Social Value of Online Communities

Social media might be taking precedence in our lives these days, but there is another form of online communication that we used to (and for some of us still) rely on for exchanging knowledge or receiving emotional support from strangers – the online forums or bulletin boards. For example, Stack Overflow features a Q&A platform for software developers to exchange coding knowledge. Slickdeals, a deal and promotion-sharing forum, on the other hand, has a typical forum structure that has threads, original posters, and responses to those original posters.

Various streams of research have been conducted on online communities, from motivations behind participants in contributing time and knowledge in helping strangers, to the economic value of such contribution. What is lacking is the social value of online communities. Scholars Goh, Gordon and Agarwal (2016) aim to bridge this gap by looking at how an online community addresses the health disparity of rural and urban populations. They are also the first to quantify the social value of online communities.

Their assumptions are as follows: there is limited access to resources such as specialized care, information, healthcare programs, and social support groups in rural areas, which creates significant disadvantages for rural patients. Therefore, rural patients tend to have decreased health status and health functioning, possess less health knowledge, and have lower health-seeking skills, beliefs and self-efficacy. Together with other health capability gaps, rural patients are more likely to have poorer health statuses and higher mortality rates than urban patients. Given rural patients’ disadvantage relative to the urban patients, these researchers suggest that online communities can reduce the health capabilities gap experienced by rural patients by enabling the exchange of social support, in the form of both health information exchange and emotional support. Moreover, to the degree that community interaction has a relatively more positive effect for rural patients, they hypothesize that online communities generate social value by reducing rural–urban health disparities.

To prove their hypothesis, they collected message data on a rare disease online forum posted by 111 rural patients and 527 urban patients from October 2005 through June 2009. They adopted a network methodology in studying the knowledge and emotional exchange among original posters and responses. To illustrate, each node in the network represents a patient who participated in the forum. There is directionality associated with support provisions such that a supportive tie between a patient who posts a thread and a response from another patient is represented by a directed dyadic tie, where the arrow points toward the originating poster and the arrow head terminating at the recipient (e.g., a patient whose initial post generates a reply in the thread would have a tie that is directed toward her).  If their hypothesis is correct, it should show that the rural nodes are more likely to be recipients and urban nodes are more likely to be providers of social support.

Their findings suggest the following: the probability of a node with an incoming tie is 7 percent higher for a rural node as compared to an urban node. In other words, all else equal, a rural patient is more likely to receive support compared to their urban counterparts. They also find that rural patients are less likely than their urban counterparts to provide support.

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The research suggests that support online flows in one direction

Taken together, these results show that the likelihood of an urban patient responding to a rural patient is higher than the likelihood of responding to another urban patient, all else equal, and therefore providing support for the claim that there is a net surplus of social support flowing from urban to rural users.

Their results yield implications for policy makers and practitioners concerned with meeting patient needs and overcoming disparities in medical access. Entities responsible for resource allocation decisions, such as governments, community agencies, and public health facilities should leverage the powerful role that online collectives can play. Online communities can serve as a low cost alternative to or as a complement to existing health programs. For instance, healthcare entities can have professional nurses or doctors participate in these communities by providing information in addition to regular patients. Such information shouldn’t replace necessary office visits. Rather, it can guide the patients in the right direction and serve as a conduit towards further examination.

 

Reference:

Goh, Jie Mein; Gao, Guodong (Gordon); and Agarwal, Ritu. 2016. “The Creation of Social Value: Can an Online Health Community Reduce Rural-Urban Health Disparities?” MIS Quarterly, (40: 1) pp.247-263.

Disclaimer: This Blog is for educational purposes only as well as to provide general information and a general understanding of the topics discussed.  The Blog should not be used as a substitute for legal advice and you are advised to seek additional information from your insurance carriers, Medicare and/or Medicaid agencies for additional criteria and regulations regarding these services.

Does new health IT adoption in hospitals actually impact patient outcomes?

In my last post we talked about how to employ a successful health IT implementation at a hospital. After hospital staff accept and get accustomed to the new processes that are brought by the health IT solutions, a natural question that follows would be how effective these health IT solutions are. In other words, how does health IT adoption in hospitals impact patient outcome? Researchers McCullough, Parente, and Town published an article in 2016 on the RAND Journal of Economics examining exactly this question.

To study this question, they compiled IT adoption data from 4000 hospitals as well as diagnosis and outcomes of their Medicare, fee-for-service (FFS) patients during 2002-2007. The IT solutions they looked at are the Electronic Medical Record (EMR) and Computerized Provider Order Entry (CPOE). necEMRs systematically collect patients’ health information replacing traditional medical charts. CPOE allows providers to electronically enter medical orders for patient services and medications, thus reducing opportunities for miscommunication between disparate care providers. They studied the effect of EMR and CPOE on 3 types of patient outcomes: 60-day mortality rates, length of stay and 30-day hospital readmission.

They hypothesize that Health IT solutions positively affect patient outcomes through two mechanisms: 1) clinical decision support, and 2) information management and care coordination. Clinical decision support can include things like providing rule-based treatment guidelines or preventing drug prescribing errors. Health IT can support information management and care coordination because many conditions require extensive monitoring and testing, and generation of large quantities of clinical information. Health IT solutions can be used to capture and organize these data, therefore expediting and improving treatment decisions. When patients need multiple specialists to work together to come up with a treatment plan, IT solutions can help physicians access their colleague’s treatment decisions, therefore reducing communication and coordination barriers.

In studying patient outcome, they focus on 4 conditions: acute myocardial infarction (AMI), congestive heart failure (CHF), coronary atherosclerosis (CA) and pneumonia. These conditions were selected because they are common, mortality is a common outcome and health IT can plausibly reduce medical errors and improve the quality of care.

At first, their research findings suggests that health IT adoption does not affect outcomes for the median patient. As they dug deeper, they found that the actual impact of health IT adoption on patient outcomes is more subtle. They decomposed patient conditions at different severity levels and found that while health IT has no measurable benefits for relatively healthy patients, it significantly decreases mortality for relatively high-risk PN, CHF and CA patients. In other words, the effect of healthcare IT is small for low-severity patients but the benefits from IT adoption increase with severity. Their results also show little support for the hypothesis that health IT improves quality through rules-based decision support. Rather, health IT improves quality by facilitating coordination and communication across providers and by helping providers manage clinical information.

Their findings also showed that health IT adoption affects patient outcomes differently and the effect on conditions varies, too. They found no effect on AMI and no relationship between health IT and either readmissions or length of stay. Rather, they found an average mortality reduction of approximately 200 deaths per 100,000 admissions from IT adoption. The impact is largest for PN where IT adoption is estimated to prevent 500 deaths per 100,000 admissions while IT adoption reduces approximately 10 deaths per 100,000 admissions for both CA and CHF.

These days more and more hospitals are adopting health IT solutions like the EMR (https://healthintegrity.blog/author/hihealtherecords/). This research shows that they are most effective for patients with severe diagnoses and they can reduce mortality rate by improving information management and coordination.

 

 

Citation:

Jeffrey S. McCullough, Stephen T. Parente, and Robert J. Town. “Health Information Technology and Patient Outcomes: The Role of Information and Labor Coordination.” The RAND Journal of Economics. Vol. 27, no. 1 (2016): 207-236.

  • Disclaimer: This Blog is for educational purposes only as well as to provide general information and a general understanding of the topics discussed.  The Blog should not be used as a substitute for legal advice and you are advised to seek additional information from your insurance carriers, Medicare and/or Medicaid agencies for additional criteria and regulations regarding these services.

Growing Pains

Information Technology (IT) implementation in a healthcare setting like a hospital is a complex process. The nature of work in patient care delivery is intrinsically complicated, time-sensitive, and highly uncertain. In order to ensure quality, hospitals adopt rigorous sets of clinical routines that caregivers must engage in as they administer patient care. IT adoption however, from early CT scanners and radiology imaging devices to more recent software applications that collect electronic healthcare records, can bring disruptions to the clinical routines that healthcare providers are accustomed to.

Under such circumstances, what are the underlying mechanisms that would lead to a successful IT adoption in a healthcare setting? This question motivated a study by Goh, Gao, and Agarwal published in 2011. In an extensive longitudinal field study, those researchers observed the process when a large hospital shifted its paper-based clinical documentation to a computerized documentation system (CDS) and studied the CDS implementation through two key routines in the hospital: rounding routine and consulting routine.

They found that three mechanisms constitute the CDS implementation and these mechanisms evolved from the pre-implementation phase to the transition phase and again to the refinement phase. Those three mechanisms are: 1) technology capabilities and what users can do with the technology; 2) users’ perceived value of the technology, which is more symbolic and abstract compared to the first mechanism; and 3) the role that leadership and personal innovativeness plays in steering the first two mechanisms.

To illustrate, before CDS launched, potential users in the hospital envisioned that CDS would bring legible and timely information and reduce redundancy. They believed that CDS would provide such values as enhancing care quality, improving patient safety, and enhancing efficiency. Leaders were motivating training and education opportunities in the hospital and individual users were actively preparing for changes to their routines.

frustratedAfter CDS went live however, users found that the new user interface slowed them down in typing, there was not enough computers on wheels (COWs) to handle CDS, billing codes in the system were incomplete, and there were missing features such as the ability to track communications between healthcare providers about the patient.  At that point, users thought that CDS had led to loss of productivity, a decrease in quality, and lowering of safety and accountability. Facing these challenges, leadership tried to restore the confidence among users about the potential of CDS. The users, in turn, worked to develop workarounds to address the issues they encountered with CDS.

As they continued to use CDS, healthcare providers explored the use of the more advanced features in CDS. For instance, they developed specialized templates to reduce the number of clicks required to create a note and created a list of common diagnoses codes to speed up the search process. In addition, the users began to associate positive values with the CDS, such as performance improvement and providing physicians with a sense of autonomy.  Attending physicians, who were held high in regard in the hospital, began to build tentative optimism about the CDS implementation. Individual users also began to be creative with the system and use the technology for other than its explicitly intended purposes.

The process of refinement was continuous and ongoing and the CDS was integrated as part of the rounding and consulting routines in the hospital. From users’ interaction with and perception of the technology, the implementation of CDS was deemed successful six months after rollout.

This study has implications for both the creators and consumers of health IT solutions. For creators, normally the focus is on developing technological capabilities. While doing that, creators also need to be mindful about how technologies might disrupt users’ work routines. More importantly, creators need to understand how users’ perceived value or symbolic expression of the technology might evolve over time and should consider those aspects while refining the solution itself. change-948005_1920

For consumers of the technology, the take-away from the study was that it is inevitable that users will need to develop workarounds while interacting with the technology and they are encouraged to employ innovativeness and suggest refinements in ways in which new technology can be used. Gradually, the health IT solutions will influence and adapt to the routine of the work process.

 

Citation

Goh, Jie Mein, Guodong Gao, and Ritu Agarwal. “Evolving work routines: adaptive routinization of information technology in healthcare.” Information Systems Research 22.3 (2011): 565-585.

Introduction

When the words anthropology and business are put together, most people might find it hard to draw connections between the two. At least it took me a long time to reconcile the competing logics between them as I was getting a graduate degree in each. My anthropological background makes me sensitive to humanity and the cultural aspects of life, while my business and IT experience allows me to converse topics that are ubiquitous in today’s age.

Getting graduate degrees in these disciplines has also equipped me with the ability to decipher research puzzles that academics spend their life studying. Researchers have the capability to ask hard questions and explore uncharted territory, all using their curiosity. While this is all good, how can the knowledge transfer to our daily life? Many media outlets are doing this heavy lifting: the NASA channel on Verge.com and Science Friday from NPR are just some examples.

What I will be trying to do here is hand pick some top-notch academic articles on Health IT that are relevant to the broad mission of Health Integrity and explain how they are conducted using layman language. Creativity is an important path to the advancement of humanity and personal happiness, and in my mind, research is one of the most creative processes. I hope that my efforts assist readers like you to get to know more about academic research, with a focus on Health IT.

I am not trying to convince everyone that these research articles represent truth. The post-modernism in anthropology has told me long time ago that there isn’t one absolute truth out there. Rather, I will share with you a perspective and some facts on a social/economic/cultural phenomenon in Health IT that some researchers have provided based on their rigorous research.  It is up to you as a reader to draw your own conclusion based on your experience.

 

Do online doctor ratings reflect public perceptions of doctor quality?

When it comes to selecting doctors in this country, it’s almost like searching for a rare collectible on eBay – you need the knowledge to spot the mint condition item out of a group of seemingly similar candidates, and you need some luck. The only trick is that the “mint condition” of a doctor is hard to define. In China, where I come from, we go to a hospital to see doctors. Hospitals are graded and, presumably, better hospitals employ better doctors. In the U.S., when most doctors have the required credential to practice, how do you find a doctor that you can rely on and trust? Word of mouth! You might say. And, in the digital era, online reviews and ratings!

Are online reviews about doctors informative and trustworthy? Do they reflect quality of a doctor? If you have talked to any doctor friends about online reviews, 9 out of 10 times they will tell you that those who post reviews online are the disgruntled patients, so you shouldn’t trust those reviews. However, are these anecdotal stories representative of the wider pattern? Is there any relationship between online doctor ratings and public perceptions of doctor quality? A group of Health IT researchers affiliated with the CHIDS of the Smith School of Business examined exactly this question.

In their study “Vocal minority and silent majority: how do online ratings reflect population perceptions of quality” published in MIS Quarterly, Gao, Greenwood, Agarwal and McCullough examine 1,425 general practitioners in Denver, Kansas City, and Memphis, 794 of whom have been rated online. They use data from the consumer advocacy group Consumers’ Checkbook to measure patients’ underlying offline perceptions of physician quality and use data from RateMD.com to measure physicians’ online ratings.

Their research reveals three notable findings. First, physicians with low patient-perceived quality are less likely to be rated online. In other words, as physician quality increases there is a corresponding increase in the probability of the physician receiving an online rating. Not only that higher-quality physicians are more likely to be rated online, but their second finding also suggests that these physicians tend to receive higher ratings as well. This indicates that online ratings are informative to patients and do reflect population opinions of physician quality. Their third finding reveals a very interesting phenomenon. While higher online ratings reflect higher quality, online ratings are most effective at distinguishing physicians with average quality; they are less sensitive to physicians with low quality, and have no ability to distinguish quality variation of high-quality physicians.

What’s the major take-away for those of us who want to use online ratings to select doctors? First, both doctors and we as patients can rest assured that online ratings are not just composed by unhappy consumers of healthcare. Second, if a doctor does not have online ratings, you can infer that this doctor’s quality tends to be low. Third, higher online ratings reflect higher quality, although such correlation gets weaker for doctors with high ratings. Lastly, we shouldn’t be too picky about minor differences in online ratings among high-end physicians. For example, a rating of 4.9 vs. 5.0 might not reflect meaningful quality difference.

Cite:

Guodong Gao, Brad Greenwood, Ritu Agarwal, and Jeffrey S. McCullough “Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality?” Management Information Systems Quarterly. Vol 39, no. 3 (2015): 565-589

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