Investigating Discrete Event Simulation Method to Assess the Effectiveness of Wearable Health...

Procedia Economics and Finance 11 ( 2014 ) 838 – 856

2212-5671 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.doi: 10.1016/S2212-5671(14)00248-2

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Symbiosis Institute of Management Studies Annual Research Conference (SIMSARC13)

Investigating Discrete Event Simulation Method to Assess the Effectiveness of Wearable Health Monitoring Devices

Srinivasan Radhakrishnana*, Arjun Duvvurub, Sagar V. Kamarthic aAcademic Research Associate, Symbiosis Institute of Management Studies, Symbiosis International University, Pune

bQuality Engineer at JDA Software Private Limited

cDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, USA

Abstract

The current primary care units of the US healthcare system are strained by supply-demand deficiency. Last 12 years have seen a

decline in number of US medical students opting for primary care after their medical education. Research studies have

recommended solutions targeting, primary care payment reform, investment in primary care infrastructure and organization, and

attracting more US medical students to primary care. Such reforms demand expensive implementation plans and might take years

to yield benefits. Meanwhile, the generation of baby boomers and the healthcare reform under current US administration

(insuring additional 33-43 million patients) may stress out the primary care providers in near future. In this paper, we investigate

the usefulness of wearable health monitoring devices, to alleviate primary care patient load. To conduct our study, we adopted

the discrete event simulation method to simulate the proposed model and analyze the behaviour of the same in terms of

performance measures. We simulated three scenarios with different vital sign measurement time and physician consultation time.

In addition we varied the percentage of patient population adopting the wearable device health monitoring (WHMS) program. In

all the three scenarios, the WHMS system yielded beneficial results in absorbing more patients and reducing patient denials than

the current primary care system irrespective of adoption percentages. However the average patient wait times were sensitive to

the adoption percentages. On the whole the simulation results indicated that wearable health monitoring devices could act as a

potential solution in reducing the patient load across the primary care system.

© 2013 The Authors. Published by Elsevier B.V.

* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected]

© 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.

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839 Srinivasan Radhakrishnan et al. / Procedia Economics and Finance 11 ( 2014 ) 838 – 856

Keywords: operations research, discrete event simulation, primary health care, wearable devices

1. Overview

The current US primary care is facing issues on three fronts: (1) decrease in number of primary care physicians, (2)

uneven distribution of primary care practitioners, and (3) difficulty in accessing primary care. In addition, the

generation of baby boomers and the healthcare reform under current US administration (insuring additional 33-43

million patients) might stress out the primary care providers in the near future. The failure to address the above

mentioned issues might cripple and render a primary care system incapable of absorbing excessive patient load.

Apart from the excessive patient load in primary care settings, there is the problem of communication across

different care units. Poor communication eventually leads to poor patient satisfaction and unnecessary expenditure.

Inefficient communication between primary care physicians and specialist, primary care physicians and ED,

physicians and source of diagnostic data, and hospital-based physicians and primary care physicians is elevating the

problems in addition to uneven load distribution.

The objective of this paper is to assess the effectiveness of wearable health monitoring devices in reducing the

primary care patient load and improve the communication across various healthcare units. The discrete event

simulation (DEs) method forms the backbone of this study, helping us achieve the objective. We initiate the paper

by highlighting the current problems faced by the US primary care setting. In the subsequent steps we propose the

wearable health monitoring system (WHMS)model for the primary care followed by a careful selection of target

patients and performance measures for the discrete event simulation. In the simulation we create the patient flow

sequence and study the behavior of both the current primary care system and the proposed WHMS primary care

model in terms of performance measures. The final part of the paper deals with the simulation results, discussion

and the future scope of the work.

In the following sections we will start with the discussion of the current primary care problems, identify the

requirements for the solution space, and introduce the proposed WHMS primary care model.

2. Background

In the US, the primary care responsibilities are distributed over several categories of practitioners: family

physicians, general internists, geriatricians, general paediatricians, nurse practitioners, and physician assistants [1].

These individuals are grouped under a class termed, primary care practitioners. Nurses, pharmacists, health

educators, medical assistants, and other health workers form an additive layer to the primary care practitioner base

[1]. The primary care clinics are usually located in the outpatient division of teaching hospitals (urban areas) and the

community health centers [1]. Community health centers are public or non-profit organizations that serve low-


income and minority populations [1]. In this section we will take a look at the current US primary care, which is

facing issues on three fronts: (1) decrease in number of primary care physicians, (2) uneven distribution of primary

care practitioners, and (3) difficulty in accessing primary care.

3. Declining Primary Care Physicians

The mid-1990s saw a rise in health maintenance organizations (HMOs) who based their care structure on the

primary care as the initial entry point for patients entering the healthcare system [1]. The 90’s experienced an

increase in primary care physicians from 67 to 90 for every 100,000 people and the percentage of graduating US

medical students pursing primary care careers grew from 15% in 1992 to 40% in 1997 [1, 2]. The widening income

disparity between primary care physicians and specialists, coupled with increasing clinical workload led to the

decline in number of US medical students opting for primary care careers [1, 3]. In 2005, approximately 300,000

physicians, and another 100,000 nurse practitioners (NP) and physician assistants (PA) provided medical care in the

US [1]. Although these figures may seem impressive, they do little to solve the primary care crisis especially when

one considers the current and the future state of the primary care. Population growth and aging population are

estimated to increase the workload of adult primary care practitioners by 29% from 2005 to 2025, and the number of

adult primary care practitioners is estimated to grow by only 2%–7% [1]. The estimated shortage of adult primary

care practitioners is projected to be in the range 35,000 - 44,000 [1]. Including nurse practitioners and physician

assistants, the ratio of primary care practitioners to population is estimated to decline by 9% from 2005 to 2020 [1].

Therefore it is important to create new strategies to ensure availability of adequate primary care practitioners in

order to address the existing and the future primary care patient demands.

Uneven distribution of primary care practitioners: The uneven distribution of the primary care physicians (100

PCPs: 100,000 urban population and 46 PCPs: 100,000 rural population) means that 21% of the US rural population

is serviced by 10% of primary care physicians [1]. The areas where population to PCP ratio exceeds 2000:1 are

designated as primary care health professional shortage area by federal Health Resources and Services

Administration (HRSA) [1]. As of 2009, 65 million people live in these shortage areas. In short, the primary care

part of the healthcare system is facing issues regarding declining PCPs, uneven distribution of PCPs and

inaccessibility. We have outlined the existing problems faced by the primary care, but that alone is not sufficient to

solve their problems. In addition to the above mentioned problems, we must understand the stress involved in other

components of the healthcare system. The following section looks into the uneven distribution of the patient load

across the healthcare system and how they create additional problems for the primary care.

4. Need for a Patient Load Sharing System

The complexity of the current US healthcare system inhibits the ability to design an entire new system. The most

feasible solutions are those that can be injected into the current healthcare system in order to boost its efficiency,


efficacy, and quality. The wearable health monitoring devices offer a promise solution. In this research we plan to

investigate a framework with wearable health monitoring devices and their diffusion into current healthcare system.

The primary objective of our work is to discover the benefits of wearable devices, if any, to understand the primary

care system using simulation models. Before proceeding further we need to understand the patient flow through the

existing healthcare system. Theoretically, the primary care is the initial entry point for a patient in the health care

system except in case of emergencies. The primary care providers perform preliminary health checkup, short term

treatment, and establish a patient medical record. For above mentioned functions a hospital admission is not

necessary and the care is delivered on an outpatient basis (termed as Ambulatory Care). In case of complex

abnormalities the patients are referred to a specialist by the primary care physician (PCP). Thus the PCP’s form the

link between the patients and the specialists.

5. Patient Load Problem

Although the largest proportion of ambulatory care services occurs in physician offices, approximately 11% of all

ambulatory medical care visits in the US occur in the emergency department (ED). From 1996 through 2006 the

number of ED visits increased by 32% (from 90.3 million to 119.2 million) and the number of hospital ED

decreased from 4019 to 3833[4]. Other studies acknowledged the cause of excessive ED load and long primary care

patient wait time [5], to patient groups such as low income groups who were offered health care via ED [6] and

Medicare beneficiaries who couldn’t access the primary care [7]. Thus the US healthcare ED which forms one of the

important components of healthcare system is overburdened due to a significant gap between the supply and

demand. Moreover, recent studies have indicated that the number of visits to specialists exceeded the number of

visits to PCPs, particularly among the elderly, highlighting the fact that the trend of excessive load is creeping into

specialty care also [8]. The stressed up ED and specialty care providers initiates the concept of load distribution,

especially redirecting the unnecessary visits to the primary care hospitals. This concept of redirecting patients is

rightly brought out by Yaneer Bar-Yam [9], who proposed the separation of simple care (vaccination, health

screening, and healthy-habits counselling) from that of the complex care. He also highlighted the fact that the

existing system is inefficient to handle large volume of preventive care; he suggested that the doctors must

concentrate on what they are good at-complex care [9]. This calls for additional responsibilities from the primary

care sector, but the redistribution of the load over primary care providers is easier said than done. As discussed in

the earlier section, the primary care has its own share of problems. The generation of baby boomers and the

healthcare reform under current US administration (insuring additional 33 million patients) will stress out the

primary care providers in near future. Bodenheimer, et al. [10] suggested remedies to avert the declining size of the

PCP’s: concentrate on primary care payment reform, invest in primary care infrastructure and organization, and

attract more US medical students to primary care. It may take years to arrive at agreeable changes to the primary

care, implement the reforms, and to determine the outcome of the reforms, but we need a solution to address the

existing primary care patient load at the current ling.


6. Communication Problems in Care Settings

Apart from excessive patient load in primary care settings, there is the problem of communication across different

care units. Poor communication eventually leads to poor patient satisfaction and unnecessary expenditure.

Inefficient communication between primary care physicians and specialist, primary care physicians and ED,

physicians and source of diagnostic data, and hospital-based physicians and primary care physicians is elevating the

problems in addition to uneven load distribution. Thomas Bodenheimer [11], reported that the communication

problems stems from lack of interoperable computerized records and lack of integrated systems of care. His studies

also highlighted the dysfunctional communication system between providers, patients and their families. The

solution space for tackling the above mentioned issues must contain a patient load sharing mechanism backed up by

strong information support system.

Solution Space: Our proposed model (see Figure 1) addresses the load sharing issue by incorporating wearable

devices for health monitoring to existing primary care setting. This will enable the patients to use wearable devices

at home and communicate the quantitative vital signs data (heart beat, blood pressure, respiration, etc.) and

qualitative data (sleeplessness, skin discoloration, etc.) via internet to a data analysis center which in turn will send

the results to the primary care provider. The data analysis center is responsible for interpreting, analyzing and

visualizing the medical data; it is also responsible for detecting incidences and abnormalities, creating health reports,

and publishing and updating health records which are compatible across all platforms. The presence of Common

Medical Records (CMR) will help us address the communication problems that exist across various components of

the healthcare system. The proposed model ensures participation of primary care physicians, NPs and MAs who

have the sole authority to take individual case-based decisions. By taking care of the low priority tasks, the data

analysis center eliminates the time wasted by primary care physicians on low priority tasks and thereby enables the

providers to absorb more patients. More importantly it advocates and enables the concept of home-monitoring and

preventive care which is regarded by many as one of the important area of focus for improving healthcare.

7. Shifting the Health Monitoring base to Home

The proposed model indicates a partial health monitoring responsibility shift towards patients (in-home). This raises

several important questions regarding home monitoring, especially concerning, data reliability, incidence detection,

patient adherence, and patient satisfaction. In this section we will try to outline several research works conducted

previously to address the above mentioned issues.

The medical devices in case of telemonitoring generate streams of data. Several studies have revealed good accuracy

and reliability of the reported medical data with minimal data transfer problems and errors, in case of chronic illness

[12-20]. Other studies have revealed the close agreement of the data collected from home monitoring using wearable

devices with that of laboratory results [21, 22]. Such studies boost the confidence on the wearable health monitoring


Figure1: Existing primary care system vs. proposed primary care system

devices which forms a crucial component of our proposed model. The ability of telemonitoring to identify early

changes in health conditions related to patients suffering from pulmonary abnormalities [23 - 26], and diabetes [27-

30] have increased the confidence on the efficacy of telemonitoring. However, the relation between early detection

and its effects on reducing the number of complications is not firmly established [12, 31], and only future research

will helps us understand this relation much better. Additionally, several studies on pulmonary conditions and

diabetes have demonstrated high patient acceptance and satisfaction with various telemonitoring systems and

procedures [12] and high compliance with telemonitoring programs were observed in case of cardiac and

hypertensive patients [32, 33]. On other hand, there were several instances that reflected decrease in patient

adherence with respect to time [34, 35]. Though the patient satisfaction and adherence to telemonitoring procedures

are polarized, we must consider the positive effects and try to implement novel systems directly involve of patients

in the care process. Ryan, et al. [36] and Piette, et al. [37] report that the direct involvement of patients empower


them with knowledge and awareness regarding their medical condition; we argue that telemonitoring helps patients

achieve the same. Studies have shown that the benefits of the telemonitoring are not limited to the patients;

telemonitoring could also significantly decrease in-hospital admissions, emergency department visits, and hospital

length of stay in case of patients suffering from cardiac and pulmonary abnormalities [38, 39]. This is an

encouragement for researchers to further study systems involving home monitoring and analyze their effects on

patients who take part in the system and also on the healthcare providers overall. To sum up all, the proposed model

incorporates wearable device technology and information system to address the challenges of patient load and care

setting communication that exists in current primary care system, achieving true integration of in-home monitoring,

customized care and smart healthcare system. Currently the wearable device studies are widely implemented as

hospital programs to ease the burden on the emergency department. The success or failure of such programs can be

observed only after their implementation, making it a risky approach. The use of simulation method to assess the

effectiveness of the wearable devices is a unique approach. This approach will enable the hospitals to design and

simulate various wearable device related programs for both inpatient and outpatient setting and obtain results

without implementation, thereby saving time and costs. In this paper we limit our scope to primary care unit and use

simulation method to assess the effectiveness of the wearable device in reducing patient load. In the following

sections we will discuss the classification of wearable devices, basic wearable device architecture and explain the

simulation method, simulation results followed by discussion and future work.

8. Classification of Wearable Devices

Raskovic, et al.[40] formed two groups to introduce the taxonomy of personal health monitoring devices: usage, and

implementation. The usage group was further classified according to the wearability of the devices

aswornorimplanted. Recording, processing, correcting and replacing or combination of these capabilities form the

core objectives for both worn and implanted sensors[40].Devices that send or store relevant signals and data from

the patient, but do not evaluate the signals to provide feedbacks to the patient are termed recording devices[40].The

recording devices cannot be classified as wearable computational devices due to their limited computational

capabilities[40]. Processing devices are those devices that process relevant information and provide immediate or

conscious feedback to the patients regarding their health condition[40]. Processing devices may or may not provide

continuous feedback. For example ECG device provides alert only if there is any irregular cardiac events[40].

Correcting devices act on malfunctioning organs in order to enhance organ functioning (eg. hearing aid) and in

contrast replacing devices substitute for failed organs (eg. artificial heart)[40]. Wearable devices falling under the

category of worn are used external to the body either embedded in clothes or attached as accessory[40]. Implanted

devices are usually surgically inserted inside the body or consumed orally (eg.Endoscopy capsule)[40].

Raskovic, et al. [40]represented the taxonomy of implementation with two axis. One axis dealt with patient mobility

while the other axis represented gateway ability. The patient mobility can be static or moving and the gateway

ability can be local or global[40]. It is important to understand the patient mobility state. Inside the hospital the


patient can be in non-mobile state or ambulatory state. The design criteria for wearable sensor devices can change

just by knowing the mobility state of the patient. One can design heavy, reliable, cable electricity, high

computational power devices if the patient is static. On the other hand the design dynamics change to light weight,

reliable, battery operated, lesser computational ability, wireless devices if the patient is in ambulatory condition. The

gateway ability is the access point to connect to the medical network in order to obtain relevant information [40].

Satellites are an example for global gatewaypoints while in-city health kiosks represent local

gatewaypoints[40].From the above described broad classification of wearable health monitoring devices, we now

move on to the generic architecture of the same.

9. General Architecture of Wearable Devices

Wearable Health Monitoring Systems (WHMS) have seen increased research and development efforts in the last

decade [41-44]. The research has proliferated in both sensor design and its data transmission network. Figure 2

depicts the Generic WHMS architecture, as stated by Pantelopoulos, et al.[45, 46].The Biosensors form the front end

of the WHMS pipeline. Sensing appropriate bio signals are their main function. These biosensors can be embedded

in textiles or worn on body[45, 46]. The biosensors send data wirelessly through Bluetooth-enabled or Zigbee motes

channel[45, 46]. This form of data transfer network is termed as Body Area Network (BAN). Filtering,amplifying

and other AD conversion forms the basic signal processing steps that are performed by a dedicated hardware

attached to the sensor or by the central node (see Figure 2). The central node which can be a portable PDA, portable

computer or smart phone is responsible for tasks such as handling the communication of the biosensors, performing

additional data processing, verifying and validating the data, comparing the extracted features or values from each

signal with the thresholds, generating alarm signals, displaying real time data on GUI and transmitting the processed

data to the database server, hospitals or the physicians cell phone[45, 46]. It is to be noted that the existing WHMS

framework incorporates both quantitative (heartbeat rate, blood oxygen level, respiration rate etc.) and qualitative

data (sleeping condition, body pain, skin discoloration etc.). In case of the qualitative data there is a need for verbal

interactions with the patient[45, 46]. The basic types of bio-signal sensed are, ECG (electrocardiogram), blood

glucose, respiration rate, blood pressure, heart rate, heart sound and body movements. The respective types of

sensors used for the above mentioned signals are skin/chest electrodes, strip-based glucose meter,

piezoelectric/piezoresistive sensors, arm-cuff monitor, pulse oximeter, phonocardiograph and, accelerometer. For

further classification of types of signals, sensors and data measured one can refer to Pantelopoulos, et al.[45, 46].

10. Applying Discrete Event Simulation to Wearable Devices Framework

Before moving on to the discrete event simulation (DEs) we will discuss about the limitations of DEs and suggest

alternate simulation method that will enable to analyze the behavior of the WHMS primary care model on whole.

The key performance measures required for assessing the behavior of the WHMS primary care model are, primary

care patient wait time (WT), patient satisfaction, patient population absorbed by the primary care, number of patients


Figure 2: Generic WHMS architecture [20, 21]

denied primary care, and patient visits per episode. The patient satisfaction and patient visits per episode represents

entity behavior i.e. behavior based on individual patients. The entity level behavior cannot be captured using

discrete event simulation method. The entity level behavior can be captured by adopting agent-based simulation

method. In this paper we concentrate on primary care patient wait time (WT), patient population absorbed by the

primary care, and number of patients denied primary care which represents event level behavior. The event level

behavior can be captured by discrete event simulation method. We have used discrete event simulation method to

simulate and analyze the behavior of our proposed model in terms of performance measures. The following section

deals with identification of the patient categories and selection of performance measures for the simulation.

11. Patient Categories

UpkarVarshney [47] divided the healthcare applications among the following categories: prevention, healthcare

maintenance and checkups, short-term monitoring (or home monitoring), long-term monitoring (nursing home),

personalized healthcare monitoring, incidence detection and management, and, emergency intervention,


transportation and treatment. The important prerequisite for studying WHMS framework is to identify the category

of the patients. We use these categories as suggested by UpkarVarshney [47] and accommodate them into two broad

categories of patients; (1) Preventive care patients who receive prevention, healthcare maintenance and checkups,

short-term monitoring or home healthcare monitoring, personalized healthcare monitoring, incidence detection and

management; and (2) Post-treatment patients who have been recently operated on, surgically or non-surgically, and

are in their recovery phase, patients with need for long-term in-hospital monitoring. In this study we will

concentrate on preventive care patients and study the effects of wearable devices when used for pervasive health

care.

12. Selecting Performance Measures

Quality indicators are the explicitly defined measurable indicators that describe the structures, processes, or

outcomes of care [48, 49].Activity indicators measure the frequency of events (e.g., flu vaccination

frequency)[48].Quality indicators infer a judgment about the quality of care provided and performance indicators

are statistical devices for monitoring performance (such as use of resources) without any necessary inference about

quality[48, 50]. Indicators do not provide definitive answers but indicate potential problems or quality of care[48].

Though most indicators are developed for in-hospital use, the primary care is witnessing an increase in the use of

indicators[48].Usually quality indicators are developed using non-systematic research methods or systematic

evidence based methods[48]. The evidence based methods rely on evidence alongside the expert’s opinion. But due

to difference in interpretation of the evidence, various consensus methods have been developed to address this

issue[48, 49-54]. The quality measures developed by consensus techniques are subjected to, acceptability,

feasibility, reliability, sensitivity to change, and validity[48]. Before developing indicators, one needs to identify;

Structures (staff, equipment, appointment systems, etc), Process (such as prescribing, investigation, patient-

personnel interaction) and Outcomes (such as mortality, patient satisfaction)[48]. In this paper we wish to

investigate the effects of proposed model on the structures and outcomes. The factors affecting the performance

indicators are displayed in Table 1. The performance measures/indicators selected for the current model are, primary

care patient wait time (WT), patient population absorbed by the primary care, and number of patients denied primary

care.

13. Simulation

The simulation model consists of two environments; current primary care setting, and new primary care setting that

use wearable health monitoring system (WHMS). The snapshot of the discrete event simulation (DEs) model is

shown in Figure 3. The simulation model has the following elements; patient generator, filtering mechanism, patient

admission, vital signs measurement, and physician consultation. The patient generator is responsible for generating

scheduled and unscheduled patients. The scheduled patients are those who have already secured an appointment for

a particular day. The unscheduled patients are those who turn up every day, and seek appointment for that particular


Table1: Selected performance measures/indicators

day they need care. The filtering mechanism sends scheduled patients into the primary care system. A percentage of

scheduled patients cancel their appointments, and those slots are given to unscheduled patients who seek

appointment for that day. In addition the filtering mechanism sends unscheduled patients into the system if the

physician is free.

For the simplification of the model we have assumed that the care provider is notified beforehand about the

cancellation of certain scheduled patients and the unscheduled patients are given the cancelled slots. Once the

patients enter the primary care setting, they are subjected to admission procedure, vital sign measurement and

eventually sent to physician consultation. For both the primary care settings patient arrivals were modelled as

Poisson processes, where the number of patients arriving per unit time is described by Poisson distribution. Because

patient arrivals are probabilistic in nature and independent of each other, a Poisson distribution can be used to

describe the events (patient arrivals) that occur during a period of time [55]. A uniform random distribution was

used to model the probability of scheduled patients cancelling appointments. The time a patient spends at each stage

in the primary care settings: admission, vital signs and physician consultation was assumed to follow an exponential

distribution. An exponential distribution is an appropriate choice as it models the time elapsed between events

(Example: customer enters admission process, leaves admission process) very well in a statistical context [55]. For

the WHMS setting the time spent at each stage was also assumed to be exponential, but the mean value of time was

set to a lower value than its counterpart. As stated in the previous sections, the effect of WHMS on time spent at

each stage of length of stay is not known. So a sensitivity analysis was performed by decreasing in steps the mean

Performance Indicators Measurable Influencing Factors Non-measurable Influencing

Factors

• Primary Care Patient

Wait Time (WT)

• Patient Population

Absorbed by Primary

Care

• Number of Patients

denied care due to lack

of care unavailability

• Admission time

• Initial medical data collection

(Vital Signs) time

• Patient waiting for primary care

physician (Wait Time)

• Patient-Doctor consultation time

(Visit Time)

• Percentage of population

adopting Wearable device based

health monitoring system

• Wearable Health

Monitoring Device

Technology

Development


values of time in spent at each stage and comparing the results with those of the current primary care setting model.

The results of the simulation are discussed in the following section.

Figure 3: Snapshot of the discrete event simulation model built using Arena


14. Results and Discussion

The y-axis of Figure 4 shows the average patient wait time in minutes and the x-axis shows the percentage of patient

population adopting WHMS model. In addition, Figure 4 compares three scenarios by varying the vitals

measurement time and the physician consultation time. The vital 5-5 and physician 10-5 as indicated in Figure 4

represent a normal distribution (vital 5-5 mean-5 minutes and SD-5 minutes). The same convention is used in case

of Figure 5 (number of patients denied care) and Figure 6 (number of patients absorbed by the system). Scenario 1

represents vital5-5 and physician 10-5, scenario 2 represents vital 10-5 and physician 15-5, and scenario 3 represents

vital 15-5 and physician 20-5.

Figure 4: Average patient wait time

The factors that promoted the reduction in the average wait time were the time taken for vital sign measurement, the

physician consultation time, and the percentage of population that adopted the WHMS model. The average wait time

for current primary care system was 30 minutes. The scenario 1 yielded better results than scenario 2 and scenario 3

concerning the reduction in the average wait time across all percentage population of patient adoption. Comparing

the current primary care system and 80%-100% population adoption rate, scenario 1 and scenario 2 yielded average

patient wait time of 6 and 16 minutes respectively. On the other hand for 80%-100% adoption rate, the scenario 3

05

1015202530354045

Avg.

patie

nt w

ait t

ime

(min

s)

% Population adopting WHMS model

Avg.Patient Wait Time

Vital 5-5 and Physician 10-5




yielded same value for average patient wait time as the current primary care system. Overall, scenario 3 yielded

higher average patient wait time than the current system across all the adoption percentages. Scenario 2 was

effective in reducing the average patient wait time for adoption greater than 50% and Scenario 1 was effective in

reducing the average patient wait time for adoption greater than 35%. These results are obtained under a system

constraint of physician capacity and daily working hours. The physician capacity was not changed and the daily

working hours were set to 8 hours per day. This essentially takes into consideration the effect of reducing primary

care professionals.

Figure 5: Number of patients denied care

0.0000

500.00001,000.0000

1,500.00002,000.0000

2,500.0000

Num

ber o

f Pat

ient

s Den

ied

Care

Population Adopting WHMS

Patients Denied Care





Figure 5 gives the results of the number of patients who were denied care on the day of visit. All the three scenarios

yielded better results in reducing number of patient denials than the current system. Scenario 1 had lowest number

of patient denials as compared to scenario 2 and 3. Scenario1 for 80%-100% adoption rate yielded 90 denials which

was the least number of denials. The patient denials are applied only for last minute schedule requests. The patients

already scheduled for the day are not denied care. In real situation there may be cases where the scheduled patients

are denied care due to some problems at the care provider side or due to uncertain changes in physician’s schedule.

Figure 6 gives the number of patients absorbed by the current system and the WHMS model (according to

percentage adoption). All the three scenarios yielded better results and showed an increasing patient absorption

capacity compared to the current system. Scenario 1 yielded better results than scenario 2 and scenario 3.

Figure 6: Number of patients absorbed by the system

The aforementioned three scenarios performed better than the current primary care system irrespective of adoption

percentages (in case of patient absorption and patient denials). However the same observations are absent in case of

the average wait time. In all the three scenarios the WHMS system reflected increasing patient absorption capacity.

This increase in patient load had a negative impact on patient wait time. Scenario 1 and Scenario 2 were effective in

reducing the average patient wait time for adoption percentages above 35% and 50% respectively. The simulation

0.0000500.0000

1,000.00001,500.00002,000.00002,500.00003,000.00003,500.0000

Num

ber o

f Pat

ient

s Abs

orbe

d

Population Adopting WHMS

Number of Patients Absorbed





shows that the WHMS model works well across all adoption percentages in absorbing more patients and reducing

patient denials. However the average patient wait time is sensitive to the adoption percentages. The novel method of

using simulation to study the effects of wearable devices will enable researchers and healthcare providers to save

implementation costs and reduce time consumption.

15. Future Work

In this work we analyzed the primary care system at the process level using discrete event simulation. The use of DE

simulation restricts our ability to understand the system at the macro and micro level. In future we wish to

incorporate agent-based and system dynamics technique to get a better understanding of the primary care system at

the micro and macro level respectively.

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no. 5, pp. 799-805, May 2010.

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