The Medicare accountable care organization (ACO) program financially rewards ACOs for providing high-quality healthcare, and also factors in the patient experience of care. This study examined whether administrative measures of wait times for specialist consults are associated with self-reported patient satisfaction. 

 Analyses used administrative and survey data from a clinically integrated healthcare system similar to an ACO. 

 Veterans Health Administration (VHA) data from 2012 was obtained. Administrative access metrics included the number of days between the creation of the consult request and: 1) first action taken on the consult, 2) scheduling of the consult, and 3) completion of the consult. The Survey of Healthcare Experiences of Patients—which is modeled after the Consumer Assessment of Healthcare Providers and Systems family of survey instruments used by ACOs to measure patient experience&mdash;provided the outcome measures. Outcomes included general VHA satisfaction measures and satisfaction with timeliness of care, including wait times for specialists and treatments. Logistic regression models predicted the likelihood of patients reporting being satisfied on each outcome. Models were risk adjusted for demographics, self-reported health, and healthcare use. 

 Longer waits for the scheduling of consults and completed consults were found to be significantly associated with decreased patient satisfaction.  

Because patients often report high levels of powerlessness and uncertainty while waiting for consultation, these wait times are an important patient-centered access metric for ACOs to consider. ACOs should have systems and tools in place to streamline the specialist consult referral process and increase care coordination.

The American Journal of Accountable Care. 2017;5(1):23-28

The Medicare accountable care organization (ACO) program financially rewards ACOs for providing high-quality healthcare. Participating providers are then financially rewarded if healthcare spending is kept below targets set by Medicare, which can be achieved by the prevention of medical errors and duplication of services.

 The success of ACOs assumes that a structure of clinical integration and appropriately targeted incentives will improve coordination of care and quality.

 A key measure of quality of care is patient experience of care, including perceived coordination. Using the Consumer Assessment of Healthcare Providers and Systems (CAHPS) family of survey instruments, ACOs are required to collect information on patient experiences, such as the ability to obtain timely care and access to specialists.

Care coordination between primary and specialty care has received little attention despite the fact that specialty care accounts for more healthcare resource use than primary care, and specialists outnumber primary care physicians in the United States.

 Primary care and specialist providers report inadequate communication between each other about referrals, which compromises their ability to provide high-quality care

 and may also have a negative impact on self-reported patient satisfaction. 

Patients report high levels of uncertainty and powerlessness during the period of time between a requested referral and subsequent action, as they wait for clarity on disease outcomes.

 Consequently, if patients experience inadequate coordination between primary and specialty care, their experiences may suffer. Previous research has found that shorter wait times for appointments are not always the most important priority for patients. Patients are willing to wait longer to get an appointment at a convenient time or to see a preferred provider, especially for low-worry, long-standing conditions. Yet, when there is a new health concern, faster access becomes a higher priority.

The Veterans Health Administration (VHA)—the largest clinically integrated system similar to an ACO in the United States that coordinates primary and specialty care&mdash;is a source of data that can provide important insights into the effect of the consult process on patient experience. In 2014, the VHA had over 9 million enrollees and provided or coordinated over 92 million outpatient encounters.

 Since 2009, the VHA has consistently measured patient experiences and satisfaction with the Survey of Healthcare Experience of Patients (SHEP) Survey that is modeled after the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey that ACOs use to measure patient experiences.

 This paper investigates the effect of several different measures of VHA consult wait times on self-reported patient satisfaction. This effect on patient satisfaction provides a specific point of intervention for ACOs to consider when thinking of ways to measurably improve patient experiences. 

As discussed in detail below, analyses used administrative data on wait times for consults in the VHA to predict self-reported patient satisfaction with care. Study approval for human subjects was obtained from the VA Boston Healthcare System Institutional Review Board. 

Administrative Consult Wait Time Measures

The VHA’s electronic consult system was implemented in 1999 and its use is mandated for all consultation requests (according to internal VHA data). Data from this study was extracted from fiscal year (FY) 2012. The consult system automatically records time stamps when consult-related administrative events occur. These events can be used to understand the number of elapsed days between consult creation and first action and/or scheduling. An additional measure included total consult resolution time (

); in other words, consults are considered resolved when the appointment (if performed) has been completed and the report is written and signed. Time stamps are also recorded when the consult is updated, discontinued, or returned to the sending service for clarification. When consults are returned for clarification of the request, the consult wait time clock is not reset. The time stamps include the time from consult creation through appointment scheduling, and eventually to consult completion. In contrast, discontinued consults stop the clock.

Standardization of the consult system enables distinctions between documents used for traditional clinical consultation and those used for other administrative purposes, such as for requesting transportation.

 With this in mind, we narrowed the 2012 data to focus on consults for clinical services by excluding administrative consults and non-VHA care consults. 

Following our previous work, the wait times were weighted by a national proportion based on FY2011 data. Weights were developed based on the frequency of different consult appointments. If a station did not have a consult request for every type of appointment in a month, the remaining appointment weights were adjusted so they would sum to 100.

 Wait time measures were used in 2 ways in statistical models: 1) as a continuous variable; and 2) categorized roughly into quartiles, with the lowest quartile used as the reference group.

Consult wait times were linked to self-reported satisfaction using the 2012 Survey of Healthcare Experiences of Patients (SHEP), which is modeled after the Consumer Assessment of Healthcare Providers and Systems (CAHPS) family of survey instruments. Managed by the VHA Office of Analytics and Business Intelligence, SHEP is an ongoing nationwide survey that seeks to obtain patient feedback on recent episodes of VHA inpatient or outpatient care. For outpatient care, a simple random sample of patients with completed appointments at VHA facilities is selected each month (according to internal VHA Support Service Center data). The overall response rate was 53% and respondents came from all VHA medical centers (n = 130). 

Different sample selection rules were applied to each consult wait measure. First, all individuals who had the visit date in SHEP match the date for a completed/updated consult were flagged. In addition, we required the station (medical center code) in SHEP to match the station of the completed consult. This was the sample for the completed consult wait measure (n = 28,328). The wait computed was the facility average for all resolved consults in that month. Not surprisingly, because a clinic visit triggers a patient to be eligible to be contacted for SHEP, 90% of the individuals in this sample had a completed/updated status compared with a discontinued or canceled status. 

For the next 2 measures—days to first action and days to scheduled consult&mdash;all individuals who had a visit date in SHEP match the date a consult was initiated were flagged (n = 44,387). The receiving (not the sending) station for these requests was linked because receiving stations actually do the scheduling. We computed a facility average wait time looking forward for all consults requested at that receiving station in the month. 

Satisfaction measures were selected and operationalized following previous work.

 Satisfaction with timeliness of care was measured by asking respondents how often they were able to get VHA appointments as soon as they thought they needed care, excluding times they needed urgent care. Access to VHA tests, treatments, and appointments with VHA specialists was measured by asking how easy it was to get this care in the last 12 months. Response options for the above 3 measures included “always,” “usually,” “sometimes,” and “never”; we estimated the likelihood of answering always/usually compared with sometimes/never. We also examined more general satisfaction measures that wait times for consults may influence. General satisfaction is measured by asking respondents to rate VHA healthcare in the last 12 months on a scale of 0 to 10 and their satisfaction with their most recent VHA visit using a Likert scale ranging from 1 to 7 (higher numbers indicate greater satisfaction). We estimated the likelihood of a 9 or 10 rating compared with less than a 9 on the first measure, and the likelihood of a 6 or 7 compared with less than a 6 on the second measure. 

Risk adjustors included age, gender, race/ethnicity, education level, number of visits to a doctor’s office in the last 12 months, and self-reported health status—all obtained from SHEP FY2012. Models also included month fixed effects to control for secular changes in wait times and a VHA medical center random effect to control for facility quality and case-mix differences. 

STATA version 10.0 (Statacorp, College Station, Texas) was used to estimate logistic regression models that predicted the dichotomized patient satisfaction variables. 

The SHEP respondents selected for this sample reflect the larger VHA patient population. Respondents were predominantly male, in poor health, and frequent healthcare users. There is evidence of high satisfaction with VHA care, with nearly 80% of respondents reporting they usually or always received appointments, treatment, or specialist care in a timely fashion. Additionally, 81% expressed the top 2 highest satisfaction levels for the most recent visit and 58% expressed the highest satisfaction levels with VHA care in the last 12 months (

There is significant variation between facilities regarding consult wait times. Facilities in the top quartile have waits that are more than 10 days longer than facilities in the lowest quartile (33.5 days vs 23 days) for consult completion. Facilities in the highest quartile took about a week longer to schedule appointments in response to a consult request than facilities in the lowest quartile. There was very little variation in time to first action, with less than a half-day difference in the highest quartile compared with the lowest quartile (

The measures for completed consult and time to scheduled consult have strong and consistent relationships with patient satisfaction (

). Generally, there is a linear relationship with satisfaction decreasing for patients who visit facilities with longer waits (the higher quartiles of wait times). Patients who visit facilities in the highest quartiles of wait times are significantly less satisfied than patients who visit facilities in the lowest quartile for these 2 measures. Sensitivity analyses that included wait times as a continuous measure found that longer waits were significantly associated with lower satisfaction on these measures for every outcome except the model using the completed consult wait to predict the overall VHA satisfaction measure (data not shown). There was no significant relationship between the days to first action measure and patient satisfaction.

This study finds a consistent relationship between measures of consult wait times and patient satisfaction. Longer waits between initial request and either scheduling of the consult or the completion of the consult are associated with poorer satisfaction. Generally, there was a stronger negative relationship between waits and satisfaction measures that were specifically related to accessing care, treatments, or specialists compared with more general satisfaction measures. There was no relationship between the waits for time to first action and patient satisfaction. 

These findings are consistent with previous research that validated access metrics for appointments using patient satisfaction as an outcome. Prentice et al (2014) found that different types of access metrics predicted patient satisfaction for new and returning patients.

 Specifically, the wait time between appointment creation and appointment completion for new, but not established, patients strongly predicts patient satisfaction. One reason this relationship pertains to new patients but not established ones may be because new patients typically want to be seen right away due to emerging health concerns. This previously validated measure is consistent with the time to scheduling and time to completion measures used here that predict the same relationship between consult waits and patient satisfaction. Patients being referred to specialty care are likely to have new problems and want to be seen as soon as possible after the need is identified.

The findings from this study also expand our understanding of administrative access metrics that are patient centered. In contrast to the measures, days to scheduled consult and days to completed consult, the days to first action measure had no relationship with patient satisfaction. This metric largely measures “behind the scenes” processes of transfer and scheduling of consults. Patients repeatedly reported a sense of powerlessness and uncertainty, as well as a feeling of “living their life on hold” when waiting for diagnosis or treatment that is compounded by a lack of information from the healthcare system.

 As ACOs and the VHA put a greater emphasis on the experiences of patients, these findings suggest that metrics should focus on measuring tangible processes that patients easily understand as action being taken on their behalf, such as scheduling appointments. 

Given that longer waits for consults negatively impact patient experience, ACOs should have systems and tools in place to streamline the consult referral process and increase care coordination. These tools include policy, training, appropriate technological support tools, and organizational culture. Greenberg et al (2014) argue for the importance of developing a collaborative care agreement that delineates the roles of the referring and consulting provider in the preconsult exchange, the actual consultation, and any co-management of the patient required after the consult is completed.

 Others argue that ACOs need to specifically focus on improving the structure of care coordination. This includes appropriate training to all providers and care teams on best practices in care coordination, technological support tools to help providers get a complete picture of a patient’s care, as well as to achieve care coordination goals and a culture that prioritizes such coordination, including protected time during the workday for care coordination activities.

The main limitation of this study is that we cannot definitely state that the relationship between longer consult wait times and lower patient satisfaction is causal because omitted variables may be responsible. Our models attempted to minimize this possibility by including month fixed effects to control for secular changes in wait times and a medical center random effect to control for facility quality and case-mix differences. On the other hand, research has repeatedly found a relationship between longer wait times and poorer outcomes, including decreased patient satisfaction and poorer health outcomes in a variety of veteran populations and time periods, thus strengthening the inference that the relationship may be causal.

Another limitation is that the study sample is largely elderly and male, so results may not be generalizable to other patient populations. Finally, wait times for all types of consults may not have the same impact on patient satisfaction. Due to data availability at the time of the study, our measures included all clinical consults. Long waits for a recommended preventive screening (eg, colonoscopy) may not have the same effect on patient satisfaction as waits for consults that are a result of new health concerns. Future work should consider these nuances to determine consult access that has the largest impact on patient experiences. 

The consult process occurs at an anxiety-producing time for patients. Findings from this study suggest that certain types of consult waits that can be easily obtained from the scheduling system are strong predictors of patient satisfaction. As ACOs reorganize to become more patient-centered, better management of consult waits has the potential to significantly improve patient satisfaction. 

	The authors are indebted to Aaron Legler for programming support. 

Partnered Evidence-based Policy Resource Center (SDP, JCP), Boston VA Healthcare System, Boston, MA and Office of Veterans Access to Care (MLD), Department of Veterans Affairs

Washington, DC; Department of Pharmacy and Health System Sciences, Northeastern University (SDP), Boston, MA; School of Medicine and Public Health, Boston University (JCP), Boston, MA.

Funding for this research was provided by the Access Clinic and Administration Program (now the Office of Veterans Access to Care) in the Department of Veterans Affairs.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (SDP, MLD, JCP); acquisition of data (SDP, MLD, JCP); analysis and interpretation of data (SDP, JCP); drafting of the manuscript (MLD, JCP); critical revision of the manuscript for important intellectual content (SDP, MLD); statistical analysis (SDP); obtaining funding (MLD); administrative, technical, or logistic support (SDP, MLD); and supervision (SDP, JCP). 

Julia C. Prentice, PhD, VA Boston Health Care System, 150 South Huntington Ave, Mail Stop 152H, Boston, MA 02130. E-mail: jprentic@bu.edu.

1. Accountable care organizations (ACOs): general information. CMS website. https://innovation.cms.gov/initiatives/aco/. Updated December 15, 2016. Accessed December 9, 2015.

2. McWilliams JM, Landon BE, Chernew ME, Zaslavsky AM. Changes in patients’ experiences in Medicare accountable care organizations. 

. 2014;371(18):1715-1724. doi: 10.1056/NEJMsa1406552.

3. Press MJ, Michelow MD, MacPhail LH. Care coordination in accountable care organizations: moving beyond structure and incentives. 

4. CAHPS Survey for Accountable Care Organizations (ACOs) Participating in Medicare Initiatives—2016 ACO-9 Survey version required (English). CMS website. http://acocahps.cms.gov/globalassets/aco---epi-2-new-site/pdfs-for-aco/survey-instruments/2016-aco-survey/english/2016_aco-9_mail_survey_english.pdf. Accessed December 9, 2015.

5. Greenberg JO, Barnett ML, Spinks MA, Dudley MJ, Frolkis JP. The “medical neighborhood”: integrating primary and specialty care for ambulatory patients. 

. 2014;174(3):454-457. doi: 10.1001/jamainternmed.2013.14093. 

6. O’Malley AS, Reschovsky JD. Referral and consultation communication between primary care and specialist physicians: finding common ground. 

. 2011;171(1):56-65. doi: 10.1001/archinternmed.2010.480.

7. Fogarty C, Cronin P. Waiting for healthcare: a concept analysis. 

. 2007;61(4):463-471. doi: 10.1111/j.1365-2648.2007.04507.x.

8. Hansen BS, Rørtveit K, Leiknes I, et al. Patient experiences of uncertainty—a synthesis to guide nursing practice and research. 

. 2012;20(2):266-277. doi: 10.1111/j.1365-2834.2011.01369.x.

Articles

The American Journal of Accountable Care

Given that accountable care organizations (ACOs) will be rated on patient experience and wait times for specialist consults are associated with patient satisfaction, ACOs should monitor this process.

Consult Coordination Affects Patient Experience

Telemedicine aims to meet the needs of today’s healthcare consumers and has the capacity to revolutionize the delivery of healthcare. It supports efforts to significantly improve the quality of healthcare by increasing accessibility and efficiency through reducing the need to travel, providing clinical support, overcoming geographic barriers, offering various types of communication devices, and improving patient outcomes. Thus, given the current focus on efforts to contain costs, improve the delivery of care to all segments of the population, and meet consumer demand, telemedicine is an attractive tool to use for success in these areas.

The American Journal of Accountable Care. 2017;5(1):e1-e5

The use of telemedicine has been shown to allow for better long-term care management and patient satisfaction; it also offers a new means to locate health information and communicate with practitioners (eg, via e-mail and interactive chats or videoconferences), thereby increasing convenience for the patient and reducing the amount of potential travel required for both physician and patient. Web-based disease management programs encourage clients to assume greater responsibility for their own care, help healthcare providers to treat clients earlier (when they are not as ill), provide local and less expensive services, extend scarce healthcare resources, enhance follow-up care, improve client access to services, and increase the accuracy and quality of patients’ medical records.

Telemedicine connects the convenience, low cost, and ready accessibility of health-related information and communication using the Internet and associated technologies. Beginning with the use of telephone consults, telemedicine has become more sophisticated with each advance in technology, and now involves telecommunication and computers to provide healthcare information and services to clients at multiple locations. The application of this tool covers a wide and diverse scope, including: a) online databases and tools to ensure standards of care, b) critical pathways and patient outcomes, c) computer-assisted diagnosis, d) effective drug information and electronic prescription filling, and e) enhanced availability of research data. Through that information to healthcare professionals and consumers alike. Clinicians are able to handle more patients than traditional care models would typically allow, and with increased access and a more connected level of care, physicians and patients can work together to achieve their therapeutic goals, particularly in the home and hospice care environments.

This paper presents a critical review of the prevailing literature on telemedicine. 

A literature review was established by utilizing databases accessible through the Health Professions Divisions Library at Nova Southeastern University. The following databases were used to search for the sources for this project and identify current research on telemedicine: Citations and Abstracts for Literature of Nursing and Allied Health (CINHAL) plus full text, Cochrane Library, EBSCO host, and OVID. Key search phrases used in the scan included “telemedicine” and “telehealth,” which produced 6563 and 4482 results, respectively. To narrow study selection, Boolean strings were considered for the literature search, with “AND” used with the advanced search pairings, such as “telemedicine AND nursing” and “telehealth AND nursing.” Sources from the last 10 years were considered for inclusion in the review of literature. The subsequent search generated 119 and 272 results, respectively. The abstracts were reviewed and 18 articles were chosen based on their consistencies surrounding the investigation of telemedicine.

There are 4 clinical applications of telemedicine: 1) real-time interactive mode, 2) store-and-forward mode, 3) remote monitoring, and 4) communication via telephone.

 Telephone communication the most basic type of telemedicine that provides communication and remote care delivery. In real-time telemedicine, a link between the involved individuals allows a real-time interaction to take place, but requires sending patient data from one site to a remote location, with an expert available to evaluate the data. These real-time encounters use a specially equipped personal computer with a telephone line hook-up to allow people to meet face-to-face and/or view papers and images simultaneously, even though they are not in the same location. Videoconferencing is one example of this type of application.

 Store and forward ensures that all information, including digital images, video, audio, and data, is captured, stored, and sent electronically to a specialist or clinician at another facility for interpretation and feedback.

 In remote monitoring, devices intelligently acknowledge the patient, record any abnormalities, and transmit data. For example, a cardiac event monitoring device allows patients to record arrhythmia whenever they experience dyspnea, angina, palpitations, or unexplained syncope. They can transmit the data over the phone and the ordering physician is notified immediately in the event of an abnormal rhythm. The benefits of this type of technology allow clinicians to cut travel time without decreasing client contact. In addition, telemedicine services eliminate the need for visas for international patients.

Using telemedicine technology to remotely monitor health, such as through the use of smart surveillance cameras and analytical software, can be used with elderly clients to notify their caregivers of changes in activity, falls, or lack of movement. This type of care can reduce costs, potentially keep these older individuals in their own homes longer, and help physicians to more easily tailor treatment according to a patient’s choices and availability of services.

 This is particularly important as the 65-years-and-older population explodes without a concomitant increase in funds for healthcare services. It is estimated that by 2030, individuals 65 years or older will represent 20% of the US population. Therefore, Medicare and Social Security programs will face financial challenges as the ratio of individuals paying taxes to retirees receiving benefits will drastically diminish.

Use of telemedicine also has the potential to help patients become more involved in their healthcare plan and increase their autonomy.

 Patients who require wound care are another population that can be managed well at home through telemedicine applications. For instance, telemedicine facilitates communication among: a) a tissue viability nurse at a patient’s home who evaluates the patient’s condition via wound description and photographs that are entered into the database, b) another tissue viability nurse working in an outpatient clinic at a different hospital, and c) a physician who is highly interested in wound care working in a different healthcare facility.

A Web-based solution for care coordination can integrate information from biometric measures and diagnostic tests and automatically alert clinician of panic values. As an example, “Health Buddy,” an in-home communication device, can be used to provide heart failure disease management,

 with biometric measurements (eg, heart rate and pattern, blood pressure, respiratory rate, fetal heart rate) able to be monitored at another site. Women with high-risk pregnancies, individuals with diabetes, and cardiac and postoperative patients can also be monitored at home. The device prompts the appropriate patients to take their medicine and keep their legs elevated when sitting and monitors subjective reports of difficulty breathing or increased edema. On the other end of the connection, nurses receive alerts when problems are indicated. 

Telemedicine is plagued by a number of liability concerns.

 First, there is the possibility that a patient may perceive it as inferior because the consulting professional does not perform a hands-on examination. Therefore, the distant provider, who has not personally examined the patient and may be relying on another presenter, might not be able to render a fully informed opinion or could end up with results that are inaccurate, incomplete, or misleading. Major issues include questions of liability when information provided over the telephone is misinterpreted.

These 2 items represent major barriers to the growth and practice of telemedicine and need to be addressed in the context of technology-enhanced interventions.

 There have been incidents in which practitioners were eligible for reimbursement of the costs associated with telemedicine services and problems with how they are paid. Further, although healthcare professionals are only licensed to practice within certain jurisdictions, telemedicine requires multiprovince licensure, both for their primary province and for the jurisdiction in which services are rendered. Applications for licensure in different provinces can be lengthy and expensive, with the ultimate result being restricting access to services. There are different practice provisions across the country, and the uncertainties related to licensure may be subject to malpractice lawsuits and questions about how that liability might be distributed, which will continue to hamper access to telemedicine.

Telemedicine should not create greater concerns about or risks to medical record privacy than any other form of consultations—in the United States, it is subject to Health Insurance Portability and Accountability Act (HIPAA) regulations.

 Although patients require continuous support and education, the privacy, security, and confidentiality of their data must be maintained at all times. Only authorized users—those who are directly involved in the ongoing care and treatment of a patient&mdash;and those having a legal right and clear need to approach the systems where the information resides have access to this information. This restricted access increases patient safety and reduces anxiety regarding misuse and availability of personal information. Maintaining the privacy and confidentiality of telemedicine services is crucial to acceptance by consumers and healthcare professionals; these providers must adhere to all data privacy and confidentiality guidelines.

 Nurses and other healthcare professionals need to be mindful of these issues, especially when technicians not bound by professional codes of ethics are present at telemedicine sessions.

Protection of information and computer systems should receive top priority. Security mechanisms use a combination of logical and physical restrictions to provide a greater level of protection, including firewalls and antivirus and other software that detects malicious programs and spyware. An example of a logical restriction is automatic sign-off; the operating system should lock down after a specified period of inactivity.

 In addition, the constant creation of new viruses makes it necessary to update antivirus software often. These measures should be reevaluated periodically to determine what modifications need to be made.

Information security training and education are important components in fostering proper system use. Most problems with information system security are primarily related to the human factor rather than the technical one. Support staff should be capable, flexible, and experienced. Technical support staff who are present during the exchange of client information need to be aware of institutional policies, procedures, and laws (such as HIPAA) that are designed to protect client privacy. These individuals should sign the same sort of statement that clinical personnel sign on when receiving their information system access code. In the case of home monitoring, support is crucial to help participants feel comfortable with the technology, particularly when using the internet and Web applications.

Additionally, secure modems and encryption are particularly useful in conjunction with remote access. System security involves protection against deliberate attacks, errors, omissions, and disasters. Good system management is a key component of a strong security framework because it encompasses monitoring, maintenance, operations, traffic management, supervision, and risk management. Greater awareness, sufficient resources, and an organization-wide commitment to information security are needed.

Speed and access to information at any time, from any place, are essential to maintaining a high quality of service; slowdowns or outages in service are not acceptable.

 Descriptions of some telemedicine applications describe inadequate funding to establish and maintain the technological infrastructure needed.

 In certain cases, nurses are responsible for the set-up and basic support of telemedicine devices. Although the wisdom of this approach may be questioned in light of the limited availability of nurses, it can be used as an opportunity to establish rapport and comfort with the technology.

 Equipment capable of transmitting and receiving diagnostic-grade images can be cost-prohibitive—although costs are declining&mdash;but it is significantly less costly than that of an inpatient admission.

There are 2 other major issues surrounding the quality of telemedicine services. The first is that services must be at least of the same quality as traditional services, particularly for reimbursement services. The second is the paradox that geographically isolated populations stand to derive the largest benefits from telemedicine although they have limited access to traditional healthcare services and often have the poorest infrastructure, resources, and capability to support telemedicine.

 Telemedicine visits can require extra time for equipment management and transmittal of prescriptions.

 There is also a need for extensive research to establish effectiveness and cost and quality relationships.

Despite its advocates, many healthcare professionals have been slow to accept the application of telemedicine.

 Some are simply resistant to change unless they see the potential benefits, while other reasons include the perception that telemedicine applications are not indicative of “real” nursing—this may stem from liability concerns and discomfort over not seeing a client face-to-face&mdash;and that telemedicine applications will eventually reduce the number of healthcare professionals needed and they fear job loss as more clients can be treated at home.

There are threats to patient safety when telemedicine applications fail to render the same level of care as hands-on care or when problems occur with the use of electrical devices.

 There is also lack of acceptance by users that may arise from discomfort with technology, the relationship with the provider, and concerns over security of information and confidentiality. It is essential to educate the clients and increase public awareness. Healthcare professionals need to hold free information sessions for patients who are interested in more comprehensive information to aid them in making an informed decision and to promote a better understanding of such technology. 

Most discussions of telemedicine include the electronic health record as the primary means to make client data readily available and store diagnostic images. Picture Archiving Communications Systems (PACS) permit remote access to diagnostic images at times that are convenient to physicians. Health personnel need to shape the development of technological standards by determining the adequate image quality for diagnosis. Unfortunately, there has been insufficient funding for further development of PACS technology.

Strategies to Establish a Telemedicine Plan

Successful establishment and implementation of a telemedicine plan requires strategic planning and consideration of: a) necessary infrastructure, b) costs and reimbursement, c) human factors, and d) equipment and technology issues. In terms of infrastructure, it is essential to address how telecommunication breakdowns will be handled: will backup be provided? What happens when a power outage in the home severs a link? Technical support must be available to resolve any problems that might arise.

 Additionally, the individuals who will use the system should be involved in its design from the beginning to ensure its effectiveness and uptake. Specific competencies that must be addressed include training time to develop the technical skills needed to set up and use equipment, professional knowledge, interpersonal skills, documentation, professional development, resource management, practice and administrative issues, and security of healthcare information.

 Time is needed for healthcare providers to get accustomed to telemedicine practices; an example of this may be seen in teleradiology, where radiologists must learn how to interpret images using a monitor.

Converged devices, such as smartphones, combine the utility of cell phones and personal digital assistants, allowing users to monitor telemetry patients while performing other tasks. These devices provide the potential to increase patient safety, facilitate communication among healthcare professionals, and reduce liability (because orders can be clearly viewed), thus eliminating the errors associated with poor handwriting or verbal instructions.

The model of “connected health”—a new paradigm of care&mdash;promises to reduce costs and improve quality by working with clients proactively. Individuals monitor their own health, which results in fewer visits to physicians and inpatient hospital stays.

 The demand for telemedicine services is expected to grow exponentially as baby boomers age, and home-based care will likely continue its exponential growth as a means to help keep older patients in their own homes and better manage their conditions. Demands for quality, patient safety, and more options will help change the reimbursement picture and open the door for more telemedicine applications, new technologies will emerge to meet this need, and the use of existing measures, such as biometric authentication, will become more common.

Telemedicine significantly contributes to the provision of healthcare in underserved areas through services such as telestroke, telecardiology, teledermatology, telepediatrics, telepsychiatry, and teleneonatology care. This tool aims to improve access to care for anyone regardless of their location

 and has the potential to reduce the number of face-to-face visits. Additionally, it can help improve the organization of the health record with automatic collection of data and better coordination of care among clinicians in various locations.

In telecardiology—the transmission of cardiac diagnostic tests in conjunction with electronic stethoscope examinations for second opinions by cardiologists at another site&mdash;there may be a reduction in the need for a patient to travel by allowing the specialist to perform an assessment remotely using a digital Bluetooth stethoscope and existing telemedicine equipment. Another example is telestroke, which enhances on-demand emergency acute stroke care by using videoconferencing to connect neurologists with emergency departments at facilities without neurologists on site. With telestroke, neurologists can communicate directly with the patient and family during assessment, discuss the treatment plan with all involved, and provide comprehensive and coordinated acute stroke care. Reducing the time to receive treatment is critical in improving patient recovery. 

During the telemedicine implementation phase, providing support to facilitate transition and communicate accuracy and availability of the electronic system is very important. There is a need to provide continuing education and develop a communication plan, with useful tools like newsletters and posters, to increase awareness of telemedicine and later expand its capability. To promote adoption, a change management plan must be structured in conjunction with the telemedicine implementation design. It is essential to incorporate change agents into the program that will help implementers and users to effectively and meaningfully communicate with each other in order to address any gaps that may exist. The change agents provide education sessions related to the importance of change that are key to having healthcare providers open their minds to change and adapt the system to existing practices.

Change management provides invaluable assistance to healthcare organizations to better manage their resources and improve productivity through the use of continuous positive reinforcement and involvement of supporting resources. They will be able to troubleshoot, and their inputs for change will act as catalysts to get their active involvement in setting up new environment. Timely and ongoing training will allow healthcare educators, providers, and agencies to examine information trends and react to changes proactively. As new rules emerge and technology changes, all organizational members need to participate in the use of telemedicine to ensure wide acceptance.

Among other benefits, the use of telemedicine improves follow-up care, ensures patient access to services, and allows providers to treat patients at home and in remote areas. Some telemedicine applications include diagnostic evaluation, decision making, storage and dissemination of records, and education of healthcare professionals. Several factors are essential to establishing and using a telemedicine link: implementers should develop a plan that addresses areas such as compliance with standards, technical requirements, reimbursement, human factor considerations, and strategies to handle telecommunication breakdowns and how to deal with equipment malfunctions. 

This paper was also submitted toward a partial completion of Doctor of Health Science at Nova Southeastern University.

Department of Health Science, Nova Southeastern University, Fort Lauderdale, FL.

 Dr Alvandi reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. She affirms that her academic institution has no financial interest or conflicts. 

Concept and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content.

Maryam Alvandi, DHS, Nova Southeastern University, Fort Lauderdale, FL 33329. E-mail: malvandi@yahoo.com. 

Handbook of Informatics for Nurses & Healthcare Professionals. 

4th ed. Upper Saddle River, NJ: Pearson Prentice Hall; 2009.

2. Andrews V. Using telemedicine in clinical decision-making. 

. 2014;25(1):42-46. doi: 10.12968/pnur.2014.25.1.42.

3. Demiris G, Oliver DP, Courtney KL. Ethical considerations for the utilization of telehealth technologies in home and hospice care by the nursing professions. 

Nursing Informatics and the Foundation of Knowledge

. 2nd ed. Burlington, MA: Jones and Bartlett Learning; 2011.

5. Garrett N, Martini EM. The boomers are coming: a total cost of care model of the impact of population aging on the cost of chronic conditions in the United States. 

6. Jelnes R. Reflections on the use of telemedicine in wound care. 

7. Rosenberg H. Connecting with seniors to reduce hospitalizations. 

Given the current focus on efforts to contain costs, improve the delivery of care, and meet consumer demand, telemedicine is an attractive tool to use for success in these areas.

Telemedicine and its Role in Revolutionizing Healthcare Delivery

Geographic variation in the use of postacute care (PAC)—specifically, skilled nursing facility and home healthcare&mdash;after hospital discharge is substantial, but reasons for this remain largely unexplored. PAC use in urban hospitals compared with rural hospitals may be a key contributor. We aimed to describe PAC use, explore substitution of one type of PAC for another, and identify how PAC use varies by diagnosis in urban and rural settings.

 Secondary analysis of the 2012 National Inpatient Sample, including adult discharges to PAC after a hospitalization.  

 We adjusted for differences in patient demographics, comorbidities, hospital care provided, and hospital information, comparing the use of PAC in urban and rural settings in multivariable logistic regression.  

 Rural patients discharged from rural hospitals constituted 188,137 (12.1%) of the 1.56 million discharges in the sample. Rural discharges received less home healthcare (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.80-0.90) than urban discharges, resulting in less rural PAC use overall (OR, 0.95; 95% CI, 0.91-0.99). Rural discharges received more overall PAC for stroke (OR, 1.11; 95% CI, 1.03-1.19) and less PAC for sepsis (OR, 0.92; 95% CI, 0.86-0.98), hip fracture (OR, 0.82; 95% CI, 0.70-0.96), and elective joint arthroplasty, where rural discharges had 41% lower odds of receiving PAC (OR, 0.59; 95% CI, 0.49-0.71).  

The striking differences in receipt of postacute care in urban and rural patients may constitute a disparity. Evaluation of costs and outcomes of PAC use in these settings is urgently needed as Medicare expands bundled payments for this care.

The American Journal of Accountable Care. 2017;5(1):16-22

Use of postacute care (PAC), such as skilled nursing facility (SNF) and home healthcare (HHC), is increasing rapidly for patients discharged from the hospital.

 PAC is now the most rapidly growing area of Medicare spending, but this growth is not uniform.

 Instead, geographic variability in PAC use explains the vast majority of geographic differences in Medicare spending across the United States, and geographic differences are stable over time.

 An underexplored area that may significantly contribute to this variability is how PAC use differs in urban and rural hospitals nationally.

The location of the patient and the discharging hospital may have a strong influence on whether and what type of PAC is used. The location of the nearest SNF to the hospital has been shown to influence the use of that facility.

 Medicare’s “swing bed” policy, which allows critical access hospitals to convert an inpatient hospital bed into a rehabilitation bed during a patient’s stay, has strongly increased use of this care in rural areas.

 However, significant gaps in understanding this important area remain. Most studies of urban and rural PAC use consider only Medicare patients, despite the important roles that other payers play.

 Studies that consider both the patient’s and hospital’s location are scarce, although they both plausibly affect PAC use.

 Finally, prior studies also have not considered whether use of PAC in urban and rural areas may vary by diagnosis, despite evidence of differential use of PAC based on diagnosis type in other settings.

We sought to evaluate how PAC is used by hospitals in urban and rural areas across the United States, exploring whether there is evidence of a substitution effect of one type of PAC (eg, home healthcare) for another (eg, SNF care). We also wished to explore how this varies among patients treated for different conditions. In doing so, we hoped to provide insights into underlying mechanisms behind geographic variation in PAC use, and provide preliminary data for future studies evaluating costs, appropriateness, and outcomes of PAC in urban and rural settings.

This was a secondary analysis of the 2012 National Inpatient Sample (NIS), a nationally representative sample of discharges from hospitals in 44 states that participate in the Health Care Cost and Utilization Project (HCUP).

 In 2012, NIS covered more than 95% of the US population and more than 94% of community hospitals; long-term acute care hospitals (LTACHs) were excluded. We included all hospital discharges of patients 18 years or older in 2012. Patients who died during the hospitalization were excluded, as were admissions for neonatal or maternal care, or those that ended with transfer to another hospital. 

Urban and rural location of patient residence was defined using the 2006 National Center for Health Statistics classification scheme

: patients residing in metropolitan counties (counties of 50,000 or more people) were considered urban and patients residing in nonmetropolitan counties were considered rural. Similarly, hospitals residing in counties that are considered metropolitan by Core-Based Statistical Area are classified in the NIS as urban; hospitals in nonmetropolitan counties were considered rural. Rural patients who were hospitalized at urban hospitals (n = 429,052; 8.3%) and urban patients hospitalized in rural hospitals (n = 44, 053; 0.9%) were also excluded from this analysis as they may denote nonrepresentative patient populations being discharged from these hospitals. 

Postdischarge care is defined in the NIS as a discharge that is either “routine” (no PAC), involves HHC, or is categorized as “other transfers, including SNF, intermediate care, and another type of facility.” This designation is used to connote discharge to an SNF or swing bed, and specifically excludes transfers to hospitals, LTACHs, and inpatient rehabilitation facilities, which are captured elsewhere. The NIS does not distinguish between community SNF beds and swing beds. We excluded transfers to other short-term hospitals, and patients who left against medical advice or were discharged to an unknown location. The NIS captures only the most proximate source of admission (transfer from another hospital or the emergency department); thus, we were unable to accurately capture prior living situation, such as long-term care facilities. 

Discharge records were abstracted for patient demographic information (eg, age, sex, race/ethnicity, marital status, median income of residents in their home residence zip code), comorbidities, hospitalization information (eg, urgency, length of stay, source of admission), primary payer (eg, Medicare [including Medicare Advantage], Medicaid, private payers), hospital information (eg, ownership, size, geographic location according to 9 boundaries defined by the NIS), discharge disposition, and primary discharge diagnosis (from 

International Classification of Diseases, Ninth Revision, Clinical Modification

, grouped into Agency for Healthcare Research and Quality Clinical Classification Software [CCS] categories).

 The CCS category “osteoarthritis” in our sample included more than 94% elective admissions, and more than 93% had a Current Procedural Terminology code for a hip or knee replacement; thus, “elective joint arthroplasty” was used as a more appropriate label for this diagnostic category. 

We first evaluated the differences between rural patients discharged from rural hospitals and urban patients discharged from urban hospitals to PAC as we hypothesized these may represent distinct populations of patients, and compared both patient and hospital characteristics. We used HCUP-provided survey weights to produce nationally representative estimates in all analyses; weights can be understood as the number of discharges that a sample discharge represents. These weights account for clustering at the hospital level. Given the very large sample size, we did not use statistical testing as most comparisons would be statistically significant, but rather, we reported absolute differences in the 2 groups.

We then identified the 10 most common hospital discharge diagnoses that preceded PAC use in both urban and rural hospitals and the associated hospital length of stay preceding a PAC stay in order to establish whether possible differences in use of PAC may be related to differences in hospital length of stay. Next, we calculated rates of discharge to home, HHC, and SNF overall and within diagnostic categories, using the 10 diagnoses. Finally, we analyzed the relationship between use of PAC and urban/rural location within diagnostic categories, adjusting for patient and hospital characteristics in multivariable logistic regression. We evaluated SNF use and HHC use individually, then combined them into an overall PAC use variable to evaluate whether the overall use of PAC was similar despite differences in use of SNFs and HHC in different locations. In these comparisons, discharges home are the reference category. Hospital size was collinear with geographic location (rural regions generally had smaller hospitals) and was therefore not included in the regression. 

Our total sample comprised 1.56 million discharges of urban patients discharged from urban hospitals and rural patients discharged from rural hospitals that, when weighted, represented 7.8 million discharges nationally. Of this 1.56 million, 188,137 (12.1%) were rural patients discharged from rural hospitals to PAC (either HHC or SNF care). Rural patients discharged to PAC were more commonly white, poor, and hospitalized in a government facility. There were small differences between the groups in other characteristics (see 

Diagnoses Treated in Urban/Rural Patients Prior to PAC

Rural patients discharged to PAC were more frequently hospitalized for pneumonia, congestive heart failure, urinary tract infections, and chronic obstructive pulmonary disease, whereas urban patients more frequently underwent elective joint arthroplasty. Urban patients had longer hospital lengths of stay for most diagnoses (

Factors Influencing Use of PAC in Urban and Rural Hospitals

When all the factors from Table 1 (patient demographic characteristics, health factors, hospital ownership, and regional location) were incorporated into a multivariable model, rural patients at rural hospitals were slightly less likely to receive PAC overall (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.91-0.99). The index hospital length of stay, patient age, sex, and race/ethnicity, as well as whether the admission was elective, had the strongest associations with use of PAC (

). However, there were significant differences within diagnostic categories (

). Rural patients at rural hospitals received more overall PAC services for acute cerebrovascular accident (OR, 1.11; 95% CI, 1.03-1.19), and less frequently receive any PAC for sepsis (OR, 0.92; 95% CI, 0.86-0.98), hip fracture (OR, 0.82; 95% CI, 0.70-0.96), and particularly for elective joint arthroplasty (OR, 0.59; 95% CI, 0.49-0.71). 

Rural patients at rural hospitals received significantly more SNF care and significantly less HHC overall in 6 of the top 10 most commonly treated diagnoses (

). The main differences in this pattern were for elective joint arthroplasty and acute hip fracture, where rural patients received less SNF care and less HHC than urban patients. The other 4 diagnoses (acute cerebrovascular accident, sepsis, hip fracture, and elective joint arthroplasty), demonstrated overall similar use of PAC between urban and rural sites. 

Geographic variation in the use of postacute care explains the majority of geographic variation in Medicare spending, yet the reasons for this variability are underexplored. In this analysis, we found that rural patients discharged from rural hospitals were overall more likely to receive SNF or swing bed care after hospital discharge, and less likely to receive HHC, but will receive similar amounts of PAC overall when compared with urban patients. However, significant differences were revealed within diagnostic categories. The most striking difference occurred in elective joint arthroplasty, after which rural patients were 41% less likely to receive PAC of any kind.

This finding complements those of prior reports.

 Others have found reduced use of PAC for Medicare patients undergoing elective joint arthroplasty in critical access hospitals—which are a smaller subset of rural hospitals included in this analysis.

 The supply of skilled nursing beds per capita, however, did not strongly influence whether SNF care was used for these patients.

 The size of the rural skilled therapist workforce and staffing of SNFs may play a role; others have found that patients undergoing joint arthroplasty are less likely to receive PAC in counties with low availability of rehabilitation therapists.

 It is unlikely the difference in urban and rural use is related to length of stay. We found the length of stay was very similar for elective joint arthroplasty (mean 3 days) in both groups, and the Medicare short-stay transfer policy penalizes hospitals—particularly rural hospitals using swing beds&mdash;from converting patients from inpatient stays to PAC stays too quickly.

Our work also extends these reports by systematically evaluating all urban and rural patients and hospitals sampled in a nationally representative dataset of all payers consisting of more than 1.5 million discharges. In doing so, it provides new insights into geographic variability in the use of PAC, and suggests further evaluation of the use of PAC in elective joint replacements, hip fracture, sepsis, and after acute cerebrovascular accident may be most fruitful for identifying the reasons for these differences. 

These differences are important to understand from a cost perspective. For example, there are nearly 1 million hospitalizations for elective joint arthroplasty annually in the United States.

 The mean cost of the hospital stay and 30-day postacute episode for these procedures is $18,225, for a total of $15.7 billion annually. The increased use of SNF care instead of HHC in rural areas may be leading to a significant increase in costs, given that the average cost of an SNF stay in the United States is $11,357 compared with the average cost of an HHC episode of $2720.

 Given this difference, evaluating PAC outcomes in these populations is a critical next step. It is especially important now, as Medicare’s expansion of Bundled Payment for Care Improvement, which encompasses the acute hospitalization and 90 days of postdischarge care (including PAC), expanded to 67 metropolitan areas in April 2016 using elective lower extremity joint replacement as the first targeted condition.

This type of focused investigation may also lend insight into whether the use of PAC after elective joint arthroplasty in these patients constitutes a disparity that affects important patient outcomes, as well as costs.

 Systematic evaluation of outcomes in these patients has the potential to strongly inform development of guidelines for appropriate use of PAC.

 For example, preliminary evidence suggests patient outcomes after stroke or hip replacement—2 of the conditions where PAC use was most different between rural and urban areas in our analysis&mdash;are significantly affected by what type of PAC they receive.

 Outcomes may be poorer in patients treated at critical access hospitals who use less HHC and more SNF care,

 but the quality of PAC may be an important variable to consider in such analyses.

There is little prior literature suggesting an underlying etiology for this marked difference in PAC use between patient discharges from urban and rural hospitals. Although there are several possible etiologies for this difference, including differences in premorbid physical function—an important variable we were unable to evaluate in this analysis&mdash;we speculated this difference may be due to decreased access to or availability of HHC in rural areas. In other words, there may be a significant proportion of patients who would qualify for and benefit from HHC, but no availability of agencies to care for this population. It may also reflect the significant lack of consensus on the use of PAC, particularly in borderline cases where patients might be expected to regain function with enough time and therapy either inside or outside the hospital.

These findings should be interpreted in the context of the data from which they were derived. The study also had several notable strengths: systematic evaluation of a nationally representative sample of all payers and hospitals in the United States, including more than 4.6 million discharges, ascertainment of important patient and hospital characteristics that plausibly affect PAC use, and clear identification of both patient residence location and hospital location to ascertain true urban and rural differences. We were unable to ascertain some factors that may influence PAC use, such as preprocedure physical function or involvement of a caregiver in postdischarge care. To our knowledge, neither has been systematically assessed or compared between urban and rural patients before elective surgical procedures—a potentially important gap in the literature for informing policy in rural areas. Discharge diagnoses are potentially subject to bias and coding error. We were unable to evaluate the appropriateness of discharges to PAC as this dataset does not contain postdischarge outcomes. We were also unable to formally undergo a market-level analysis, as geographic area is coded only at the regional and urban/rural levels in this dataset.

In the setting of increasing PAC use nationally, informing optimal practices for this population in urban and rural areas across the United States is essential for delivering the highest-value care in this growing population.  

Research and Hospital Medicine Sections, Denver VA Medical Center (REB), Denver, CO; Veterans Affairs Geriatric Research, Education, and Clinical Center (JES), Denver, CO; Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado (JRF, JES), Aurora, CO; Division of General Internal Medicine, Department of Medicine (CDJ), and Division of Health Care Policy and Research (EAC), and Department of Emergency Medicine (AAG), University of Colorado School of Medicine, Aurora, CO.

Dr Burke was supported by a VA HSR&D Career Development Award. Mr Falvey was supported by a Promotion of Doctoral Studies I Award from the Foundation for Physical Therapy; a research grant from the American Physical Therapy Association, Health Policy and Administration section; and from a National Institute on Aging training grant T32AG000279.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Concept and design (REB, AAG); acquisition of data (REB, AAG, CDJ, JRF); analysis and interpretation of data (REB, JRF, JES, CDJ, EAC, AAG); drafting of the manuscript (REB); critical revision of the manuscript for important intellectual content (REB, JRF, JES, CDJ, EAC, AAG); statistical analysis (AAG); and supervision (AAG, EAC, JES). 

Robert E. Burke, MD, MS, Denver VA Medical Center, 1055 Clermont St, Denver, CO 80220. E-mail: Robert.Burke5@va.gov. 

1. Burke RE, Juarez-Colunga E, Levy C, Prochazka AV, Coleman EA, Ginde AA. Rise of post-acute care facilities as a discharge destination of US hospitalizations. 

. 2015;175(2):295-296. doi:10.1001/jamainternmed.2014.6383.

2. Jones CD, Ginde AA, Burke RE, Wald HL, Masoudi FA, Boxer RS. Increasing home healthcare referrals upon discharge from U.S. hospitals: 2001-2012. 

Rural patients receive less postacute care after hospital discharge than urban patients, especially after elective joint replacement-a condition selected for bundled payments.

March 2017

Consult Coordination Affects Patient Experience

Telemedicine and its Role in Revolutionizing Healthcare Delivery

Use of Postacute Care After Discharge in Urban and Rural Hospitals

Beneficiary Engagement in Medicare's Alternative Payment Models: From Serendipity to Active Choice

Creating Successful Alternative Payment Models

Meeting the Measure: Improving ADHD Care in the Medical Home

Successful Implementation of APSO Notes Across a Major Health System