, the research concluded: “All performance improvement outcomes reached or exceeded

the targeted goals.”(Meyer, Kobb, and Ryan, 02).

Under the domain of redesigning the health care delivery system to support patient self-management

by using clinical information and decision support systems, a study by Rothman indicated that chronic

diseases patients do not receive effective therapy and do not have optimal disease control. Therefore,

chronic care is shifted to specialists or disease management programs. “The future of primary care may

depend on its ability to successfully redesign care systems that can meet the needs of a growing

population of chronically ill patients”(Rothman and Wagner, 03).

Concerning self-management of chronic kidney disease, one of the possible complications of diabetes,

a study by Costantini discussed the impact of its patient self-management experiences on the level of

support needed. This qualitative study identified themes, such as searching for evidence, taking care of

the self and the need for disease-specific information(Costantini et al. 08).

10


2.2 Self-Management of Diabetes via Information Technology (IT) Integrated clinical management systems can facilitate the management of clients with chronic diseases

and provide an efficient way to integrate consultations and client education with monitoring, follow-

up, and support(White and etc, 01). Diabetes has been the focus of telemedicine and medical

information technology for years(Lahtela and Lamminen, 02). In this section, I describe published

studies on applying IT to facilitate diabetes self-management.

First, at the earliest stage, many studies discussed using telemedicine to improve diabetes care. These

studies are reviewed in the first subsection below. Later, in tandem with IT development, electronic

tools were applied in diabetes management. Studies of those applications, discussed in the second

subsection, have mainly focused on clinical evaluation. After those studies, some researchers

introduced more functionality to eTools—for example, adding “Clinical Decision Support” by

including diabetes guidelines in applications (third subsection below).

2.2.1 Telemedicine

Successful telemedicine projects have demonstrated that information and communication technologies

facilitate the improvement and efficiency of the quality of health care. A number of telemedicine

systems have addressed the needs of clients with DM. For example, the home care system for Type 1

diabetes clients was discussed by Bellazzi et al. with the goal of: (i) providing clients with an effective

insulin treatment, (ii) obtaining an appropriate level of continuous and intensive care at home through

tele-monitoring and tele-consultation services, (iii) allowing for cost-effective monitoring, (iv)

supporting continuing education of clients through tele-consultation(Bellazzi, Montani, Riva, and

Stefanelli, 01).

The Diabetes Education and Telemedicine (IDEATel) project was recently conducted to evaluate the

feasibility, acceptability, effectiveness, and cost-effectiveness of telemedicine. The focal point of this

intervention was the home telemedicine unit, which provided four functions: (i) synchronous video-

conferencing over standard telephone lines, (ii) electronic transmission for glucose and blood pressure

readings, (iii) secure Web-based messaging and clinical data review, and (iv) access to Web-based

educational materials(Starren et al. 02).

A randomized control study was conducted by Montori et al. to determine the efficacy of telecare

(modem transmission of glucometer data followed by nurse-mediated feedback and clinician feedback)

11


to support intensive insulin therapy in 31 clients with Type 1 diabetes and inadequate glycemic control.

The results indicated that telecare which augmented usual care among clients with Type 1 diabetes and

inadequate glycemic control had a small effect on glycemic control compared with the transmission of

glucometer data without feedback in the context of usual care(Montori et al. 04).

2.2.2 eTools Designed to Improve Diabetes Mellitus Management

Chronic disease management programs that use Web-based communication offer an opportunity to

shift the focus of health care away from clinicians’ offices and towards clients’ daily lives at

home(Ralston, Revere, Robins, and Goldberg, 04).

A study conducted in 1998 by Smith et al. first introduced the concept of applying electronic

management tools to improve the management of diabetes mellitus. Smith et al. measured the number

of exams, such as foot, blood pressure, and glycated hemoglobins documented by providers using an

electronic management tool. This study indicated the number of foot exams and blood pressure

readings were greater (P < 0.01) for providers using the system(Smith et al. 98).

Two additional papers describe a similar approach. Meigs et al. (Meigs et al. 03) reported on a

randomized controlled trial conducted to assess the impact of a Web-based EMR system on quality of

diabetes care at a teaching practice for outpatients. Physicians using the EMR ordered significantly

more HbA(1c) and LDL cholesterol tests for diabetes patients. Because of this, the authors concluded

that EMR use led to better quality of diabetes care. Another controlled study of the impact of an EMR

system on outpatient diabetes outcomes also found increased rates of test ordering such as HbA(1c),

lipids, microalbuminuria, or blood pressure levels(Montori et al. 02).

A study of the implementation of a Web-based diabetes care management support system was carried

out in the Henry Ford Health System by Baker et al.(Baker, Lafata, Ward, Whitehouse, and Divine,

01). The study included 13,325 diabetes patients aligned to 190 primary care providers practicing in 31

primary care clinics. Three features were provided as part of the support system: clinical practice

guidelines, patient registries, and performance reports. The application was available via a corporate

intranet. Because it was hosted on an intranet instead of the Internet, patients were not able to access

their records from home, but had to be on site to get access. This study documented the frequency with

which clinical benchmarks testing was performed, treating this frequency data as its outcome measure.

The results indicated an increase in the frequency with which patients received lipid profile testing and

12


retinal exams if their tests were ordered by physicians who used the system more often. The authors

concluded that frequent use of the system by caregivers improved the rate of routine testing among

patients with diabetes. No relationship was found, however, between system usage and glycated

hemoglobin testing.

As O'Connor pointed out, there is a drawback relating to studies like those above, “Increased test

frequency did not translate into better A1C or LDL levels in the patients of physicians with access to

the EMRs compared with patients of physicians without access to the EMRs…While it is encouraging

that EMR use led to increased frequency of testing, it is disappointing that key care outcomes such as

A1C and LDL levels did not improve” (O'Connor, 03). Thus, it deviates from the principle of diabetes

management because it only measures the test rate instead of measuring the improvement in metabolic

parameters levels.

Conversely, a randomized clinical trial involving 110 patients in Korea demonstrated the impact of

using the Internet to improve blood glucose monitoring. With 12-week follow-up examination,

HbA(1c) levels were significantly decreased from 7.59 to 6.94% in the intervention group (P < 0.001).

Moreover, HbA(1c) levels in the intervention group were significantly lower than in the control group

(6.94 vs. 7.62%; P < 0.001, respectively) (Kwon et al. 04).

Conducted at the University of Washington, Goldberg et al.’s study introduced a Web-based

application as a co-management module between physicians and their patients to improve the quality

of chronic disease care, which mainly focused on diabetes. In this Web-based disease management

program, the services included: (i) access to an electronic medical record (EMR) over the Internet, (ii)

secure email communication between doctors and clients, (iii) ability to upload blood glucose readings

via the phone line, (iv) an education site with endorsed content, and (v) an interactive online diary for

entering exercise, diet, and medication. Three pilot participants were in the study. One patient achieved

control (glycohemoglobin [HbA1c] from 8.0% to 6.1%) in diabetes management. While the result was

positive, the study was severely limited by the small sample size (Goldberg et al. 03). Based on the

same Web-based EHR application, Ralston and colleagues published a qualitative study of patients'

experiences with this diabetes support program. Six themes emerged, three of which had particular

relevance to the design and use of Web-based tools for care of patients with diabetes: 1) a feeling that

non-acute concerns were uniquely valued; 2) an enhanced sense of security about health and health

care; 3) frustration with unmet expectations. The study concluded that qualitative analysis supported

13


further study of open access to the EMR and online communication between clients and their

caregivers(Ralston et al. 04).

A more recent study conducted by Cho et al. in Korean found information technology-based diabetes

management system reduced complications on subjects with Type 2 Diabetes. They are mainly

“microangiopathic complications, including diabetic retinopathy, diabetic neuropathy, diabetic

nephropathy, and diabetic foot ulcer” (Cho et al. 08).

2.2.3 Linking Diabetes Guidelines to eTools

Some EHRs have integrated clinical guidelines, decision support and workflow into their systems

(Montori and Smith, 01) in the interest of promoting evidence-based practice. Barretto et al. report a

case study of guideline-compliant treatment of hypertension in diabetes (Barretto et al. 03).

Tang et al. report using a personalized portal combined with workflow management tools to improve

diabetes care. The Web service technologies applied Microsoft .Net Environment, plus MS SQL

Server as its backend. The results of this descriptive study were positive; however, no control group

was included(Tang, Li, Chang, and Chang, 03).

Plougmann et al. reported on a diabetes advisory system (DiasNet) implemented for communication

and education via the Internet between clinicians and patients. Patients could experiment with their

own data, adjusting insulin doses or meal sizes. The system was developed by Java based on a

client/server model (Plougmann, Hejlesen, and Cavan, 01).

Without discussing clinical benchmarks in the paper, De Clercq et al. reported on another Web-based

system for diabetes patients. The system had two main functionalities: 1) Both care providers and

patients could enter data; 2) The system downloaded the data from a glucose meter and provided

feedback to patients based on the data entered and incorporated guidelines(De Clercq, Hasman, and

Wolffenbuttel, 03).

The section following this one (Section 2.3) summarizes publications focusing on eTools and factors

associated with access.

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2.3 Electronic Tools: Components and Factors That Impact Access Prior to developing the DCSS, I reviewed the literature focusing on information-technology-based

chronic disease management tools associated specifically with the self-management of diabetes. In this

section, I summarize the components of existing eTools, and the research to date that has identified

factors that influence access, including patients’ views.

The table below summarizes the key elements of these electronic tools, both those that were included

and those that were missed (the latter most often being benchmarks for long-term diabetes

complication surveillance).

15


Table 1 Existing eTool Components and Access Indicators included in eTools Comments on Applications References

Home Telemedicine Unit: glucose meter,

automated BP meter.

Case Management Software (CommuniHealth

product by Siemens): patients’ view: glucose,

glycosylated hemoglobin, blood pressure, treatment

plan, weight, diet, and lipid levels; graphical

longitudinal displays.

This paper was based on a plan for

system development, but did not

involve a real implemented

application. It intended to create a

Web-based guideline messaging

system with videoconferencing.

It did not include benchmarks for

long-term complications such as: eye

and foot exam, albumin.

(Starren et al. 02)

Glucose, blood pressure, diet, weight This paper comes from the same

research group as the above (Starren

et al), and was based on same project.

Similarly, it did not include such long-

term complication benchmarks as

albumin, eye exam, foot exam.

(Shea et al. 02)

Lab: blood glucose, glycated hemoglobin, urinary

microalbumin, lipid profile (total cholesterol,

triglyceride, and HDL cholesterol);

Vital signs: Blood pressure (diastolic and systolic

pressure), foot exam, eye exam;

Lifestyle: smoking status;

Education: on diet & diabetes management

This study included most long-term

complication indicators but did not

include cardiac measurement.

(Smith et al. 98)

Frequency of foot/eye check, lipids exam;

Metabolic outcomes: HbA1c, microalbuminuria,

lipids (LDL cholesterol, total cholesterol,

triglycerides), blood pressure, exercise/smoke

advice, diet, immunization

This paper was published by same

research group as Smith (above). It

did not include a cardiac surveillance

indicator.

(Montori et al. 02)

HbA1c, LDL cholesterol, blood pressure, eye and

foot exam

This paper is based on third-party

software: COSTAR. For long-term

complication measurement, it did not

(Meigs et al. 03)

16


Indicators included in eTools Comments on Applications References

include albumin and cardiac indictors.

Glucose, HbA1c (glycated hemoglobin),

microalbumin, LDL, and retinal (eye/foot exam)

This study did not include blood

pressure and cardiac measurement for

long-term complications.

(Baker et al. 01)

Glucose meter date upload, Glycohemoglobin

[HbA1c], daily diary, clinical email.

This study did not include: lipids,

albumin, blood pressure, eye/foot

exam, cardiac measurement.

(Goldberg et al. 03)

Blood glucose, blood pressure or weight, drug

information (types and dosages of insulin, oral

antidiabetic medication), diet, exercise.

This study did not include: eye/foot

exam, cardiac, albumin measurement.

(Kwon et al. 04)

From information in the above table, I found that most eTools were for managing acute diabetes

complications. Some included long-term components, but since they were missing key benchmarks

they were not effective for long-term diabetes complications surveillance.

Patient's self-management is highly encouraged in chronic disease management. To optimize access to

eTools, some researchers have investigated the factors that impact patients’ use of IT when managing

chronic disease. It was found that some people are unwilling to use the Internet as a health tool for

several reasons(Gimenez-Perez et al. 02): “Lack of IT training; anxiety and stress; derived information

from different sources; lack of time; poor readability; concerns about quality of information; lack of a

specifically-designed and professionally-moderated Web page.”

Another study of an Internet-based diabetes management system found that proactive outreach and

patient tracking are critical success factors. Personalization is important for chronic disease

management, such as a self-management plan, as individuals are ultimately responsible for its success.

“Consequently, an Internet-based diabetes management system must allow patients to tailor the

intervention to their specific needs”(Mazzi and Kidd, 02).

Patients hold particular views about accessing their electronic records. These are important to consider

when designing electronic tools (like DCSS). Pyper and colleagues conducted a descriptive study to

discover patients’ attitudes to EHRs in a primary care setting in Oxfordshire, UK. The author analyzed

data by stratifying it by age and sex. Patients used private EHR viewing booths with a computer. The

17


results showed: 1) 86% thought that patients should have the right to see their records, 2) 72% knew

that they had the right to see their records, and 3) 4% had done so. Of the 100 patients who saw their

online EHR, 99% found the session useful and 84% found their records easy to understand(Pyper,

Amery, Watson, Crook, and Thomas, 02).

Honeyman’s study revealed “the interest and expectations of patients having access to their electronic

care records.” This qualitative study applied “semi-structured prospective interviews” in a community

setting in London. A booth was provided to patients to access their electronic records in the waiting

room. Overall, the results for this qualitative study were positive. “Patients were more interested in

seeing their electronic than their paper record; they felt it would improve their relationship with their

clinician; they generally trusted in the security of their records; they anticipated that there would be

some mistakes; they were enthusiastic about the idea of adding to the record themselves, but were

divided about having access over the Internet. Patients are confident in and anticipate the value of

having access to their electronic records”(Honeyman, Cox, and Fisher, 05).

Hassol’s study was based on a setting of “an integrated provider network located in 31 counties of

north central Pennsylvania” including clinic sites, hospitals, and 1.5 million outpatient visits per year.

The research group applied a commercial EHR from Epic Systems Corporation, called EpiCare. “The

application is Web based and it allows patients to view selected portions of their EHR.” It was a

descriptive study conducted over a six-month period. The author conducted “an online survey of active

MyChart users who had registered, [and] activated their account.” For the online survey, “responses

were based on a continuous scale, which ranged from 1 (hard to use or strongly disagree, depending on

the question) to 100 (easy to use or strongly agree)”(Hassol et al. 04).

2.4 Study Objectives Under the Chronic Care Model framework, a number of studies have attempted to illustrate its value in

improving chronic disease management. Several studies focused on DM, showing that CCM can

improve clinic outcomes and reduce costs(Bodenheimer et al. 02a;Glasgow et al. 01;Wagner et al.

01b;Warsi et al. 04). Providing appropriate information and teaching problem-solving skills are

important determinants of self-management in CCM(Bodenheimer et al. 02b).

There are numerous examples of the development of Web-based information technology for improving

diabetes management. Most of these studies have focused on the evaluation of clinical outcomes. A

18


few reported on the system development approach, such as the selection of patient demographic

indicators, clinical benchmarks, and disclosure of the development process.

In response to the self-management imperative, I developed the DCSS, embedding appropriate,

evidence-based information and data fields supported by research evidence into the tool. The literature

does not offer a self-management tool specifically designed for diabetes complication surveillance.

Therefore, the use of a Web-based electronic system to address the surveillance of the complications

per se has not been developed or piloted until this study. Thus, this study is novel and unique: first, it

presents an electronic tool for diabetes mellitus patients to assist with self-surveillance, including acute

and long-term complications. Second, the tool is predicated on the best available clinical research

evidence, the Canadian Diabetes Association’s Clinical Practice Guidelines.

19

Development of Electronic Tool “DCSS” by Shuo Wang Chapter 3 – Methods

CHAPTER 3: METHODS

This section describes the study design, the development process relating to the DCSS, and the project

timelines, as well as a modest pilot of the DCSS that administered survey questionnaires, recruited

participants, collected data, and measured outcomes.

I was responsible for all stages of this project. I communicated with doctors, nurses, patients, and IT

staff, and also provided consultation for the study design, questionnaire design, data analysis and

report writing. In addition, I worked on the tasks of preparing and distributing questionnaires,

recruiting patients, supporting patient Web access, and collecting, entering, analyzing and reporting

data. I translated the medical requirements into IT language and instructed developers to develop a

two-way, real-time, Web-based DCSS application. I also worked on the eTool implementation and

technical support, such as system installation, configuration, and maintenance.

3.1 Overview The DCSS is an electronic tool designed to facilitate the self-management of diabetes complication

surveillance. This electronic tool was designed for regularly monitoring key benchmarks for diabetes

complication surveillance. This is an important attribute of this electronic tool. As indicated in the

previous chapter, as controlling diabetes complications becomes more serious, key benchmarks should

be on target, e.g., periodic eye/foot checks should be done; lab results should remain under certain

recommended values. This eTool provides functions that monitor the process and the outcomes on

those benchmarks for various diabetes complications.

The DCSS prototype can display interactive patient-specific clinical data through the Internet. It is

possible for patients to access their diabetes medical information from anywhere and at any time. The

Website was developed as a demonstration site for the pilot study; it allowed patients to walk through

the application to get some experience. In the pilot study, some outpatients with diabetes were selected

for utilizing the DCSS and providing feedback via questionnaire. They were diagnosed as having

diabetes and had Internet access to the Web-based DCSS prototype.

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3.1.1 Timeline

In this project, the key steps included system design, development, implementation, evaluation, and

reporting writing. The key activities, milestones, and time frames are summarized in Table 2 below.

Subsections that follow the table describe each phase in detail.

Table 2 DCSS Project Timeline

Section Timeline Tasks 3.2 Initiation (Nov. – Dec. 2004) Project scope and IT approach 3.3 Month 1–6 (Jan. – Jun. 2005)

Development & Implementation (3.3) • Data fields definition (3.3.2~3.3.3) • System design (3.3.4) • Development / Implementation (3.3.5)

3.4 Month 7–12 (Jul. – Dec. 2005)

Pilot of DCSS (3.4) • Data collection (3.4.1~3.4.2) • Data analysis and knowledge dissemination (3.4.3~3.4.4)

3.2 Initiation During this period, through consulting with my study supervisor, I determined the topic of the project

based on the following information:

- What I had learned about the principles of health informatics, which includes applying IT in

health care to improve quality of care;

- My study supervisor’s expertise, and her opinion about the needs in clinical practice;

- Results of a literature review regarding a suitable topic within the eHealth field;

- The importance and value of a research topic dealing with one type of chronic disease.

Based on the purpose and goal of the study, I determined the scope of the project, which included

developing a Web-based eTool focusing on patients with diabetes complications, and carrying out a

pilot study of the eTool. The resources and budget needed to accomplish the project were also

considered. The timeline was determined as well.

3.3 Development and Implementation of Electronic Tool “DCSS”—Months 1–6 The DCSS provides patients with a live environment to apply a Web-based program to enhance the

self-surveillance of diabetes complications. The DCSS includes 9 evidence-informed clinical

benchmarks based on CPG as well as on consultations with clinical experts.

21


3.3.1 System Overview

The online DCSS prototype was designed and developed by medical and IT professionals. The DCSS

application worked as a Web-based portal that allowed health care providers and clients to access

patients’ information online. Medical information can be entered into the DCSS electronically from

home, lab, hospital, or point of care. Patients can view their information from multiple places (i.e.,

health care sites, including doctors’ offices and their own homes) at anytime.

3.3.2 User Profile Data Fields Selection

The data fields for patient demographic information were derived from the paper-based form records

that were used in the clinic. The author also made use of his other EMR application experience in

selecting the data fields. This demographic information conformed to the specifications of the Ontario

Provincial Enterprise Master Patient Index project for client registry data fields. Data fields like

medical records (ID) number, OHIP number, last name, first name, date of birth, address, and phone

number are the key patient identifiers in an EMR system.

3.3.3 Surveillance Benchmarks Selection

The essential benchmarks for the DCSS were identified from: 1) “Clinical Practice Guidelines [CPG]

2003,” published by Canadian Diabetes Association [CDA](Canadian Diabetes Association, 03); 2) a

literature review on diabetes(Votey and Peters, 05); 3) other eTool publications related to diabetes; and

4) the recommendations of clinical experts. Table 3 summarizes the benchmarks, their sources, and the

main rationale for their inclusion in the DCSS.

Table 3 Surveillance Benchmarks and Sources Benchmarks Sources (2) Rationale for Inclusion3 (4)

CPG DM Ref eTool Ref (3)

Expert

Acute Complication 1 HbA1c

(Hemoglobin A1C)

X X X X It is mainly used to track average blood glucose levels based on average lifetime of red blood cells which contain hemoglobin.

2 FBG (Fasting Blood Glucose)

X X X X It is a key indicator to monitor diabetes. Glucose accumulated in the blood will lead to various complications.

3 Lipids, LDL-C

X X X X High blood lipid levels lead to vascular damage, which will increase diabetes complications.

3 http://en.wikipedia.org/wiki/Diabetes_mellitus

22

http://en.wikipedia.org/wiki/Diabetes_mellitus


4 Body Mass

Index (BMI)(1)X X X Weight should be evaluated in relation to height,

rather than on its own.

5 Blood Pressure (Systolic / Diastolic )

X X X X Blood pressure control, and lifestyle factors such as maintaining a healthy body weight, may reduce the risk for most of the chronic complications.

Long-term Complication 6 Urinary

Albumin Excretion Rate

X X X X To detect a serious diabetes long-term complication, chronic renal failure

7 Eye Exam X X X X To monitor a serious diabetes long-term complication, retinal damage, which can lead to blindness

8 Feet Exam X X X X To monitor a serious diabetes long-term complication, microvascular damage; poor wound healing, particularly of the feet, can lead to gangrene.

9 Heart Exam (ECG)

X X To detect a serious diabetes long-term complication, cardiovascular disease

* notes:

(1). BMI (calculated by weight and height) is related to physical activity/exercise, which is indicated

by CPG, diabetes Ref.

(2). “X” above means that sources recommended this benchmark.

(3). For details, refer to Table 1.

(4). According to 2003 Clinical Practice Guidelines [CPG] published by Canadian Diabetes

Association [CDA] and related literature, to stop diabetes from progressing and prevent diabetes

complications, HbA1C, blood glucose, lipids, and BMI levels should be under the target values, and

the checking period should be on schedule. Comparing with an updated CPG from CDA, there is no

major difference between version 2008 and 2003, such as benchmarks, target values, and checking

period.

Details of Information Source

1. Clinic Practice Guide on Diabetes

Diabetes can cause many complications. To control diabetes complications, related indicators

to be monitored are: hypertension (blood pressure), hyperlipidemia (lipid, LDL, and HDL),

nephropathy (urinary albumin), neuropathy (10-g Semmes-Weinstein monofilament),

retinopathy, erectile dysfunction, and macrovascular complications (foot ulcers).

23


2. Literature on Diabetes

Search criteria: terms included diabetes mellitus, diabetes, diabetes complications, blood

glucose, HbA1c, nephropathy, neuropathy, retinopathy, diabetes control, hypertension,

cardiovascular disease. Language is English, without restriction on the timeframe.

According to “Diabetes Mellitus, Type 2—A Review”(Votey et al. 05), indicators to be

monitored are: blood glucose, HbA1c, diet or exercise. Microvascular complications include:

retinopathy (eye examination) and nephropathy (urine protein and serum creatinine).

Macrovascular complications include: hypertension (BP, ACE taken), coronary artery disease

(ECG), peripheral vascular disease, cerebrovascular disease (transient ischemic attack), and

hyperlipidemia (lipid levels). Other complications includes: neuropathy, diabetic foot (foot

ulcers). Physical indicators include: vital signs, funduscopic examination, and foot

examination.

3. Literature on eTool -related Diabetes

Another source for determining indicators is other publications that are relevant to using eTools

for managing diabetes (see Table 1). Through reviewing those papers which discussed using

EMRs in managing diabetes, I determined what indicators other researchers had employed in

their applications. Since those indicators should be useful in managing DM, I included them in

our application as well.

4. Consultation with Clinical Experts

Having identified a number of diabetes complication surveillance indicators through the criteria

noted above, I asked four clinical experts (mentioned in Acknowledgement section, mainly

nephrology specialists) to provide feedback regarding the indicators they preferred for diabetes

self-management and complication surveillance.

All of this yielded three criteria for selecting indicators to include in the DCSS: frequency of

use in the field, as documented by the literature and the clinical practice guide; suggestions

from the experts I had consulted; and suitability for patients’ self-monitoring at home by

themselves.

24


3.3.4 System Design

To build this Web-based DCSS, two major design steps were taken. The first, the medical design step,

was to select key medical benchmarks which can represent the status and progress of diabetes, as well

as indicate the risk of diabetes complications. This step also involved designing flow charts for the

DCSS. The second, technological step was to choose an IT approach that could develop such an

application within budget.

The system design had to be patient-oriented. Applying the theory from Knowledge Media Design, the

principal investigator combined the concepts of human and machine, society and technique in

developing this application to be used for diabetes patients and providers. Because most of the diabetes

patients were elderly people, the design process had to consider their special requirements. For

instance, the font should be large enough, and the layout should be simple and follow a simple

business flow (unlike some media Websites which have lots of fancy stuff that could confuse users).

3.3.5 DCSS Development / Implementation

1) Steps First of all, I established a system development environment. It included installation and configuration

of hardware, network, and software. I also needed to configure the Web server [Tomcat5.0], database,

ODBC [Open Database Connectivity], and network TCP/IP.

Prior to the programming, I identified the application requirements. It included Web page layouts,

workflow from one page to another, and the linkage between tables in the database.

During the programming stages, following the technical design, the development team created each

Web page under the DCSS prototype. After I got the prototype, I reviewed it and discussed if anything

needed to be improved. If I was satisfied with the application based on the functional requirements, I

finalized the application. During the testing that followed, I found certain minor problems in the

application, and then I requested that the developer solve the problems and ensure the system worked

properly. After repeating these debugging processes several times, I concluded that the system was

okay to use.

After I got the completed application, I implemented it on another testing server where the Internet was

accessible. Later, I provided this application to pilot study users.

25


Figure 1 Business Logic Workflow

Application Development

system requirements

software requirements

preliminary design

detailed design

code and debug

test and pre-operations

operations and maintenance

There were other important aspects considered within the IT scope:

• Advanced information technology was used for the system development. System performance,

capability and scalability were determined. To get the optimal performance, I considered all

related factors, such as hardware device (server, network connection, etc), software (operating

system, database system, etc), Web server, back-end and front-end programming languages,

database architecture, system workflow, etc.

• Security and privacy were critically considered with reference to the Personal Health

Information Protection Act 2004 by the Ontario Ministry of Health and Long-Term Care4—for

instance, who can access what.

• System user interface design: layout, button, text, font size, etc. This design focused on the

system's usability from the perspective of the seniors who would make up the bulk of the

research subjects.

Through analyzing the scope of the project, I determined the content of the DCSS Website and

programming architecture. An outline detailing the creative content of each section of the Website was

developed, along with a comprehensive site flow chart mapping out the relationship and links required

4 Health Information Protection Act, 2004 http://www.health.gov.on.ca/english/providers/legislation/priv_legislation/priv_legislation.html

26

http://www.health.gov.on.ca/english/providers/legislation/priv_legislation/priv_legislation.html


in the initial roll-out. Before programming, I reviewed the outline and the flow chart. Upon the

completion of the first cycle of development, I tested the application and communicated our comments

to developers for debugging. Finally, when I was satisfied with the application, I implemented the

application and finalized the deployment.

To illustrate how the system works, artificial patient records were presented on the demo Website. The

Website could be accessed through the Internet from anywhere via a Web browser that used HTTP

protocol, i.e., Internet Explore [IE]. In my pilot study, I recruited 12 outpatients with diabetes receiving

care from a hemodialysis unit and diabetes clinic at a university-based facility, St. Michael’s Hospital

in Toronto, Ontario. These patients were diagnosed as having diabetes with related symptoms and

laboratory test results. Each patient was given a unique login ID and password to access the demo

Website and get experience using the entire application.

2) DCSS Technical Specification System capacity, network protocol, and future scalability were carefully considered in the project.

Computer Hardware

• I selected a Dell computer system to host the Website after comparing the price and

performance of different brand names, i.e., Dell, IBM, and HP. After testing, I confirmed

that the Central Processing Unit [CPU], memory, and hard drive of a Dell machine were

sufficient to process the data access.

Computer Software

• Operating system: Microsoft Windows Server 2000 was used to host the Website.

• Database: Microsoft Access 2000 was selected to store the surveillance medical benchmark

data.

• Development software: HomeSite 4.5 for programming, and PhotoShop 6.0 for image

processing.

• Other software, i.e., File Transfer Protocol [FTP], was used for file uploading and

downloading if working remotely.

Network

27


• I managed the Internet connectivity independently via a Netgear router as a firewall device.

It was installed to protect the system by port filtering.

• I used Rogers Cable Network for Wide-Area Network (WAN) access.

• To ensure the security of Internet data transfer, I closed all other ports, only opening the

port for Hypertext Transfer Protocol [HTTP].

Other Devices:

• Uninterrupted Power Supply [UPS] and external hard drive for data backup were

implemented as well.

Programming Language

• Java, a new generation Web-based programming language, was used for this Internet-based

application development. The Web pages are called Java Server Pages [JSP].

Web Server

• Jakarta-Tomcat-5.0.12

Backup Plan

• A sc

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