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Posted on Dec 28, 2017 in Original Article | 0 comments

Effectiveness of a Countdown Timer in Reducing or Turnover Time

Effectiveness of a Countdown Timer in Reducing or Turnover Time

Majbah Uddin, MS1, Robert Allen, PhD2, Nathan Huynh, PhD1, Jose M. Vidal, PhD3, Kevin M. Taaffe, PhD4, Lawrence D. Fredendall, PhD5, Joel S. Greenstein, PhD4

1Department of Civil and Environmental Engineering, University of South Carolina; 2School of Health Research, Clemson University; 3Department of Computer Science and Engineering, University of South Carolina; 4Department of Industrial Engineering, Clemson University; 5Department of Management, Clemson University.

Corresponding Author: nathan.huynh@sc.edu

Journal MTM 6:3:25–33, 2017

doi:10.7309/jmtm.6.3.5


Background: In production environments, a countdown timer is used to report the status of the planned start time and to provide both a communication mechanism and an accountability aid.1 It has been used in the airline industry to remind all personnel of the remaining time until when the aircraft door should be closed. This study explored the effectiveness of a countdown timer in the operating room (OR).

Aims: This study was designed to assess the effectiveness of a countdown timer in the OR setting and to determine the factors that contribute to prolonged OR turnover time (TOT) (defined to be from the “procedure finish” time of the preceding case to the “procedure start” time of the following case), as well as the impact each of the significant factors has on TOT. In this study, the term case denotes a surgical procedure.

Method: An Android app named ORTimer was developed for the study. The app was installed on Android tablets that were placed at the Certified Registered Nurse Anesthetist (CRNA) workstations in the OR at Greenville Memorial Hospital (GMH) in South Carolina. The CRNAs helped collect the event milestones and record the delay reasons (if applicable). Additional OR case information was extracted from GMH’s electronic medical record. Regression analysis was used to identify significant factors that contribute to prolonged OR TOT and to estimate their impacts. A t-test was conducted to test the hypothesis that the use of a countdown timer is effective in an OR environment.

Results: The data from a total of 232 cases where the ORTimer app was used were examined. Among the factors (i.e., delay reasons and case information) considered, an outpatient from a following case had the highest correlation with excessive room idle time, which is the difference between the actual TOT and the allotted TOT. Delays due to patient-related issues added about 12.7 minutes to the turnover time (90% CI: 7.2, 18.3) when other factors were fixed. Delays due to preoperative-related issues added about 27.4 minutes to the turnover time (90% CI: 20.0, 34.7) when other factors were fixed.

Conclusions: As is the case with most production environments,1 the use of a visual management tool such as the countdown timer in the OR is found to be effective. Additional research is needed to determine whether this finding is applicable to other hospitals.

Keywords: Information Technology (IT) intervention, operating room efficiency, operating room process improvement.


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Posted on Dec 28, 2017 in Original Article | 0 comments

Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Carmel Mercado, MD1, John Welling, MD2,3,4, Matthew Oliva, MD3,4,5, Jack Li, MD2, Reeta Gurung, MD6, Sanduk Ruit, MD6, Geoff Tabin, MD1,3,8, David Chang, MD7, Suman Thapa, MD, PhD6, David Myung, MD, PhD1,8

1Byers Eye Institute, Stanford University, Palo Alto, CA, USA; 2John A Moran Eye Center, University of Utah, Salt Lake City, Utah, USA; 3Himalayan Cataract Project, Waterbury, VT, USA; 4Medical Eye Center, Medford, OR, USA; 5Casey Eye Institute, Oregon Health Sciences University, Portland, OR, USA; 6Tilganga Institute of Ophthalmology, Kathmandu, Nepal; 7Los Altos Eye Physicians, Los Altos, CA, USA; 8VA Palo Alto Health Care System, Palo Alto, CA, USA

Correspondence Author: david.myung@stanford.edu

Journal MTM 6:3:34–42, 2017

doi:10.7309/jmtm.6.3.6


Background: The ability to obtain high quality ocular images utilizing smartphone technology is of special interest in under-resourced parts of the world where traditional ocular imaging devices are cost-prohibitive, difficult to transport, and require a trained technician for operation.

Purpose: The purpose of this study was to explore potential anterior and posterior segment ocular imaging use cases for a smartphone-based ophthalmic camera adapter (Paxos Scope, Digisight Technologies, San Francisco, CA, USA) in under-resourced settings in Nepal.

Methods: From September to November of 2015 we utilized the Paxos Scope smartphone camera adapter coupled with an iPhone 5 to explore anterior and posterior segment clinical applications for this mobile technology. We used the device at a tertiary eye care facility, a rural eye hospital and a rural cataract outreach camp. We tested the device’s capability for high quality photo-documentation in clinic, in the operating room, and in the outreach camp setting. Images were automatically uploaded to a secure, cloud-based electronic medical record system that facilitated sharing of images with other providers for telemedicine purposes.

Results: Herein we present 17 ocular images documenting a wide variety of anterior and posterior segment pathology using the Paxos Scope from clinical cases seen in a variety of settings in Nepal.

Conclusions: We found the quality of both the anterior and posterior segment images to be excellent in the clinic, the operating room, and the outreach camp settings. We found the device to be versatile and user-friendly, with a short learning curve. The Paxos Scope smartphone camera adapter may provide an affordable, high-quality, mobile ocular imaging option for under-resourced parts of the world.

Keywords: Paxos Scope, mobile health, smartphone ophthalmic imaging, teleophthalmology, triage


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Posted on Dec 28, 2017 in Original Article | 0 comments

Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Kwee Choy Koh, MBBS, MMed, Pilane Liyanage Ariyananda, MD, FRCP, Esha Das Gupta, MRCP, FRCP, Rumi R. Khajotia, MD, MD, Sethuraman Nagappan, MRCP, FRCP, Vaani Valerie Visuvanathan, MBBS, MRCP, Mina Mustafa Mahmood, MD, MRCP, Siew Huoy Chua, MB BCh BAO, MRCP, Poh Sim Chung, Dipl. BA

Assoc. Prof. Kwee Choy Koh, Postal address: Head & Infectious Diseases Consultant, Department of Medicine, Clinical Campus, International Medical University, Jalan Rasah, 70400 Seremban, Negeri Sembilan, Malaysia

Corresponding Author: kweechoy_koh@imu.edu.my

Journal MTM 6:3:2–6, 2017

doi:10.7309/jmtm.6.3.2


Background: Student attendance at teaching-learning sessions is traditionally registered using pen-and-paper. This method has many weaknesses: lost, hard to verify, attendance-by-proxy, late submission. The Quick-Response Code is a two-dimensional barcode that can be read with a QR reader on a smartphone that captures and instantly transmits information to a cloud storage. We describe the use of a QRC method to register medical students’ attendance and assessed their perception of this method compared to PAP.

Method: The attendance of 112 medical students in all teaching and learning sessions in internal medicine posting was registered either with QRC or PAP. A static QR code was generated using open source software online and linked to Google Document for storage of data registered. At the end of the posting, the students’ perception was assessed using a 4-point Likert scale satisfaction survey.

Results: 83 students participated in the survey (74% response rate). 100% owned a smart device and 91.6% had data connectivity. Compared to PAP, the QRC method was perceived to be more convenient, more accurate, more secure and more environment-friendly and preferred (all p < 0.05). The QRC method was not significantly faster than the PAP (p = 0.361).

Conclusion: The QRC method was the preferred attendance record tool compared to the traditional PAP method. Its adoption in the closed and secure environment of a medical campus and hospital is feasible and should be explored.

Keywords: QR code, attendance record tool, medical school


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Posted on Dec 28, 2017 in Original Article | 0 comments

Mobile Applications for Stroke Prevention: A Survey of Physicians’ Perspectives

Mobile Applications for Stroke Prevention: A Survey of Physicians’ Perspectives

Douglas Halket, BS1, Jonathan Singer, MA2, Clotilde Balucani, MD, PhD3, Dimitre Stefanov, PhD3, Steven R. Levine, MD, FAHA, FAAN, FANA4

1SUNY Downstate College of Medicine, MSC, Brooklyn, NY; 2University of Nevada, Reno, Clinical Psychology, MSS, Reno, NV, 3SUNY Downstate Medical Center & Stroke Center, MSC, Brooklyn, NY; 4University Hospital of Brooklyn, Associate Dean for Clinical Research & Faculty Development, Downstate Medical Center & Stroke Center

Corresponding Author: douglas.halket@downstate.edu

Journal MTM 6:3:7–13, 2017

doi:10.7309/jmtm.6.3.3


Background: Little is known about the prevalence and nature of mobile application adoption in clinical practice.

Aims: To explore current and potential mobile application use in primary care physicians (PCPs) for stroke prevention. Do PCPs recommend, use, or discuss mobile health applications for stroke preventative measures?

Methods: Current PCPs in the New York City area specializing in Internal Medicine, Ob/Gyn, and Family Medicine were surveyed in person. The survey consisted of demographic questions and 11 questions on mobile application use.

Results: Of the 86 physicians surveyed (53% female; mean age 37 years, SD 12), 74% (95% CI 65%, 84%) reported using mobile applications in patient care, whether for their own use or in recommending to patients. Experience was the most important determining factor, with 82% of physicians with less than 3 years practice experience using mobile apps, 78% of physicians with 3 to 10 years, 60% of physicians with 11 to 20 years, and 58% of physicians with greater than 20 years experience (p=0.045). Physicians reported using mobile applications to manage stroke risk factors 25% (95% CI 16%, 35%) of the time, while 77% (95% CI 68%, 86%) expressed interest in new apps to help their patients manage these risks. Lastly, 41% (95% CI 30%, 51%) of physicians surveyed strongly agreed that mobile applications are useful in providing patient care, while 49% (95% CI 38%, 59%) simply agreed and 0% disagreed.

Conclusions: Most urban PCPs we surveyed believe that mobile applications belong in healthcare, with one in four using them to manage stroke risk factors.

Keywords: Mobile Technology, Stroke, Prevention, Primary Care Physicians, Patient-Centered


Introduction

Stroke is the leading cause of preventable disability in the United States1. Each year, approximately 795,000 Americans suffer a stroke1. Many of these strokes are preventable. According to a case-control study of 6,000 individuals, 90% of strokes can be attributed to just 10 modifiable risk factors, and targeted interventions to reduce blood pressure and cigarette smoking as well as to promote physical activity and healthy diet could substantially reduce the risk of stroke2. There are already many mobile applications that individually target each one of these interventions, although not necessarily in the specific context of stroke prevention3.

Healthcare related mobile technology has expanded rapidly over the last several years. In 2015, there were 165,000 medical and health related apps for sale in the Google and Apple store4, up from 40,000 in 20125. However, little is known about the prevalence and nature of mobile application adoption in patient centered clinical practice, let alone about its effectiveness in changing healthcare outcomes. While there are many mobile applications in fields such as diabetes management, there are still usability and integration issues among almost all of them6. A 2014 study found 93 mobile application for iPhone and Android regarding general stroke information, although many of these were lacking scientifically valid information7.

There have been relatively few randomized controlled trials involving mobile applications, although some have shown promise. A systematic review of 9 studies assessing mobile application effectiveness in managing cardiovascular disease, lung disease, or diabetes mellitus found 3 studies where a mobile application intervention showed a statistically significant clinical improvement8. Additionally, there has been at least one quality improvement project studying mobile technology assistance in blood pressure control for stroke survivors9, although there have been no large scale randomized control trials specifically involving stroke prevention. Mobile applications have the potential to change the healthcare landscape, leading to improved outcomes while reducing cost. If mobile technology is going to be implemented in a meaningful way, it is imperative that it be grounded in clinical evidence.

The current study is the first step in examining the role that mobile technology plays in primary care physicians’ (PCPs’) practices. The purpose of this study is to examine the prevalence of PCPs using mobile applications in patient care. More specifically, to study if and how PCPs use mobile applications for stroke prevention. Also, we evaluated preferences and attitudes of PCPs toward the use of mobile applications in their practices with specific focus in stroke management, and how these preferences vary over physician and practice demographics. We hypothesize that younger physicians practicing in wealthier areas are more likely to use and recommend mobile applications.

Methods

Current PCPs in the New York City area specializing in Internal Medicine, Ob/Gyn, and Family Medicine were surveyed in person.

Participants: A convenience sample of 86 physicians (46 females) ranging from 25 to 68 years of age participated in this study, which was approved by the SUNY Downstate IRB. Participants were surveyed in person and were identified at SUNY Downstate grand rounds and the annual meeting of the New York Academy of Family Physicians. The mean age was 37 ± 12 SD. Forty one percent of the participants were attendings and the remaining 59% were residents. Sixty (70%) were Family Medicine specialists, 14 (16%) were in Ob/Gyn, and 12 (14%) were in Internal Medicine. These three specialties were selected because they provide the primary care lead in general preventative care, and the study focus is on actually managing stroke risk factors. Neurologists, despite managing patients after stroke, do not generally provide the ongoing lead in preventative care unless a patient has a separate neurological issue, and so were excluded from the study.

Measures: The standardized survey consisted of demographic questions and practice characteristics including gender, specialty, resident/attending, type of practice (private independent, private partnership, community or university hospital), years of experience (less than 3, 3 to 10, 11 to 20, greater than 20), patients seen per month (less than 30, 30 to 100, greater than 100), median income of practice zip code as determined by census data (less than $40,000 or greater than $40,000), and 11 questions on mobile application use (table 1).

Table 1: Demographic Summary of Study Subjects

Data analysis: Fisher’s exact test was used to test for association between demographic variables and whether or not the physicians used mobile applications in any patient care setting. Cochran–Armitage trend test was used to determine if higher levels of the ordinal predictors (age groups, experience and volume of patients) were associated with higher prevalence of mobile technology use. P values less than 0.05 were considered significant. All participants answered the primary question on mobile application use in patient care. Missing data in other, secondary questions were not factored into the analysis as we were not adequately powered for other specific analyses. A sample size of 87 physicians was determined to have 80% power to detect an odds ratio of 0.4 for the relationship between age and the use of mobile applications using a significance level of 0.05. The odds ratio of 0.4 corresponds to a hypothesized effect of physician age on mobile application use. This odds ratio is based on an expected 30% use of mobile applications by physicians at age 50, compared to 15% for physicians at age 63. The data were analyzed using SAS version 9.4 (SAS Institute Inc., Cary, N.C.)

Results

Of the 86 physicians surveyed, 74% reported using mobile applications in patient care, whether for their own use or in recommending to patients. Physicians reported using mobile applications to manage stroke risk factors 25% of the time, while 77% expressed interest in new apps to help their patients manage these risks. Forty one percent of physicians surveyed strongly agreed that mobile applications are useful in providing patient care, while 49% simply agreed and 0% disagreed.

Table 2 shows the most important determining factor, with 82% of physicians with less than 3 years practice experience using mobile apps, 78% of physicians with 3 to 10 years, 60% of physicians with 11 to 20 years, and 58% of physicians with greater than 20 years experience (p=0.045). Other demographic factors, including median income of practice zip code were not significant.

Table 2: Mobile Application Usage Responses

Age did not have an effect on whether or not physicians agree that mobile applications are useful in providing patient care (p=0.27; table 3). Position, type of practice, and volume of patients were not associated with higher use of mobile technology use (p=0.80, 0.49, 0.33, respectively).

Table 3: Usage of Mobile Applications in Patient Care by Demographics

Discussion

Previous studies have shown that there are a large number of mobile applications available to patients and physicians, particularly to manage stroke risk factors3,4,68. This study builds on this knowledge in an attempt to determine how physicians view mobile application use in the primary care clinical setting, particularly in stroke prevention. Our data revealed that of a sampling of mostly urban PCPs, 74% are already using mobile applications in clinical practice and an even greater portion, 90%, believe that mobile applications belong in patient care. Although PCPs with more experience were less likely to use mobile applications, the findings suggest a wide acceptance across physician age groups and practice characteristics. Income of practice zip code was not a significant predictor in whether or not PCPs recommend mobile applications to patients, and neither was PCP age. Physician experience was potentially important, with less experienced PCPs more likely to recommend mobile applications than their more experienced colleagues. These findings suggest the adoption of mobile application use in clinical practice will likely continue to increase in the coming years.

Some physicians, 34% of the respondents, expressed concerns over mobile technology use in healthcare. These concerns included ease of use, confidentiality, determining which app is best, time limitations, and integration with Electric Health Records (EHR). Some of these concerns, such as determining which apps are best, can be solved with further study. Confidentiality and EHR integration issues are more complex but are also potentially manageable in the future5.

There was a major disparity between PCP-expressed interest in mobile applications (76%) compared with their actual use of mobile applications to manage stroke risk factors (25%). This gap between interest and use can be bridged by either the development of new mobile applications specifically tailored to manage stroke risk factors or the development of a tool to assist patients and physicians in selecting from already existing mobile applications. The development of such a tool would likely require a systematic method for determining which mobile applications are effective in the patient care setting. Mobile applications that can assist in managing chronic risk factors for stroke can then be identified, potentially leading to an inexpensive and effective method for reducing one of the largest causes of morbidity and mortality in the United States1,2.

There were several limitations to the study. First, the sample size was one under the projected number, as the data were collected from physicians in discrete groups (grand rounds, etc.) rather than from individuals. Second the data were slightly skewed by specialty and position. We recruited a large percentage of Family Medicine attendings because of the ease of recruiting them at their annual meeting. Many of the Ob/Gyn and Internal Medicine respondents were residents because they were more likely to attend grand rounds and stay to fill out a survey. There were few respondents from high income zip codes. We acknowledge the potential lack of generalizability to other specialties, including neurologists who may participate in stroke prevention but generally do not lead and primarily manage this effort in their patients.

Conclusions

The majority of PCPs have either already adopted mobile applications in their clinical practice or believe that mobile applications belong in patient care. However, the implementation of mobile applications specifically for stroke prevention purposes is still limited. Concerns about mobile application use were limited to a small number of physicians and a significant number expressed interest in a new application to manage stroke risk factors. There is PCP interest in a mobile application specifically tailored to manage stroke risk factors that patients and physicians can use together and encompasses several aspects of stroke prevention. Since many physicians are already using mobile applications or support their use in patient care, it is likely that mobile applications will have a place in clinical practice in the future, and specifically in stroke prevention.

Acknowledgements

Supported by a SUNY Alumni fund medical student research scholarship to DH.

Disclosures

All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: DH received support from a SUNY Alumni grant, no other support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

References

1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016;133(4):e38–360.

2. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet (London, England). 2010;376(9735):112–23.

3. Mosa ASM, Yoo I, Sheets L. A Systematic Review of Healthcare Applications for Smartphones. BMC Medical Informatics and Decision Making. 2012;12:67.

4. Misra S. New report finds that more than 165,000 mobile health apps now available, takes close look at characteristics & use: iMedicalApps; 2015 [updated September 17, 2015. Available from: http://www.imedicalapps.com/2015/09/ims-health-apps-report/.

5. West D. How mobile devices are transforming healthcare. Issues in technology innovation. 2012;18(1):1–11.

6. El-Gayar O, Timsina P, Nawar N, Eid W. Mobile Applications for Diabetes Self-Management: Status and Potential. Journal of Diabetes Science and Technology. 2013;7(1):247–62.

7. Dubey D, Amritphale A, Sawhney A, Amritphale N, Dubey P, Pandey A. Smart phone applications as a source of information on stroke. Journal of stroke. 2014;16(2):86–90.

8. Whitehead L, Seaton P. The Effectiveness of Self-Management Mobile Phone and Tablet Apps in Long-term Condition Management: A Systematic Review. Journal of medical Internet research. 2016;18(5):e97.

9. Ovbiagele B, Jenkins C, Patel S, Brunner-Jackson B, Anderson A, Saulson R, et al. Mobile health medication adherence and blood pressure control in recent stroke patients. Journal of the neurological sciences. 2015;358(1–2):535–7.

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Posted on Dec 28, 2017 in Original Article | 0 comments

Scope of Mobile Phones in Mental Health Care in Low Resource Settings

Scope of Mobile Phones in Mental Health Care in Low Resource Settings

M Sood1, RK Chadda2 K Sinha Deb3, R Bhad4, A Mahapatra4, R Verma3, AK Mishra3

1Additional Professor; 2Professor; 3Assistant Professor; 4Senior Resident
Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.

Corresponding Author: soodmamta@gmail.com

Journal MTM 6: 3:43–47, 2017

doi:10.7309/jmtm.6.3.7


Introduction: Mobile apps are used as an aid in the mental health services in many high income (HI) countries. The present study was conducted to assess frequency of mobile phone use amongst patients with mental illness.

Methods: Patients attending a psychiatric outpatient department of a public funded tertiary care medical school in India were assessed for use of mobile phones and its possible utility in mental health service delivery using a semi structured questionnaire.

Results: The study had 350 subjects, out of whom 357 (87.7%) reported using mobile phone on a regular basis. Mobile phone was used for phone calls, sending and receiving SMS, recreation, and for accessing social networking sites. Most of the users agreed that the mobile phone could be used as an aid in mental health service delivery, and expressed willingness to receive educational messages.

Conclusion: Patients with mental illness attending psychiatric outpatient services in India use mobile phones and are willing to use as a treatment aid.

Keywords: Mobile phones, mental health, information technology, India


Introduction

In the last few years, there has been a remarkable spread of mobile technology in low and middle-income (LAMI) countries, with its penetration being much higher than the general infrastructure. For example, in India, there are 943.9 million wireless telephones with a teledensity of ~75%, and the mobile phones constitute about 97% of the total telephones1. Mobile technologies, with an easy and wider availability, portability, being self powered, increasingly better computational capacities and decreasing costs, user familiarity and internet connectivity2, have opened up new alternatives for health care delivery that can be delivered through the existing infrastructure as people carry mobiles with them all the time.2

Recent studies from high-income (HI) countries have reported that a number of people use mobile phones to search for health related information. Most patients with mental illness own mobile phones, use it for activities other than spoken conversations like sending emails, web browsing and social networking, and mobile phones have been explored as a potential aid in mental health services3. Studies from HI countries have demonstrated use of mobile phones in delivering psychosocial interventions (crucial for recovery) like provision of health information, prompts for medications, reminders, self-monitoring, and practice of skills in real world situations4.

In India, mental health resources for psychosocial interventions are meagre and are rarely applied in the care of patients with mental disorders5 as there is a gross deficiency of mental health resources (0.2 psychiatrists, 0.03 clinical psychologists, 0.05 psychiatric nurses, and 0.03 social workers per 100,000 of the population6,7. In this context, mobile technologies have a potential to become an important mental health care service link between the meagre mental health services and the unfulfilled mental health care needs of the vast majority of unreached patients and caregivers, and can be used for a range of indications in mental health like increasing awareness, training, linkages between services, clinical services and research.

We are planning to develop a mobile based intervention framework for imparting psychosocial interventions to the patients attending psychiatric services. Before making mobile based interventions suitable for the needs of the patients, it is important to know whether the patients with mental illness are using mobile phones as there is absence of research in this field from India. The present study was conducted to find out the frequency of mobile phone use among patients with mental illnesses, and to assess feasibility of using mobile phones in improving service delivery and delivering educational messages in them.

Methodology

The study was conducted in psychiatry outpatient services at the All India Institute of Medical Sciences, New Delhi, India. The service runs a walk-in clinic, where all the first contact patients are seen, and a follow up clinic where the old patients already on treatment from the service are seen. Every fifth patient aged 18-60 years, visiting the walk-in clinic, and every fifth patient visiting the follow up clinic over a period of three months (from February – April, 2015) were recruited for the study.

The subjects were explained the purpose of the study. A written informed consent was taken. In patients with psychotic disorders, who were unable to give consent, consent was taken from the accompanying relative. The data was collected using a semi-structured questionnaire (attached as Appendix), prepared for the study by the investigators. The study was approved by the Institute ethics committee.

The data was analysed using descriptive statistics, reported as means and standard deviations for continuous variables and percentages for discrete variables.

Results

A total of 350 subjects were recruited for the study, 205 (58.6%) males and 145 (41. 4 %) females. Two hundred and thirty five (77.1%) subjects were from the follow up clinic and 115(32.9%) were from the walk in clinic. Mean age of the subjects was 33.3 (±11.5) years. About 40% of the subjects had received 10 years of formal education, one fifth had studied upto 12th standard, and about 30% were graduate. Mean duration of illness was 6.2 (± 7.6) years, and mean duration of treatment was 1.5 (± 3.5) years. Common diagnosis included neurotic, stress related and somatoform disorders (47.0%), mood disorders (32.0%), schizophrenia and related disorders (14.3%), and disorders due to psychoactive substance use (4.0%).

Three hundred and seven (87.7%) subjects reported using mobile phones regularly. Most of the subjects used mobile phone for making and receiving phone calls, and about two third also used it for sending and receiving short text messages. About half of them used clock and alarm functionalities, two fifth also used it for recreational activities, and around 30% used for accessing social network sites (Table 1)

Table 1: Mobile phone use and its utility as aid to psychiatric treatment Application of mobile phone in day-to-day life (n=307)

Most of the subjects reported that mobile phones could be used as an aid to treatment for psychiatric disorders. This included use as a reminder for appointments (90.4%), and to take medications (72.3%). About half of the subjects suggested that mobile phones could be used for recording and reporting of side effects. Forty two percent of the subjects reported that mobile phones could be used for receiving educational information related to their mental illness. Only 17% of the patients felt that these could be used for imparting psychological treatments (Table 1).

More than 70% of the mobile phone using subjects expressed that the phones could be used to receive educational messages regarding any precautions to be taken (70.5%), activities and exercises (55.4%), information about their mental illness (36.5%), dietary advice (26.9%) and information about stress reduction techniques (Table 1).

In a single open-ended question about any other use of mobile phone related to psychiatric treatment, only 31 (11.4%) subjects provided a suggestion. Sixteen subjects felt that mobile phones can be used to give feedback regarding treatment. Fifteen subjects felt that these can be used for sharing experience regarding treatment through common patient group on social networking sites.

Discussion

More than 85% of the patients with mental health problems in our study were using a mobile phone. The mobile phones was being used for a range of activities besides making phone calls. Most subjects opined that it could also be used as an aid in the treatment.

All of our subjects used mobile phone for making and receiving phone calls, but over two third also used it for sending and receiving short text messages. The phone was also used for other functions like clock and alarm by about half of the subjects, for recreation by about two fifths, and for accessing social network sites by 30% of the subjects. Earlier studies have also reported the most common uses of mobile phones in patients with severe mental illnesses as for making phone calls, texting, and internet3.

Regarding the potential usage of mobiles phones in treatment, most (90%) of the subjects reported that these can be used to remind them for appointments. Majority of the subjects indicated use of mobile phones in pharmacological treatment as reminder for taking medications and reporting side effects. However, only less than half of the subjects indicated that mobile phones can be used for receiving educational information related to their mental illness and even fewer subjects felt that these can be used for imparting psychological treatments. This finding is not unusual as majority of the patients with mental illnesses receive pharmacological treatment with minimal psychosocial interventions due to lack of resources for the latter5. However, when asked specifically about the kind of educational messages they wanted to receive, majority of them listed only psychosocial interventions. About three fourth of the subjects reported that they wanted to receive messages regarding any precautions to be taken regarding illness or medications, about half of them wanted messages regarding activities and exercises, and one third wanted information about their mental illness, and a quarter each wanted information regarding dietary advice and stress reduction techniques. This shows that the mobile phones appear to have a high potential of use in the mental health care settings.

Our results are in line with the studies from HI countries which have reported that n 72%-97% of patients with mental illnesses and substance use disorders own a mobile phone2, 8, 9. The results are not surprising since compared to an average 2008–2012 growth rate of mobile subscriptions of just 10.15% in the HI countries, the growth rate was much higher (75.07%) in South Asia driven mainly by growth in India10.

The study had a limitation of being conducted in a tertiary care setting in a big city and hence the findings may not be generalizable to other settings. We did not screen specifically for the use of smart phones, since most of our subjects use ordinary phones.

The study concludes that most of the patients with psychiatric disorders attending outpatient services in India use mobile phones, and welcome its use as a treatment aid. The mobile phones offer a potential to be exploited in India with limited human resources in mental health and a potential use in psychosocial treatment.

References

1. ANNUAL REPORT (2014). Department of Telecommunications, Ministry of Communications and Information Technology, Government of India, New Delhi. Available at http://www.dot.gov.in/sites/default/files/Telecom%20Annual%20Report-2012-13%20%28English%29%20_For%20web%20%281%29.pdf (accessed on 12 Aug 2015)

2. Zhang MWB, Ho CSH, Cheok CCS, Ho RCM. Smartphone apps in mental healthcare: the state of the art and potential developments. BJ Psych Advances, 2015;21(5):354–8.

3. Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, and Drake RE. Mobile technologies among people with serious mental illness: opportunities for future services. Adm and Policy Ment Health 2013;40(4):340–3.

4. Harrison V, Proudfoot J, Wee P P, Parker G, Pavlovic DH, Manicavasgar V Mobile mental health: Review of the emerging field and proof of concept study. J Ment Health 2011;20(6):509–24.

5. Sood M, Chadda RK. Psychosocial rehabilitation for severe mental illnesses in general hospital psychiatric settings in India. BJPsych International 2015;12:47–8.

6. Chadda RK, Prashanth R (2015) Allied Mental Health Professionals: clinical Psychologists, Psychiatric Nurses and Psychiatric Social Workers: Availability and competency. In Mental Health in South Asia: Ethics, Resources, Programs and Legislation (Eds JK Trivedi & A Tripathi) Springer (India) pp. 221–32.

7. The World Health Report: 2001: Mental in Low and Middle-Income (LAMI) Countries, World Health Organization, 2001.

8. Crankshaw T, Corless I B, Giddy J, Nicholas P K, Eichbaum Q, Butler L M. Exploring the patterns of use and the feasibility of using cellular phones for clinic appointment reminders and adherence messages in an antiretroviral treatment clinic, Durban, South Africa. AIDS Patient Care STDS 2010;24(11);729–734.

9. Milward J, Day E., Wadsworth E, Strang J, Lynskey M. Mobile phone ownership, usage and readiness to use by patients in drug treatment. Drug Alcohol Depend 2015;146(1):111–5.

10. Farrington C, Aristidou A, Ruggeri K. mHealth and global mental health: still waiting for the mH2wedding? Globalization and Health 2014;10:17.

Appendix

Feasibility of using mobile phones in improving service delivery and creating health awareness in patients with mental disorders

(Please tick mark the answers)

DateRegn No.Name

AgeGender Education

Address:

Mobile:E mail (if any):

Diagnosis

Duration of Illness:

Duration of treatment at AIIMS:

  1. 1. Do you have a mobile phone?Yes/No

  2. 2. For what all functions do you use mobile phone? (Kindly mark as many as applicable)

    • • Making & receiving calls

    • • Sending & receiving messages

    • • Clock & alarm

    • • Contact list

    • • Any other

  3. 3. Do you think that it can be used as a helping aid in your treatment? Yes/No

  4. 4. If yes, kindly tell, how? (Kindly mark as many as applicable)

    • • Reminder for appointment

    • • Reminder to take medicines

    • • Recording & reporting of side effects

    • • Receiving education messages related to your illness

    • • Psychological treatment

    • • Any other

  5. 5. What kind of educational messages you would like to receive?

    • • Any precautions to be taken

    • • Activities or exercises

    • • Dietary advice

    • • Any other

Any suggestions, you would like to make

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