INFS 892: Health Informatics Research
Literature Review #3: Mobile Computing in Health Care Settings
Cory Allen Heidelberger
April 25, 2011

Introduction

Only a few years ago, discussion of meaningful use of electronic health records on mobile devices would likely have conjured images of rolling workstations and conventional laptop computers. In the last few years, the form factor for common mobile computing devices has folded down significantly, as touchscreen-enabled smartphones and tablets have experienced faster adoption that most previous technological innovations. Making meaningful use of EHR systems still requires systemwide investment in technology and training, but it can now take advantage of personal mobile computing devices widely adopted by health care professionals for their personal use. “Besides the obvious benefits of always-on, ubiquitous connectivity, it leverages something most doctors and patients already own — a mobile phone or, increasingly, a tablet. This is why we have seen so many healthcare solutions coming out that incorporate wireless” (Lewis, 2011b). This multiply advantageous form factor may not “crush” laptops in the healthcare space, as one champion proclaimed one month after the iPad hit the market (Merrill, 2010a), but new mobile computing tools are enjoying remarkably fast adoption in the healthcare field.

With the meaningful use verification period for the CMS EHR Incentive Program opened Monday (“‘Meaningful Use’ Reporting Period Starts for Electronic Health Records Program,” 2011), it is thus important and timely to see if adoption of mobile computing devices by health care professionals may help health care incorporate health IT in their regular service provision. This paper reviews current rates of adoption, uses, and motivating factors of mobile computing technology by health care practitioners.

Earlier Mobile Computing Devices

Mobile computing preceded smartphones and tablets in the form of personal digital assistants. 26% of U.S. physicians used PDAs in 2001 (Lu et al., 2003). However, at that time, PDA users in health care settings faced more barriers. Many more institutions lacked integrated information systems that could provide PDA users access to patient databases. Screen size and resolution, poor interfaces, short battery life, and hardware fragility hindered usability (Lu et al., 2003). Studies expected PDA use to rise rapidly (Lu, Xiao, Sears, & Jacko, 2005), but the above barriers prevented the devices from gaining dominance in the health care industry.

Laptop computers with tablet capability brought some expanded interface capacity. However, these devices posed challenges the same problem as PDAs with poor battery life, with the greater weight and bulk compounding usability problems. Older tablets also generally ran operating systems designed for use with keyboard and mouse, making operation in tablet mode unwieldy (Robertson, Miles, & Bloor, 2010).

Current Mobile Adoption

Now mobile computing tools appear to be rising to meet the hopes expressed for mobile computing years ago. Current literature echoes past promises of halcyon days for mobile computing—smartphone technology, for instance, is called a transformative, paradigm-changing technology (Bottles, 2011). However, current adoption and use rates appear to give ground for such statements. Smartphones are leading the way in modern penetration of mobile computing technology in the health care field. Knowledge Networks survey finds 64% of doctors have a smartphone. 27% of primary care providers and specialists have a tablet, an adoption rate five times the rate for the general population. That statistic is all the more remarkable given that health care practitioners generally lag other industries and the general population in information technology adoption. Manhattan Research mid-2010 found 72% of U.S. physicians have a smartphone or PDA; Chilmark Research finds 22% of physicians with iPad by end of 2010 (Dolan, 2011). A 2010 PricewaterhouseCooper survey found 63% of physicians are already using personal devices for mobile health solutions (Lewis, 2010b).

At the institutional level, in fall 2010, one IT enterprise supplier found that healthcare institutions constituted 10% of iPad-deploying customers, the third-largest segment among its clients, just behind the technology sector at 11% and well behind the leading financial services sector at 43% (Merrill, 2010b). An October 2010 HIMSS survey found 70% of responding members planning to deploy iPad and other Apple mobile devices in their health care facilities by the end of 2011 (Lewis, 2010a). Interestingly, 30% of physicians in a survey last year reported that, benefits notwithstanding, their hospital or practice leaders would not support the use of mobile devices (Lewis, 2010b).

Operating systems adopted show some significant differences between health care practitioners and the general consumer population. Nielsen numbers on the broader consumer market show devices running the Android operating system take up 48% of the market. iOS holds 31%, while Blackberry OS claims 18% (Zeman, 2011). A Bulletin Healthcare analysis (Hirsch, 2011) of its health professional subscribers found a much different breakdown of OS choice. It found 93% of its subscribers who use mobile devices use Apple-based machines. iPhone users decreased from an 86% share of the mobile audience in this group in June 2010 to 79% in February 2011. iPad users rose over the same period from 8% to 14%. Android doubled its share of users to 6%. This study found the three in ten subscribers used mobile devices to read Bulletin healthcare’s daily email briefings, showing that physicians use mobile devices for basic information seeking and ongoing professional learning.

The Bulletin Healthcare survey also found notable differences in mobile device adoption rates among different specialties:

Specialty

Mobile Adoption

Physician Assistants

41%

Emergency Room Physicians

40%

Cardiologists

33%

Urologists

31%

Nephrologists

31%

Dermatologists

30%

Gastroenterologists

30%

Psychiatrists

28%

Optometrists

28%

Radiologists

24%

Rheumatologists

22%

Endocrinologists

21%

Oncologists

20%

Clinical Pathologists

16%

Table: Mobile device adoption rates among different health specializations (Hirsch, 2011)

These different rates appear to be higher among more mobile practitioners like P.A.’s and ER physicians and lower among more lab-based analysts like radiologists and pathologists who may travel less in their facility in the course of their normal workflow.

Tools Available

Mobile computing funtionality ranges from access to integrated EHR solutions to standalone applications for communication, reference, and administration. DrChrono introduced its iPad EMR the same month the iPad launched; by September, ClearPractice followed with the Nimble EMR application (Merrill, 2010a). The MyChart Apple application lets patients access medical history, appointment times, and lab results. The University of Iowa Hospitals and Clinics are testing Canto (for iPad) and Haiku (for iPhone) for likely implementation in October with an upgrade of the Epic EHR system (Bennett, 2011).

The Emergency Medical Spanish Guide was developed by an EMT whose experience with Spanish-speaking patients led him to create an application that could provide and even pronounce simple yes/no questions in Spanish to bridge language barriers in the ER (Usatine, 2010). That application is now paying for the former EMT’s medical school tuition. A similar application exists to serve French-speaking patients (Castro, 2010). InVivoLink this month released OrthoPod, an iPad app to help orthopedic surgeons access their personal implant registry and see patterns in their procedures and patient data (press release, 2011).

Reference apps like Epocrates and WebMD are most popular mobile apps; apps from pharma manufacturers get little use (Dolan, 2011). As of April, 2010, over 125,000 doctors were using Epocrates on iPhone and iPod touch devices; one study claimed that “60 percent of Epocrates users avoided three or more medical errors a month” (Bottles, 2011). The American Medical Association released its first mobile application this month, a free application through iTunes to allow doctors to look up, track, and organize Current Procedural Terminology billing codes (Gillette, 2011). This release for Apple devices acknowledges the dominance of Apple products in this sector.

Articles on mobile applications provide multiple examples of physicians educating patients with mobile images from Netter’s Atlas of Human Anatomy (Davis, 2011; Porter, 2011). The FDA this February approved Mobile MIM, a mobile radiology application that allows clinicians to share and make diagnoses based on CT, MRI, and PET scans (Lewis, 2011b).

The AirStrip Cardiology app (iPhone, iPad; planned for Android) gets data from GE Healthcare’s Muse Cardiology Information System’s cloud database of current electrocardiograms. Through the AirStrip system, doctors can access ECGs immediately anywhere instead of relying on potentially distorted faxes or PDFs. On mobile screens, physicians can zoom in on small differences, as small as a half-millimeter, that make make a big diagnostic difference but that might be lost in the resolution of a typical fax (Horowitz, 2011). This application complements other apps planned by GE Healthcare to expand mobile EHR access (Lewis, 2011b). AT&T last month announced similar expansion of its cloud-based Healthcare Community Online to include a mobility interface for smartphones (Lewis, 2011b).

Cloud computing and virtualization broaden mobile health IT from the realm of specific mobile apps to full mobile access to an institution’s integrated EHR system. Dell this month released a mobile clinical computing program that allows hospitals using the Meditech health Care Information System to virtualize their system and provide secure access to desktop applications via any number of devies, including tablets (Lewis, 2011c). Virtualized clients also mitigate concerns about information security on personal devices (Lewis, 2010a).

Mobile hardware is improving the capabilities available to doctors. This year’s iPad 2 processes images nine times faster than the original device in 2010, allowing faster processing of more detailed medical images. The dual cameras of the tablet also make it easier for rural doctors to share photos of wounds and conference with remote colleagues (Lewis, 2011b). These capabilities can facilitate communication among radiologists and clincians and expedite diagnosis and treatment (Choudhri & Radvany, 2010). Radiologists who are accustomed to more sednetary workflow in a dedicated imaging lab with specialized equipment may find value in accommodating mobile platforms to support better interaction with referring physicians in patient management teams (Shih, Lakhani, & Nagy, 2010).

Motivations for Mobile Adoption

The largest motivators for this apparent rush to mobile deployment include point-of-care applications, clinical decision support, medical image viewing applications, and general administration. Another survey finds that physicians agree the greatest benefit of mobile health will be the ability to make decisions faster by accessing more accurate data in real time (Lewis, 2010b). The perception overlaps with the perception among more than 60% of physicians that the best benefit of EHR systems is real-time patient information access (staff, 2011).

A Knowledge Networks marketing survey finds physicians relying on mobile units for checking email, researching medications and conditions, and taking surveys. Physicians still prefer in-person visits with drug company representatives over e-marketing—markedly more so among physicans over 55, but still with a strong majority among those under 40 (Dolan, 2011). Nonetheless, Epocrates has just launched a mobile drug-sampling service, offering their 315,000 physician members the ability to order custom samples via mobile devices (Millard, 2011).

Christ Community Health Services in Memphis chose iPads as its main hardware platform for its coming switch to electronic health recorsd. The hospital chose from among numerous technological options based on tests in clinical settings and criteria including battery life, ease of use, and portability. As a result, CCHS Memphis is now preparing to implement 38 iPads in five health care centers for use in all charting. CCHS physician John David Williamson said “that intimate, non-disruptive doctor-patient interaction is the ‘definite advantage of the iPad. Rather than having my back turned to a patient while on a computer, I can actually continue to have a normal interaction with the patient…’” (Maki, 2011). Similar advantages have driven an Ottawa hospital to order 1800 iPads to be in the hands of its physicians, residents, and pharmacists by July (CBC, 2011).

Methodist Le Bonheur Hospital in Memphis started with in-house iPad pilot project and is now testing use of a virtual private network to give authorized iPad users outside the hospital access to medical records (Maki, 2011). Such a move is a logical extension of the mobility allowed by such devices, not just from room to room within the facility, but beyond the walls of the facility. MLBH user cites portability, ease of use, and quick access (no long boot-up, no long log-in and log-out from room to room) as reasons for adoption. Hospitals that have already implemented EHR may also have invested in desktop computers in each room, lessening the need for mobile solutions (Bennett, 2011; Maki, 2011). One official at Stern Cardiovascular Center in Memphis suggests putting desktop on cart to wheel from room to room (Maki, 2011), although physicians who have tried newer handheld mobile solutions might balk at the idea that the traditional computer on wheels (the COW, as it is abbreviated with just a touch of mockery) can provide comparable utility.

Dr. John Halamka, chief information officer of Beth Israel Deaconess Hospital in Boston, puts mobile medical device criteria in concrete terms that argue strongly in favor of iPads as a perfect fit for doctors: “The secret for the ideal clinical device is it has to weigh a pound, it has to last 10 hours, because that’s [a doctor’s] shift, you have to be able to disinfect it so there’s no risk of contamination, and you have to be able to drop it 5 feet onto carpet without damage” (Davis, 2011). Size, weight, and battery life are cited by other users and observers as reasons newer mobile computing devices are gaining acceptance among health care practitioners (Merrill, 2010b). Doctors at Beth Israel Deaconess also say ability to share information with patients is key. Consider the difference between visiting with a doctor who is ensconced a few feet away at a computer terminal, data upon which the patient cannot see, and a doctor bedside, entering data on a screen the patient can see and even touch and manipulate.  Mobile devices create the possibility for more natual bedside communication and interaction (Bennett, 2011) between health care providers and their patients.

Mobile applications provide another opportunity to reduce the separation between health care provider and patient. When tools are downloadable to common platforms like mobile phones and tablets, doctors and patients may be able to use these devices together on their own devices with minimal expense. A psychiatriast may be able to provide information to a visiting patient from an application like the new PTSD Coach, just released this month by the Department of Veterans Affairs and Department of Defense to help veterans with post-traumatic stress disorder (Montalbano, 2011). If the patient has a compatible smartphone or tablet, the psychiatrist can just as easily “prescribe” that the patient install the application on his or her own mobile device and use the application outside the office to manage his or her own symptoms. Unlike a large integrated EHR system accessible only to physicians at hospital or clinic workstations, mobile applications open more possibilities for patient use and interaction with their health care providers and data. Common mobile devices also offer physicians channels by which to provide remote healthcare, an option a majority of consumers and physicians find acceptable (Lewis, 2010b). Such remote monitoring could mark an electronic return to the practice of “house calls” (Lewis, 2010b).

Integrating mobile devices into regular care may also promote patient confidence in the quality of their care. A Sage Healthcare Insights study released this month finds 81% of patients “have a positive perception of documenting patient care electronically” (staff, 2011), a level of positive perception greater than that found among physicans (62%). Patients report greater confidence in physicians who use EHRs and icnreasingly expect physicians to give them access to electronic records and tools (staff, 2011). These findings stand in contrast to concerns expressed that patients may object to their physician answering questions by seeking answers from a machine instead of consulting their own professional knowledge; besides, the push for evidence-based medicine will demand increased use of technology in health care (Shaw, 2011). Also driving expectations of greater health care technology use may be the againg and retirement of baby boomers. This demographic has egnerally lead other groups in technology spending and adoption (Bottles, 2011); as they become senior citizens and increasingly need health care services, they will bring their preferences for technology to that industry. Physicians can respond to such perceptions and expectations by working with EHR data via mobile devices in sight of patients and even showing them the information they are accessing and entering on those devices.

Personal choices and innovativeness appear to be playing an important role in mobile device adoption. At one Memphis hospital, for example, almost all iPads in use are owned by individual physicians (Davis, 2011). Even if their institutions lag in promotion and integration of health information technology, individual practitioners are finding the new generation of smartphones and tablets sufficiently useful to incorporate into their own workflow, even if just for reference and communication. The new mobile technologies are also sufficiently accessible that health care practitioners have designed many of the simple apps available for varios devices.

Conclusion

Past health information research contains various predictions of great technological advances in mobile computing that did not catch on. However, the swift penetration of smartphones and tablets in the heallth care industry, coming on the heels of increased institutional and government investment in EHR, suggest that mobile computing has reached a new level of integrative utility for health care providers.

Works Cited

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Text of presentation on prior research, delivered in INFS 892, 2011.03.03

Health information technology (HIT) research frequently cites privacy as a concern of health care providers and patients. Privacy is recognized internationally as critical factor in HIT adoption (Anderson, Forgner, Johns, & Reinhardt, 2006). Technological, social, organizational, and legal changes since the 1960s have contributed to steadily growing privacy concerns (Westin, 2003). However, the new technology of online social networks as applied to health—health social networks—poses new conditions and benefits that may change previous privacy concerns.

Health Social Networks

The “backbone” of online social networks is the connected collection of user profiles (Boyd & Ellison, 2008). Such profiles usually include user names, photos, and basic demographic information. On health social networks, these profiles may include medical information that would generally be kept private in traditional interactions with health professionals but which HSN users share to help each other find users with information and experience relevant to their specific health concerns. Online social networks generally include information about connections between members. That information may reside in explicitly declared links, like unidirectional “Like” or “Follower” links or bidirectional “Friend” links (Boyd & Ellison, 2008).

A health social network is an online social network formed around shared interest in a specific health condition like obesity or cancer, a specific area of health care like children’s medicine or hospice, or health information in general. The key characteristic of an online health social network is interaction focused on sharing knowledge and providing emotional support for individuals dealing with health-related questions and problems affecting themselves or other people they care about. Swan (2009) defines a health social network as “a website where consumers may be able to find health resources at a number of different levels.” Swan’s definition includes the word consumer, an increasingly inapt term (Adams, 2010a) (Heidelberger, 2009) given that an important characteristic of health social networks and any other type of online social network is that users are not merely consuming content but producing and organizing much of the content available in the form of, for example, posts, comments, discussions, and ratings. While Internet use is often characterized by “lurking,” the act of reading online discussions without making one’s presence known through participation, some evidence shows that health-related online groups show a markedly above-average level of user participation (Nonnecke & Preece, 2000). This increased participation and the blurring of producers and consumers into conducers affects how individuals configure their understandings of health (Adams, 2010b).

The value of any online social network lies in the sharing of information. In health social networks, one of the vital “different levels” (Swan, 2009) at which users seek information is peer information, advice from users just like them that relates specifically to their experience. Lurkers can find certain information relevant to their health situations without ever making their presence known, but to make the information on a health social network fully relevant to one’s specific health situation, a user generally must choose to engage the community in a conversation, share some personal detail, and surrender some level of privacy.

Privacy

Privacy is subject to different interpretations in different situations (Allen, 1988). Privacy is not simply the desire to withhold information; more comprehensively, privacy refers to the desire to control disclosure of information (Lanier & Saini, 2008). Privacy means controlling what personal information is revealed to others, when that information is revealed, and how it is used (Westin, 1967, 2003). The view of privacy as a matter of control enjoys broad acceptance (Elgesem, 1996; Fried, 1984; Lessig, 2002). However, some scholars argue that, while control plays an important part in managing privacy, having control does not equate with having privacy (Dinev & Hart, 2004; Laufer & Wolfe, 1977; Xu, 2007). Control may merely be a means to the privacy ends of restricted access and protection from intrusion (Tavani & Moor, 2001).

Westin (1967) identifies four states of privacy:

  1. solitude: freedom from observation by others
  2. intimacy: small-group seclusion in which members can achieve close, relaxed, frank relationship
  3. anonymity: freedom from identification and surveillance in public places and for public acts
  4. reserve: desire to limit disclosure to others (as summarized in Margulis, 2003).

Westin (1967) also identifies four main functions of privacy:

  1. personal autonomy
  2. emotional release
  3. self-evaluation
  4. limited and protected communication

Some scholars narrow the concept of privacy to informational privacy to focus on the ability of individuals to limit the access others have to their information (Alpert, 2003). In developing the “Concern for Information Privacy” construct, (Smith, Milberg, & Burke, 1996) identify four main components: collection, errors, unauthorized access, and secondary use. (Stewart & Segars, 2002) validated and extended the CFIP instrument. CFIP focuses on organizational handling and uses of information. However, the Internet creates important changes in how individuals interact with organizations and with each other with respect to personal information. The Internet gives consumers more access to and control over the data they share in the marketplace (Malhotra, Kim, & J. Agarwal, 2004). Grounded in social contract theory, the construct of Internet users’ information privacy concerns (IUIPC) focuses on consumers’ engagement with e-commerce and consists of three main components:

  1. collection: equitable exchange of information according to mutually accepted rules
  2. control: freedom to voice an opinion or exit the contractual agreement
  3. awareness of privacy practices: understanding of the rules and practices established by the firm and its agreement with consumers (Malhotra et al., 2004).

Subsequent research has used social contract theory similarly to understand patient interaction with medical websites (Gaurav Bansal, Zahedi, & Gefen, 2010).

The National Consumer Health Privacy Survey 2005 (Bishop, Holmes, & Kelley, 2005) provides these key findings about user interaction

  1. Consumers are concerned about health information privacy.
  2. Consumers are unaware of their privacy rights.
  3. A small fraction (13%) of consumers engage in specific privacy-protection behavior. Most cited behaviors have to do with keeping insurers from finding out information that might affect payment or premiums.
  4. Consumers are willing to trade privacy for benefits. Note that at this time, more consumers identified paper records as secure than identified electronic records as secure.

Privacy in Health Social Networks

More recent research suggests that privacy concerns are affected by health status: individuals in poor health are less likely to share information with consumer health websites (G. Bansal & Davenport, 2010). Connected research attempts to link privacy concerns with other personal dispositions: emotional instability may work through perceived sensitivity of health information to relate to heightened privacy concerns, but extroversion, agreeableness, conscientiousness, and intellect show little sign of significant connection to privacy concerns (Gaurav Bansal et al., 2010). Notice that the preceding studies, like many discussions of information privacy, address privacy as a consumer issue. Westin (2003) broadens the discussion of privacy to look at individual privacy concerns in the context of their relationships as citizens to government and as employees to employers in addition to the commercial relationship of consumers to business. The IUIPC focuses on the social-contract relationship between individuals and the firm as each entity surrenders different rights and makes different promises for different benefits (Malhotra et al., 2004). These conceptualizations fit with the theme of much literature on health information privacy as it considers the impacts of how health care providers may use (or misuse) electronic health records to process genetic information and other sensitive patient data (e.g., Alpert, 2003).

However, these conceptualizations assume a hierarchy that does not necessarily manifest itself in an online social network. Certainly online social networks may involve citizen–government, employee–employer, and consumer–business interactions. While there may well be consumer–business interactions in health social networks in the form of network members seeking information from staff of the online service (e.g., formal forum moderators or coaches, designated medical experts) or advertisements soliciting business, online social network interactions will generally be dominated by a form of interaction not addressed by most discussions of privacy concerns: non-hierarchical citizen–citizen interactions, users communicating with users as equals seeking the same balance between privacy rights and informational benefits.

One of the above studies acknowledges that health social networks may play by different rules from the typical consumer medical websites. In finding that extroversion shows no significant relationship with health information sensitivity, (Gaurav Bansal et al., 2010) note the the consumer-type website they used in their investigation provides a “leaner” online setting where extroverts “apparently are less forthcoming.” They acknowledge that personality traits may have different influence in different communication settings and recommend further investigation along those lines. Such investigation makes sense for health social networks, as they would provide a richer communication setting where, instead of simply making a commercial transaction for a product, users are engaging directly and often by name with each other. This richer channel can carry more social presence, which could well have more influence over privacy perceptions and behaviors than a “lean” commercial transaction. (Gaurav Bansal et al., 2010) recognize that trust in a website (a factor with a strong positive relationship to intent to disclose health information) could be enhanced for some “customers” by social interaction, the heart and soul of health social networks. Evidence that consumer-oriented research may not capture the full privacy picture in health social networks comes from recent findings that individuals with chronic conditions are more likely to go online to find others with similar health concerns than individuals who do not have chronic conditions (Fox, 2011).

Unique characteristics of online social networks pose challenges to normal privacy expectations and behaviors. The social Web is inherently leaky: they increase the chances for individuals to lose control of information they share (Solove, 2007). Online social networks constitute a “mediated public” where information becomes persistent, searchable, replicable, and open to invisible audiences (boyd, 2007). Information that would be transient in a private face-to-face conversation persists online in entries in the Google cache and copies stored on other computers. Information that would be lost in noise and fallible memory can be retraced and recalled almost instantly by search engines. Information shared online can be replicated in numerous other contexts that may have no direct relation to the original intent that motivated the sharing (such as a discussion on a weight-loss surgery forum about constipation finding its way into a doctoral dissertation on the use of narrative on health social networks). And information directed at one specific person or group in an online forum may be viewed by and provoke reactions from numerous individuals whom the original speaker does not and may not ever know. All of these factors can make social interactions online “eternal,” transcending the moment and context in which they happen and escaping the control of the people originating them (Albrechtslund, 2008). This “eternal” nature is seen in health social network profiles whose stories and data continue to inform their communities even after the originators of those profiles die (Goetz, 2008).

However, concerns about privacy in online social networks often operate from an assumption of vertical hierarchy underpinning the traditional view of surveillance (Albrechtslund, 2008). Concerns about surveillance from some “Big Brother” lead to assumptions that sacrifices of privacy in online social networks stem either from cost-benefit analysis or from ignorance of the dangers. Albrechtslund posits that online social networks support “participatory surveillance” in which individuals empower themselves and each other by sharing information rather than trading it. This participatory paradigm improves our understanding of privacy dynamics online as we witness a shift from the anonymity and pseudonymity that dominated Internet discourse in the first decade of the Web to the increased voluntary posting of personal information on social networks and integration of online and offline activities (Adams, 2010b), a shift enhanced by the increasing availability and popularity of Web 2.0 technology like blogs, Facebook, and Twitter.

Privacy is generally seen as a positive construct, and research focuses on how to protect or preserve it (Angst & R. Agarwal, 2009). One study views sharing health information online as a “disutility” (G. Bansal & Davenport, 2010). Arguably, though, from a health perspective, privacy could be seen as a negative factor standing in the way of building and sharing knowledge and support. In a discussion of blogging in a health social network context, (Adams, 2010b) suggests there is a need for further investigation of whether engaging in online documentation and communication about personal health may motivate individuals to stick with routines in pursuit of their health goals. This suggestion hearkens to the concept of participatory surveillance: health social network users may benefit from being able to “keep an eye on” each other and know that others are similarly keeping an eye on them. Increased information sharing also creates a richer database of disease treatment and patient experience, an advantage that PatientsLikeMe explicitly embraces in an “Openness Philosophy” that challenges the primacy of privacy:

“Currently, most health-care data is inaccessible due to privacy regulations or proprietary tactics,” it declares. “As a result, research is slowed, and the development of breakthrough treatments takes decades. . . . When you and thousands like you share your data, you open up the health-care system. . . . We believe that the Internet can democratize patient data and accelerate research like never before” (Goetz, 2008).

Some users of health social networks report finding informational support among peers that they may not find among their regular physicians:

Thank you for the replies. Of course I will talk to my doctor about this but wanted to come on here because it is more beneficial to me to find out how it affects a person that has had WLS [weight loss surgery]. Doctors are not always familiar with how our new systems work (ObesityHelp.com user, 2009).

User behavior in health social networks suggests different attitudes toward privacy than we might expect in public face-to-face interactions. In ObesityHelp.com, for instance, plenty of interaction takes place privately, in member-only chat rooms and via other protected channels. However, thousands of users post medical information in publicly accessible profiles, blogs, and forum posts. Users identifiable by username, location, and photo openly discuss personal details like weight loss and gain, prescriptions, constipation, sexual dysfunction, and surgical complications that would be considered impolite if not imprudent to share in the physical company of strangers and which would certainly violate HIPAA if released by those users’ health care providers.

It is possible, of course, that many users do not grasp the privacy implications of using health social networks. A study of health social networks for diabetics found varying capabilities for users to control privacy settings and share information; only eight of the ten studied networks offer accessible privacy policies, and most of them are hard to read (Weitzman, Cole, Kaci, & Mandl, 2011). Providing users with more control over publication may actually “induce them to reveal more sensitive information,” a result which raises concerns that users may conflate increased control over publication with increased control over access and usage of their personal information  (Brandimarte, Acquisti, & Loewenstein, 2010). Access and usage pose the real risk of intrusion and harm, but as potential actions of others, they are less salient than the satisfaction users may derive from the immediate exercise of control over their own information publication.

Concern for information privacy has been shown to correlate negatively with likelihood of adopting electronic health records (Angst & R. Agarwal, 2009). However, our own research at DSU on adoption of electronic health records in South Dakota suggests that privacy concerns are not nearly as prominent in the minds of practitioners as other barriers to ER adoption. And whatever privacy concerns may keep some people from engaging with health information technology, there are thousands of users who are willing to share personal stories and health information on health social networks like PatientsLikeMe (Goetz, 2008).

Privacy concerns in health social networks may be mitigated by the sense of community. Consider this observation from multiple sclerosis patient and PatientsLikeMe user Laurie Fournier:

Pretty much everybody I know over 45 has some kind of medical condition. Some people have had cataracts surgery, or they have high blood pressure, or high cholesterol or diabetes. Everyone has something. And if everyone has something, that really levels the playing field (Goetz, 2008).

The sense of community in health social networks may be even stronger among users with rare diseases. Individuals with rare diseases—i.e., diseases affecting no more than 20,000 people—are “power users” of Internet health resources (Shute, 2011). The observation that such users are “ready and willing to share with each other so that other people can benefit from their experiences” (Shute, 2011) suggests the need to look beyond a purely selfish privacy calculus: users may surrender privacy not to gain emotional or informational support for themselves but to provide such support to other members of their online community.

Privacy remains a valid concern for all individuals. Privacy sets boundaries, and people need physical and psychological boundaries to define their sense of identity. The increasing integration of online social networks into daily activities, especially in the area of patient use of health social networks, will not end the value of privacy. However, health social networks emphasize the difference in privacy concerns with hierarchical institutions and with peers who share health interests and goals.

Works Cited

Adams, S. A. (2010a). Revisiting the online health information reliability debate in the wake of “web 2.0”: An inter-disciplinary literature and website review. International journal of medical informatics, 79(6), 391–400.

Adams, S. A. (2010b). Blog-based applications and health information: Two case studies that illustrate important questions for Consumer Health Informatics (CHI) research. International Journal of Medical Informatics, 79(6), e89-e96. doi:10.1016/j.ijmedinf.2008.06.009

Albrechtslund, A. (2008). Online social networking as participatory surveillance. First Monday (Chicago), 13(3).

Allen, A. L. (1988). Uneasy access: Privacy for women in a free society. Rowman & Littlefield Pub Inc.

Alpert, S. A. (2003). Protecting medical privacy: challenges in the age of genetic information. Journal of Social Issues, 59(2), 301–322.

Anderson, G. F., Forgner, B. K., Johns, R. A., & Reinhardt, U. E. (2006). Health Care Spending and Use of Information Technology in OECD Countries. Health Affairs, 25(3), 819-831. Retrieved from http://www.ezproxy.dsu.edu:2108/pqdweb?index=17&did=1038492821&SrchMode=3&sid=1&Fmt=4&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1249163046&clientId=18865&aid=1

Angst, C. M., & Agarwal, R. (2009). Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion. MIS Quarterly, 33(2), 339-370. doi:Article

Bansal, G., & Davenport, R. (2010). Moderating Role of Perceived Health Status on Privacy Concern Factors and Intentions to Transact with High versus Low Trustworthy Health Websites. MWAIS 2010 Proceedings, 7.

Bansal, G., Zahedi, F. “., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138-150. doi:10.1016/j.dss.2010.01.010

Bishop, L., Holmes, B. J., & Kelley, C. M. (2005). National consumer health privacy survey 2005. Oakland: California Healthcare Foundation, 1–5.

Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.

boyd, D. (2007, May). Social Network Sites: Public, Private, or What? The Knowledge Tree.

Brandimarte, L., Acquisti, A., & Loewenstein, G. (2010). Misplaced Confidences: Privacy and the Control Paradox. Presented at the Ninth Workshop on the Economics of Information Security (WEIS 2010), Cambridge, MA.

Dinev, T., & Hart, P. (2004). Internet privacy concerns and their antecedents-measurement validity and a regression model. Behaviour & Information Technology, 23(6), 413–422.

Elgesem, D. (1996). Privacy, respect for persons, and risk. Philosophical perspectives on computer-mediated communication, 45–66.

Fox, S. (2011). Peer-to-peer Healthcare. Pew Internet & American Life Project. Pew Research Center’s Internet & American Life Project. Retrieved from http://www.pewinternet.org/Reports/2011/P2PHealthcare.aspx

Fried, C. (1984). Privacy. In F. D. Schoeman (Ed.), Philosophical dimensions of privacy: An anthology. Cambridge Univ Pr.

Goetz, T. (2008, March 23). Practicing Patients. The New York Times. Retrieved from http://www.nytimes.com/2008/03/23/magazine/23patients-t.html

Heidelberger, C. A. (2009). Citizens, Not Consumers. In V. Weerakkody, M. Janssen, & Y. K. Dwivedi (Eds.), Handbook of Research on ICT-Enabled Transformational Government: A Global Perspective (pp. 51-71). Hershey, PA: IGI Global.

Lanier, C. D., & Saini, A. (2008). Understanding consumer privacy: A review and future directions. Academy of Marketing Science Review, 12(2), 1–48.

Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42.

Lessig, L. (2002). Privacy as property. Social Research: An International Quarterly, 69(1), 247–269.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users’ Information Privacy Concerns(IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research, 15(4), 336–355.

Margulis, S. T. (2003). On the Status and Contribution of Westin’s and Altman’s Theories of Privacy. Journal of Social Issues, 59(2), 411–429.

Nonnecke, B., & Preece, J. (2000). Lurker demographics: counting the silent. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 73-80). The Hague, The Netherlands: ACM. doi:10.1145/332040.332409

Shute, N. (2011, March 1). People Coping With Rare Disease Are Internet Power Users. NPR: Shots. Retrieved March 3, 2011, from http://www.npr.org/blogs/health/2011/02/28/134140813/people-coping-with-rare-disease-are-internet-power-users?ps=sh_sthdl

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: measuring individuals’ concerns about organizational practices. MIS quarterly, 20(2), 167–196.

Solove, D. J. (2007). The future of reputation: gossip, rumor, and privacy on the Internet. Yale Univ Pr.

Stewart, K. A., & Segars, A. H. (2002). An empirical examination of the concern for information privacy instrument. Information Systems Research, 13(1), 36–49.

Swan, M. (2009). Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. International Journal of Environmental Research and Public Health, 6(2), 492-525. doi:10.3390/ijerph6020492

Tavani, H. T., & Moor, J. H. (2001). Privacy protection, control of information, and privacy-enhancing technologies. ACM SIGCAS Computers and Society, 31(1), 6–11.

Weitzman, E. R., Cole, E., Kaci, L., & Mandl, K. D. (2011). Social but safe? Quality and safety of diabetes-related online social networks. Journal of the American Medical Informatics Association. Retrieved from http://jamia.bmj.com/content/early/2011/01/24/jamia.2010.009712.abstract

Westin, A. F. (1967). Privacy and freedom (Vol. 97). London.

Westin, A. F. (2003). Social and political dimensions of privacy. Journal of Social Issues, 59(2), 431–453.

Xu, H. (2007). The effects of self-construal and perceived control on privacy concerns. In Proceedings of the 28th Annual International Conference on Information Systems (ICIS 2007). Montréal, Québec, Canada.

 

I keep forgetting what I planned to write about. That’s what the Internet is for: to remember stuff so I don’t have to. Here are the three topics I’ll tackle in our health informatics research class:

  1. Patient privacy concerns in health social networks (with potential for research topic on ObesityHelp.com user-generated content)
  2. Mobile/ubiquitous computing in health care settings (remember, this can address use by practitioners and/or patients!)
  3. Health care provider use of online social networks

 

Come find out what I’ve been working toward for the last three and a half years! On Tuesday, December 7, I will present what we academics call a doctoral dissertation proposal defense.

At 11 a.m., I’ll stand up in front of an audience of stern-looking academics and other interested parties and talk about the really big paper I’m planning to write about my really big research project on storytelling, social networks, and health (see below for the nitty gritty). I’ll talk for 30 minutes; the general public (yes, you!) gets to grill me for 10 minutes. Then my committee grills me for 20 minutes, throws me out to conduct secret deliberations, then drags me back in to tell me whether they’ll let me keep thinking and writing. If my profs give me the thumbs up Tuesday, I get to disappear down the rabbit hole for a few more months, come back out with lots of data, charts, and tested hypotheses, and do a full dissertation defense. And then, if I’m really good, I get some nice letters to put at the end of my name.

Sounds like fun, right? If so, then join in! The proposal defense takes place in the Tunheim Classroom Building, Room 111, on the DSU campus. If you won’t be in Madison on the 7th but would like to listen and submit questions, e-mail me, and I’ll send you a link to the online session.

Ten minutes is an awfully short time for public questions (give me the chance, and I’ll talk with an audience all day long!). If you have questions or feedback that don’t fit in the time Tuesday, I’ll be happy to take your input right here in the comment section. Fire away: just like Johnny Five, I need input!

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The Nitty Gritty

  • Title: “Effects of Narrative on Interpersonal Connection and Communication in Health Social Networks”
  • Date: Tuesday, December 7th
  • Time: 11:00 am (CST)
  • Place: TCB (Tunheim Classroom Building), Room 111

Research Questions:

  1. Does storytelling influence the structure of an online social network?
  2. Do storytellers play a distinct role in sustaining an online social network?

I plan to investigate how people communicate within a health social network, a website providing a forum for interaction among individuals interested in specific health issues (see, for example, CureTogether, PatientsLikeMe, and ObesityHelp). I want to know whether people who use narrative more frequently—i.e., people who tell more stories, share more personal experiences—tend to have more “Friends” (in the Facebook sense of the word) and draw more responses with the online content they provide.

Some theory: Narrative theory says that we make meaning through stories. We are a storytelling species; that’s how we make sense of our world. Social cognitive theory says that social influences shape individual thought and action. Social network theory further supports the idea that our connections with our social network influence who we are and what we do.

Together, these theories suggest that in the context of a health social network, users will gravitate toward information that appeals to their sense of narrative. Personal narratives may provide context, establish authority, and indicate commonality, all of which may appeal more to health social network users than non-narrative information. If narrative content and the users producing it do generate more conversation and connections within health social networks, then that will suggest that storytellers provide distinct value to health social networks and play an important role in sustaining those networks.

In studying the influence of narrative content in health social networks, this dissertation tackles just one aspect of a larger research agenda on the influence of online social networks on health behavior. Like any other health intervention, health social networks matter only so far as they help patients get better, feel better, and live longer. Health social networks may expand our access to information and resources and thus help us make better, more satisfying health decisions. Health social networks may also expose us to all sorts of untested, ill-informed content that leads us to make worse health decisions than if we had just listened to doctor’s orders. Investigating the role of narrative content in health social networks is one step toward evaluating whether health social networks positively influence health behavior.

Of interest to DSU health information technology researchers:

  • Who needs fancy hardware and in-house networks? Just use your docs’ iPhones! (And get AT&T to connect South Dakota to the network….)
  • You can get teenagers to talk about sensitive health issues: just hand them a Health eTouch tablet in the waiting room, and they’ll open up more. (See also Chisolm et al., 2009.)
  • Dr. David Blumenthal, national HIT coordinator, writes Coordinator’s Corner — not quite a blog, but close enough — to keep us updated on what Uncle Sam is up to in health IT.

Veterans Health Administration: EMR Foundation for Gains Data-Mining Benefits

For an industry driven by advanced knowledge and technological innovation, American health care is shockingly behind the curve on adoption of information technology. Only 1.5% of U.S. hospitals have adopted comprehensive electronic medical records systems (Jha et al., 2009). As of 2006, only 20% of U.S. hospitals had implemented electronic medical records (Arnst, 2006). The U.S. is lags behind several OECD countries in per capita spending on health IT (eHealth101, 2006) and is perhaps more than a decade behind international leaders in health IT (Anderson et al., 2006). Without serious investment in health IT, most American hospitals can’t take advantage of data mining.

An exception to this absence of data-mining capability is found in the Veterans Health Administration. The VA began developing the nation’s first functioning electronic medical record system in the late 1970s (Longman, 2009) and computerized medical records in all of its approximately 1300 facilities by 2000 (Arnst, 2006). VA hospitals using  VistA—Veterans Health Information Systems and Technology Architecture—constitute nearly half of the hospitals in the U.S. that have implemented comprehensive electronic medical records (Jha et al., 2009). With VistA, the VA has become the “unlikely leader” in maintaining electronic records that can be mined for insights that produce significant improvements in care and cost efficiency.

The VA has used data mining to improve practices in a number of ways. VA researchers have mined VistA data to target rewards for surgical teams that beat quality and safety benchmarks (and to identify underperforming surgical teams) and to sift through 12,000 medical records to evaluate and improve treatments for diabetes (Longman, 2009). The VA’s Center for Imaging of Neurodegenerative Diseases has used Weka to apply Random Forest and Support Vector Machine algorithms to brain imaging studies (Young, 2009). VA data mining also helped discover the link between arthritis medication Vioxx and heart attacks (Longman, 2009).

One obstacle to optimal data mining in VistA is the diversity of local data dictionaries. Local users can customize data dictionaries to meet unique local needs. That flexibility is a significant part of the system’s success (Brown et al., 2003). However, those different data dictionaries complicate efforts to combine and analyze data across the nationwide system. The VA’s efforts to create national standard dictionaries to translate local dictionaries support not only better immediate transactions such as e-prescribing (Brown et al. 2003) but improved large-scale data mining. The VA’s system has been sufficiently successful that other government hospitals in the U.S. and abroad are adopting and adapting VistA for their facilities (Longman, 2009).

References:

Anderson, G. F., Forgner, B. K., Johns, R. A., & Reinhardt, U. E. (2006). Health Care Spending and Use of Information Technology in OECD Countries. Health Affairs, 25(3), 819–831.

Arnst, C. (2006, July 17). The Best Medical Care in the U.S. BusinessWeek. Retrieved August 1, 2009, from http://www.businessweek.com/magazine/content/06_29/b3993061.htm

Brown, S. H., Lincoln, M. J., Groen, P. J., & Kolodner, R. M. (2003). VistA—U.S. Department of Veterans Affairs National-Scale HIS. International Journal of Medical Informatics, 69(2–3), 135–156.

eHealth 101: Electronic Medical Records Reduce Costs, Improve Care, and Save Lives. (2006). American Electronics Association. Retrieved August 1, 2009, from http://www.aeanet.org/publications/AeA_CS_eHealth_EMRs.asp

Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., et al. (2009). Use of Electronic Health Records in U.S. Hospitals. New England Journal of Medicine, 360(16), 1628–1638. doi: 10.1056/NEJMsa0900592.

Longman, P. (2009, August). Code Red: How Software Companies Could Screw up Obama’s Health Care Reform. Washington Monthly. Retrieved August 1, 2009, from http://www.washingtonmonthly.com/features/2009/0907.longman.html

Rundle, R. (2001, December 10) In the Drive to Mine Medical Data, VHA Is the Unlikely Leader. Wall Street Journal, New York, p. 1.

Young, K. (2009). Diagnostic Data Mining for Multi-modal Brain Image Studies. Veterans Health Administration Center for Imaging of Neurodegenerative Diseases. Retrieved August 2, 2009, from http://www.cind.research.va.gov/research/multi_modal_brain.asp

Good Saturday morning! We’re back to work with some Research in Progress presentations.

Rajeev Bukralia (DSU student, BHSU Dean of Academic Outreach), “Predictive Modeling to Improve Retention of Online Students”

  • Rajeev is looking into what variables influence retention of online students, predictors like high school GPA and ACT scores
  • focusing on course retention, not program retention or graduation
  • study looks at ACT, HS GOA, current college GPA, previous history of dropping course, credits completed, financial aid status, gender, age as indep. variables
  • dependent variable: final grade posted (did student complete course)
  • degree-seeking status, financial aid status, college GPA strong predictors
  • HS GPA and ACT not strong predictors in RB’s results! This contradicts previous published results on overall retention in terms of degree completion.
  • prev research hypothesized age would be important factor; RB’s results say nope!
  • Up next:
    • compare results with neural network model
    • include more variables
    • include other institutions
    • build and validate predictive model
    • develop system prototype to analyze incoming students and flag at-risk students, help advisors act proactively

Sheila Miller (presenting), Kristy Lauver, and Dawna Drum, “Is This What I Signed up for? Undergraduate and MBA Perspectives of Online Classes”

  • comparing student attitudes toward on-campus and online biz/MBA programs
  • why students select online classes, whether retention is worse
  • results so far: undergrads think online courses will be easier, take less time; MBA students like flexibility, have more classes offered only online

Ken Pinaire, “An Overview of Barriers to the Adoption of Electronic Medical Records”

  • Small practices (1-9 docs) account for 80% of all docs
  • EMR adoption rates much lower in small practices… which is a bummer, since it’s easier to get big orgs to adopt
  • Big barriers:
    • Cost
    • Privacy and security (but various studies find privacy and security are at the bottom of the concerns lists! Still, SD study finds deep consumer suspicion)
    • Patient education
    • Standardization issues
    • Training
    • Productivity and workflow issues

David Bixby, “Emotional and Social Intelligence in Project Management

  • Bixby is a PMI-certified project management professional with 24 years of experience (that means listen up!)
  • PMBOK is a 500-page document with just 5 pages dedicated to leadership issues; that got Bixby thinking
  • 20% of world GDP in 2009 will be project costs (that’s $12 trillion)
  • project failure rates (Standish 2009) down around 20%; Gartner 2008 finds 20-24% of projects cancelled
  • “human factors tend to drive non-determinism and variance” in project success (i.e., we screw things up)
  • emotions react faster than cognition; emotional intelligence is thus important to successful leadership
  • social intelligence: understanding and navigating human relationships; forms “open limbic system,” an emotional network among participants, shared emotions
  • 70% of communication is non-verbal; that’s why you need SI! (but if you hear that some UCLA study says 93% of communication effectiveness comes from non-verbals, be careful! that’s bogus!)

Peter Knight and Wei-Jun Zheng (presenting), “Factors Affecting the Effectiveness of e-Mentoring Program in Non-Profit Organizations”

  • imentor.org (2nd most features)
  • www.csv.org.uk
  • mentornet.net
  • icouldbe.org (most features)
  • Mentor Scout
  • Insala
  • systematic understanding of factors influencing effective e-mentoring programs still lacking
  • important thing to look at: matching mentors and mentees
  • some findings: same-sex and same-race mentor relationships allow easier communication. Lit suggests similar personalities should also be positive factor, but that’s not so solidly confirmed.

Daniel Power, Susan Wurtz, Dale Cyphert, and Leslie Duclos, “Building Virtual Iowa in Second Life: A Case Study”

  • More info at YouTube and Iowa Metaverse.com
  • inside spaces don’t work as well in Second Life! design open outdoor spaces
  • started in May 2007; established Decision Support World in summer 2007
  • Power bought the Iowa island March 2008
  • target audience is Iowans, former Iowans, and friends
  • bringing Iowa businesses in to meet; one biz interviews on SL!
  • “I spend too much time in Second Life, and my wife reminds me of that all the time.”
  • cost is problem; NSF does have grant program for virtual worlds
  • Iowa costs $1700 a year to maintain; adding/enhancing costs more in labor
  • university feels a bit weird about payign for virtual land… in Linden dollars
  • biggest issue: learning time, adult content
  • building started as 64cm x 64 cm whiteboard square. Island max: 256×256 cm
  • building a 3-D virtual space is very different from designing a website

Paul Weist, “An AHP-Based Decision-Making Framework for IT Service Design”

  • AHP: analytic hierarchy process; structures multiple criteria into hierarchy, prioritizes relative importance
  • decision table and decision tree require more subjective inputs
  • Paul hopes to dissert on this! Yahoo!