Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Reducing Risks of HIT Induced Health Care Disparities
Similar to Reducing Risks of HIT Induced Health Care Disparities (20)
Recently uploaded
Recently uploaded (20)
Reducing Risks of HIT Induced Health Care Disparities
- 1. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risks of HIT Induced Health Care Disparities Joel Abueg Joel.abueg@gmail.net
- 2. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • HIT is seen as enabler of health care transformation • ARRA/HITECH intended to spur adoption of HIT • Actual costs & benefits not well known – Providers – Underserved populations • Policy may exacerbate/induce disparities Introduction
- 3. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Medically Underserved Populations – Economic, cultural, linguistic barriers to care • Medically Underserved areas • Health Professional Shortage Areas • More than the uninsured (44.8M): 160M • Likely to have poorer health care, health Who are the underserved?
- 4. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Small practices • 25% of physicians in solo practice • 60% of physicians in groups of 9 or less – 17% of visits by low-income/uninsured • Community Health Centers – 56% of visits by low-income/uninsured • Outpatient departments of hospitals – 40% of visits by low-income/uninsured Who cares for underserved?
- 5. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities Who cares for underserved?
- 6. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities Office-based physicians: EMR adoption
- 7. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities EMR adoption lags for smaller practices
- 8. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Benefits accrue to payers, expense to provider • Reduced transcription, higher coding • Costly , >25% more than expected – KP HC: $285K/provider – Initial $44-54K/p , Install $22K /p, $7,500/p • Computing ROI complex, sensitive to many factors Benefits and costs of HIT
- 9. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Lack of skilled personnel • Higher per provider costs • Questions about incentives & support • Limited choices for solutions • Lack of applicable data to inform projects Small practices & CHCs: Issues
- 10. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Primary care as locus of system change • Clinical decision support to reduce disparities in care • New modalities of care – Email, PHRs, Chronic disease management – Telemedicine Opportunities for HIT
- 11. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • Meaningful use definitions & timeline – Revise for small & rural practices – Provide roadmap, extend timeline • Infrastructure investment & Support • Broaden the scope of HIT use – Decision support – Coordination of care • Reform payment & delivery Reducing risks of disparities
- 12. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Reducing Risk of HIT Induced Health Care Disparities • HIT must occur with a culture of change • Current HIT is inadequate • Current policy may be counterproductive • Policies need to – Accommodate actual conditions – Be better aligned with desired outcomes • Need for more research Concluding summary
- 13. UNIVERSITY INFO731-900-201015 ORG & SOC ISSUE HLTHCARE INFO NOVEMBER 16, 2010 Selected References Bernstein, W. S., Pfister, H., R., & Ingargiola, S. R. (2010, June 2010). HITECH Revisited. Manett Health Solutions report funded by the California Health Foundation, Colorado Health Foundation, and the United Hospital Fund. Retrieved online Chen, C., Garrido, T., Chock, D., Okawa, G., & Liang, L. (2009). The Kaiser Permanente electronic health record: Transforming and streamlining modalities of care. Health Affairs , 28 (2), 323-333. Hasselman, D. (2010a, September). Leveraging Medicaid to encourage HIT adoption and strengthen primary care (Presentation given that the 2010 Medicaid Managed Care Congress. Retrieved online Heyman, J. (2010, March). Health IT and solo practice,: A love-hate relationship. Journal of Law, Medicine, & Ethics, 38, 14-16. Jha, A. K., DesRoches, C. M., Shields, A. E., Miralles, P. D., Zheng, J., Rosenbaum, S., & Campbell, E. G. (2009). Evidence of an emerging digital divide among hospitals that care for the poor. Health Affairs, 28(6), W1160-w1170. Mechanic, D. (2008). Rethinking medical professionalism,: The role of information technology and practice innovations. The Milbank Quarterly, 86(2), 327-358.
Editor's Notes
- A widely held assumption is that health information technology (HIT) has the potential to transform healthcare (e.g., Institute of Medicine, 2001). HIT may reduce errors, costs, and improved quality through support of clinical work, better coordination of care, and more efficient communications between providers and patients. However, the vision of a highly interconnected health system(e.g., Liang, 2010), where relevant information is at the fingertips of all those need it, is one that will likely be out of the grasp for all but the largest health providers (e.g., Jha, et al., 2009; Jha et al., 2010). A significant barrier is initial cost. To address this, the Health Information Technology for Economic and Clinical Health (HITECH) Act included in the American Recovery and Reinvestment Act (ARRA) of 2009 allocated $19.2 billion dollars to spur adoption of Electronic Health Record (EHR) systems by hospitals and doctors. Medicare and Medicaid providers that make meaningful use of HIT by 2014 receive incentives. Those how do not, will be assessed penalties (which grow). This raises a number of important issues for those concerned with the care of vulnerable populations: the uninsured or underinsured, the poor, and/or historically underrepresented, or underserved populations. At least until significantly more resources for support of HIT and infrastructure in which to exchange information is made available for providers and their patients, it will be necessary to lower expectations or make appropriate accommodations. Without better policy and the understanding of the true costs and benefits of HIT, efforts at reducing the digital divide, between ”the haves” and “have-nots” has the potential to actually exacerbate known disparities in access and quality of care to significant underserved, vulnerable populations. HIT solutions must not divert scarce resources in pursuit of still illusory gains in productivity and quality. Moreover, scenarios in which positive changes in quality can be attributed to HIT may not generalize well to vulnerable populations. Finally, we simply need to learn more about HIT implementation in these settings. Remarkably little research has been done on the effect of HIT in solo or small practices, community health centers, and outpatient departments of hospitals.
- Primary care is a significant contributor to community health; improving access to--and quality of--primary care is an effective way of reducing health care system costs and improving quality (Starfield, Shi, & Macinko, 2005). Among other things, the good primary care is linked with better chronic disease management (e.g., diabetes) and reduction in unnecessary, costly hospital care. There are two ways in which people can lack access to quality primary care, either because of where they live, or who they are. According to the U.S. Department of Health and Human Services, Health Resources and Services Administration (citation here): Medically Underserved Populations (MUPs) may include groups of persons who face economic, cultural or linguistic barriers to health care. Medically Underserved Areas (MUA) may be a whole county or a group of contiguous counties, a group of county or civil divisions or a group of urban census tracts in which residents have a shortage of personal health services. Additionally, designated Health Professional Shortage Areas (HSPA) identify locations where a shortage primary medical care, dental, or mental health professionals. This goes beyond whether or not a person is insured. For example, in 2007, more than 96 million lived in an MUA, and nearly 64.5 million lived in an HSPA (Rosenbaum et al, 2005). While health reform is expected to cover the 28% of the total number that was uninsured, there will still be at least 160 million people underserved. Though there are certainly MUPs and MUAs who reside in urban areas, living in rural area exemplify the problems of being underserved. Though one quarter of the U.S. population live in rural areas, only 10% of physicians work there. Rural people tend to be poor, and less likely to have any type of insurance. Cerebrovascular disease and hypertension is higher in rural than urban areas. Those suffering from acute myochardial infarction were less likely to receive standard treatments in rural hospital compared to urban ones. Being underserved predicts disparities in healthcare, which leads to disparities in community health.
- Underserved populations find primary care in three types of settings: in small practices, community health centers (CHCs), and outpatient departments (OPDs) of hospitals (e.g., small rural hospitals). According to data from the 2008 National Ambulatory Care Survey (Hing & Udin, 2010), the majority of visits to primary care delivery sites were in physician offices (84%), followed by OPDs (11%) and CHCs (5%)(see Figure 1 from Hing & Udin). However, federally funded CHCs are designed to address needs of the underserved. Consequently, uninsured or low-income patients received Medicaid or State Children’s Health Insurance Plan support visited CHCs (56%) and OPDs (40%) much more than physician offices (17%). In any case, large numbers of underserved people receive care in solo or small practices, CHCs, and small rural hospitals. According to the 2007-2008 Physician Practice Survey (Kane, 2009), more 75% of all working physicians are office-based. Most are in small practices. Nearly 25% (or 33% of office-based) are solo practitioners. Nearly 60% of all working physicians are in practices of 9 or less; 70.9% are in practices or 50 or less. As would be expected, smaller practices (and smaller hospitals) tend to be “underresourced” settings that face constraints on HIT adoption. As is seen in the figures below (from Hsiao et al, 2009), the percentage of office-based physicians with HIT has been steadily growing. But as practice size decreases, so does the likelihood that they will be using HIT. Startup costs are a significant factor. Even though larger practices may chose to finance the expense, they are more likely that their small peers to retained earnings to draw from for startup costs associated with software and equipment purchases, or importantly, to compensate for losses in productivity while the system is being implemented. The latter is certainly not something software vendors will finance. Because providers that care for the underserved are usually not as well compensated as others, the resource issues such as access to capital (Jha, et al 2010), return on investment (ROI) and expected payback period become paramount.
- There are many published accounts of problems of paper, new found efficiency with HIT (e.g., Benjamin, 2010; Heyman, 2010) and projects that are intended to illustrate how HIT can transform care (e.g., see “Beacon Communities,” Maxson et al, 2010). These reinforce the notion that the investment in moving away from paper records is worthwhile. However, at best, comprehensive reviews of the current literature on the benefits of HIT (especially in terms of efficient and quality) are mixed (Desroches, 2010; Liebovitz, 2009). Moreover, HIT implementations are subject to same risks of failure and problems found in any other IT project (Avison & Young, 2007; Heeks, Mundy, Salazar, 1999). Information technology alone rarely causes social or organization transformation; on the other hand, when implemented they have political implications, creating winners and losers (Kling, Rosenbaum, Sawyer, 2005). It is important to understand where the benefits accrue and ensure that they are realized where they are intended to be. Currently, systems appear to have been designed with perspective that clinical computing is simply another form of administrative computing (Karsh et al. 2010); even when it does not directly serve billing purposes, it seems built to facilitate it and serve administrative needs, rather support cognitive work and coordination (Stead & Lin, 2009). This is evident in published accounts of the ability of EHR systems to save money in ambulatory care by reduced costs of transcription and increased revenue from better coding. Indeed, one of the barriers to adoption is the perception (and reality) that HIT benefits payers at the expense of providers (Fiscella & Geiger, 2006). From the provider perspective, it is actually difficult to make a good business case based on financial return on investment. Anecdotal accounts and projected savings exist, but methodologically sound findings of cost savings are not evident in hospitals (DesRoches, et al. 2010) or among physician practice groups (see Gans, et al 2005). Costs of implementing a comprehensive EHR system are not inconsequential. For example, the development of Kaiser Permanente’s HealthConnect and deployment into 454 medical offices and 36 hospitals, cost $4B (or 10% of annual operating revenue, and excluding cancelation of a $440M KP-internal project). The system is comprehensive: 34 of its 36 hospitals achieved stage 7 of the HIMSS EMR Adoption model (HIMSS Analytics, 2010), while the remaining two were stage 6 (as of October 2010, only 50 hospitals have be certified Stage 7 in the U.S.). Kaiser represents one of a few exemplars of the promise of contained cost growth, efficiency (Kaiser estimates approximately $500M in annual savings, or 1.5% of operating revenue), and quality improvement attributed to HIT-enabled organizational change (e.g., Liang, 2010; Chen et al., 2009). Comparing KP HealthConnect per physician cost of roughly $285,000 to the $44,000 incentive per eligible provider under ARRA suggests that physicians will bear a significant portion of costs associated with the desired changes. Indeed, in Miller et al. (2005)’s case study of 14 solo or small group practices, $44,000 represented only the initial costs per FTE provider. The range of initial costs for 12 of the providers was from $37,000 to $63,000 per FTE provider. This excluded hardware that averaged $13,000 per provider, and software training and installation costs that averaged $22,000 per FTE provider. Revenue losses due to reduced capacity during training and implemented was about $7,500. Losses depended on whether or not providers simply worked longer hours rather than reduce visits. Finally, there were ongoing maintenance and training related costs, which averaged $8400 per FTE provider per year. Related to costs are risks associated with EHR implementation and the costs to mitigate them. Recently, Heyman (2010) described his experience as a solo practice early adopter of HIT. Despite having lost all his data twice, he was fortunate to have purchased office insurance that covered restoration costs. But, time lost to the six week process was never recovered. In Miller’s et al’s (2005) study, 3 of the 14 practices underwent serious financial risks. One had to redo its billing for the first six weeks of implementation, then suffered a system crash that resulted in a total loss of data and several weeks without electronic or paper charts. The other two practices also suffered billing problems post implementation. One endured for 3 months without billing or revenue, the other went for 10 months and nearly went bankrupt. Fortunately, the picture was not uniformly bleak. Miller et al. (2005) found that there was an average $33,000 in cost savings per FTE provider per year. These came primarily from two sources: increased coding levels and decreased personnel costs (e.g., reduced or eliminated need for transcription) or modest gains from increased visits. None of the practices studied received substantial pay-for-performance for improved quality from health plans, though two practices received a nominal reward. One practice obtained a $300 per provider discount on malpractice insurance. This was in the range found predicted by a model presented by Wang et al. (2003), which estimated a net benefit of $86,400 per provider for 5-year period. Under their model, that benefit ranged from a $2300 cost to $330,900 per provider benefit for a 5 year period, depending on assumptions. Estimating ROI prior to initiating an HIT project is not trivial. There are many factors that interact in complex ways, and that can turn an estimated net benefit into a net cost. Toward that end, academics and industry have provided some tools (see Appendix A). At best, this is merely a starting point and may underestimate costs by 25% (Gans, et al., 2005). Providers need to conduct significant front-end work to consider to a proper needs analyses, examine and formulate requirements, and evaluate vendor proposals. The costs and risks associated with the open-ended “discovery” period of a HIT project are often not figured into the total costs of an HIT project , or they are barriers (Fiscella &Geiger, 2006).
- Although physicians may have solid business knowledge, and be more technically savvy than in the earlier years of electronic medical records, their skill and knowledge may still be insufficient for fully understanding the impact of vendor and design choices they have to make. The problem is more acute in smaller, more isolated practices, which have fewer staff that can share in the work of evaluating and implementing HIT solutions. Small and/or rural practices and CHCs that care for the underserved may need to go it alone, and rely more heavily on HIT vendors than larger practices or hospitals with available IT staff or contracted consultants. In 2006, changes to federal anti-kickback gave hospitals safe harbor to provider EHR and support to contracted doctors (Medicare and State Health Care Programs, 2006). The policy change was intended to leverage staff and economies of scale enjoyed by larger hospitals into physician practices, and was consistent with other provisions that gave safe harbor to entities providing care in medically underserved areas. The potential effect of the policy is muted in rural settings where hospitals are typically already underresourced. Additionally, smaller practices (Gans, et al. 2005) and CHCs appear to have higher per provider costs than large practices. In a subsequent study of six CHCs, Miller and West. (2007) found that initial EHR cost per FTE billing provider was nearly $54,000, $10,000 more than in the earlier study of solo or small group practices. Ongoing costs per FTE billing provider averaged $20,000 per year. This is probably a consequence of the complexity of CHCs, which serve as a “one-stop shop” of health care and health related services , for a population that is more challenging to care for than elsewhere. At the same time, flat-rate or lump sum reimbursement limited CHCs ability to see gains due to billing effectiveness (i.e., coding) that physicians in solo or small practices may have enjoyed. As a result, CHCs in the Miller and West study all operated with net losses due to EHR use. Presently, there is also confusion in the field about the applicability of incentives and support to Rural Health Clinics, which are either in a HSPA or MUA. For example, in the ARRA/HITECH, though it is clear that provider serving at least 30% “needy” patients will qualify for Medicaid incentives, providers that practice in these clinics may not be eligible for Medicare incentives (Wenzlow, 2010a). And, supplemental funding for support may not reach most rural hospitals (Wenzlow, 2010b). Physicians in MUAs and HSPAs may face shortages of qualified people to assist with projects, and even fewer choices for the HIT systems themselves. Because of limited resources, they have not usually been the target of vendors with a long history of successful HIT implementations. This also puts physicians at greater risk of failure in their HIT projects, or even in their businesses (recall the 3 of 14 practices in the Miller et al 2005 study that could not bill for 6 weeks to 10 months post implementation). Importantly, it is likely the case that we simply don’t yet have good evidence about what makes a good solution for smaller practices, and how it should be implemented. Thus far, HIT vendors, and research has concentrated mostly on larger practices and hospitals. This is understandable given that they have the most resources to steer into HIT projects. The rates of HIT adoption by practice size and or by rural vs. urban hospitals bear this out. It is widely accepted that quality improvements and efficiency associated with HIT come from workflow redesign. Yet, relatively little research directly applicable to small and medium-sized practices exists. Recently, Carayon et. al. (2010) reported findings of a comprehensive literature search and environmental scan to address this issue. The project’s goal was to develop a toolkit that small and medium-size practices, or those that they consult, might use in considering how best to incorporate HIT in workflow redesign. Project members read 4470 potential articles, and identified 192 that were relevant. While what they found is important in understanding what can be done now, what they didn’t find is perhaps even more important. They noted that the “quality of findings is lacking for many reasons” (p. 89). Most studies lacked scientifically rigorous design and/or lacks enough description of the socio technical context in order to properly infer the contribution of different, potentially confounding variables suchs as training, technical support, and organizational culture. Moreover, the majority of studies review were conducted in large clinics associated with large academic medical centers, health maintenance organizations, or in non-U.S. health systems. It is ironic that we know little about using HIT where a considerable amount of care occurs. On the other hand, there is some research (Ketchem et al., 2009). that indicates that HIT can alter disparities in health care even if quality does not improve overall. Ketchem et al. found that (p.675) “IT as complex implications for differences across patient types, and the implications vary across IT function. In some cases, IT eliminates these differences; in others, it leaves them unchanged, and in yet others IT appears to create differences where they do not exist.” Ketchem et al. state that “given the range of effects of IT on disparities, potential policies aimed at increasing IT adoption and those designed to decrease disparities may be at odds with each other” (p. 677). Their work further demonstrates that more research needs to be done, and that policy may have unintended consequences. HIT will also need to accommodate a growing set of users which include the patients themselves. EHRs that feed into personal health records (PHR) systems will need to be designed for accessibility by people with a wide range of literacy skills and education (e.g., Fluckinger, 2010). We will also need to look at cultural differences in interacting with health system processes and provider-patient interactions that carry over in the HIT use. For example, Roblin, Houston, Allison, Joski, and Becker (2009) conducted a study of PHR registration. Education was related to likelihood of registration, but age was not. However, among African Americans 30.1% registered compared to 41.7% of whites. Differences in education, income, or internet access did not account for the disparity. The authors noted that “e-health researchers should further investigate cultural issues related to trust, privacy, economic status, and literacy that might sustain the PHR adoption gap” (p. 688). Like other effects of HIT, the span of the digital divide may reach further than is known. Nonetheless, currently policy reflects a leap of faith that what we know about HIT success and failure generalizes to small practices and those that care for the underserved. But given the costs and risks of HIT implementation ( even when defrayed through incentives or grants), it is important to ask whether or not the trajectory guided by policy can lead us past the kinds of marginal improvements observed in larger settings. If it cannot, then investment and continued support of HIT, as it exists now, will not to be reconciled with the reallocation of resources away from direct care. The precedent for this certainly exists, as layoffs of health care workers have been attributed to balancing budgets that include large capital expenses due to HIT (citation here). That cannot be an option in the case of underserved. Finally, from the health system perspective, HIT implementation is also a question of health equity. As Miller and West stated (p. 212), “EHRs in private medical groups might make sense financially and yet might have a limited effect on increasing social good, whereas EHRs in CHC might not make sense financially (to CHCs) and yet might do more social good.”
- Some have argued that primary care is the proper target for effective health system transformation efforts (Hasselman, 2010ab). Resource issues aside, many opportunities for using HIT in primary care transformation exist, if we looked beyond HIT as implementation of EHRs. Though EHRs represent a critical and essential piece of a redesigned system, deploying EHRs is not sufficient for improvement in care (DesRoches et al., 2010). EHRs need to foster better clinical decision making to mitigate against disparities in rural vs urban, and small vs. large practice healthcare (see Appendix B). Just as critical is the requirement for interoperability (or even substitutability of clinical modules), an investment in an infrastructure to support the exchange of health data (e.g., broadband access for rural areas, Health Information Exchanges), PHRs, and even telemedicine (e,.g., see Leach, 2009), which directly addresses the access issue. Dramatic shifts in utilization (total office visit decrease 26.2%, specialty care decreased 25.3%, whereas scheduled telephone visits increased 8x, secure email increased nearly 6x), sustained quality, can be enabled by a comprehensive EHR/PHR system (Chen, et al.,, 2009). The PHR becomes a nexus for collaboration (cf., Tang & Lansky, 2005) among health care stakeholders gives rise to nontraditional, patient-centered ways of providing care. Today, care providers not include physicians and other health professional, but also family members and the patients themselves. Aligned incentives play a critical role: “our results were obtained in an integrated delivery system with an economic model that aligns financial incentives with providing effective and efficient care, regardless of how that care is delivered” (p. 331). HIT applications that aid in disease management or provide educational resources may displace visits and reimbursable work (Mechanic, 2008). Under current policies, that recognize and reimbursed for only certain kinds of health transactions (e.g,. medical procedures) as opposed to cognitive work (e.g., education, consultation), transformation made possible by HIT would be unlikely to occur. An important antecedent to HIT success is organizational readiness for change (Lozeni & Riley, 2005). For HIT to have a positive impact, organization change must be possible, and actually occur in a context of a culture of quality improvement (Liang, 2010; Millery & Kukafka, 2010). Policies need to be carefully formulated and provide the “right” set of incentives. Just as HIT implementation can have unintended consequences (e.g., errors, increased risk to patient safety), policies that attempt to pay for performance can actually reduce access and quality (Werner, Goldman, & Dudley, 2008).
- The risk of HIT induced healthcare disparities is a real one. Even when subsidized, investment in HIT comes at some expense to access for underserved populations. Whether that is large or small, persistent or temporary, and yields real benefits depends on policy and implementation decisions. The ARRA/HITECH definition of meaningful use of HIT and goals set forth demonstrates this. Recently, Bernstein, Pfister, and Inagolia (2010) completed a report on ARRA/HITECH, funded by two state health foundations and the United Hospital Fund. That report echoed voices throughout the provider community (e.g., Halamka, 2009) in its call to: Revise meaningful use criteria to accommodate current ability of small and rural practices and some CHCs to achieve goals; Reduce uncertainty in future definitions of what meaningful use that are a barrier to HIT adoption, by publishing a roadmap the goes beyond initial (Stage 1) requirements; Ensure that meaningful use includes participation in health information exchanges (HIE_ developed under the State HIE Program; Encourage clinical decision support at the point of care; Let states determine meaningful use for providers serving Medicaid patient, particularly with respect to interoperability and quality improvement goals; and, Expand the scope of eligible providers in order to enable truly coordinated care; Monitor and evaluate Regional Extension Centers, or otherwise ensure that there are sufficient support services available; Broad-scale payment and delivery reform to complement HIT investment. While some authors (e.g., Desroches, et al., 2010) see the rule making approach as “dynamic and rigorous” “with expectations rising over time as providers become accustomed to the technology,” others are less sanguine. The recommendations reflect a warranted uneasiness that the present timeline and goals for universal HIT adoption are unrealistic, especially for key segments of the healthcare systems.
- For HIT to positively transform healthcare, it must be adopted in a culture of change. Yet, current technology seems to reinforce existing ways of providing care, especially from the perspective of cost reimbursement. On the other hand, systems and resources for delivering care very considerably. Present rules appear to be at odds with the current capabilities of many providers, especially those that care for the underserved. Among these is lack of adequate support resource for all stages of HIT adoption (selection, design/implementation, ongoing post implementation support and evaluation). A labyrinth of policies address the issue of improving access and quality of care to the underserved. Generally, policies need to be better aligned. Specifically, accommodations for those caring for the underserved need to be made. Finally, without more good research directly applicable to HIT implementation in small practices and CHCs and on the potential effects of HIT on healthcare disparities, well intentioned efforts may have unintended, undesirable consequences that increase disparities.
- Avison, D., and T. Young. (2007). Time to Rethink Healthcare and ICT? Communications of the ACM, 50(6):69–74. Benjamin, R. (2010, July 13). Finding my way to electronic health records. New England Journal of Medicine, 10.1056/NEJMp1007785 Bernstein, W. S., Pfister, H., R., & Ingargiola, S. R. (2010, June 2010). HITECH Revisited. Manett Health Solutions report funded by the California Health Foundation, Colorado Health Foundation, and the United Hospital Fund. Retriwved online at: http://www.manatt.com/uploadedFiles/News_and_Events/Newsletters/HealthLaw@Manatt/HITECH%20Revisited_Final%20May%2031,%202010.pdf Burt, C. W., Hing, E., & Woodell, D. (2005). Electronic medical record use by office-based physicians: United States, 2005. Report. Hyattsville,m MD: National Center for Health Statistics. Retrieved online: http://www.cdc.gov/nchs/data/hestat/electronic/electronic.htm#5 Carayon, P., Karsh, B-T., Cartmill, R. S. et al., University of Wisconsin, Madison, The Center for Quality and Productivity Improvement. (2010). Incorporating health information technology into workflow redesign—Summary Report (AHRQ Publication No. 10-0098-EF). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from http://healthit.ahrq.gov/portal/server.pt/gateway/PTARGS_0_3882_948747_0_0_18/Workflow+Summary+Report.pdf Chen, C., Garrido, T., Chock, D., Okawa, G., & Liang, L. (2009). The Kaiser Permanente electronic health record: Transforming and streamlining modalities of care. Health Affairs , 28 (2), 323-333. Desroches, C. M., Campbell, E. G., Vogeli, C., Zheng, J., Rao, S. R., Sheilds, A. E., Donelan, K., Rosenbaum, S., Brisol, S., J., & Jha, A. K. (2010, April). Electronic health records limited successes suggest more targeted uses. Health Affairs, 29(4), 639-646. Fiskella, K., & Gieger, H.J. (2006, March/April). Health information technology and quality improvement for community health centers. Health Affairs, 25(2), 405-412. Fluckinger, D. (2010, May 6). Surgeon general: EHR systems at rural health clinics are vulnerable. SearchHealthIT.com webpage. Retrieved online from http://searchhealthit.techtarget.com/news/2240018314/Surgeon-General-EHR-systems-at-rural-health-clinics-are-vulnerable Forrest, C. B., & Whelan, E-M. (2000). Primary care safety-net delivery sites in the United States: A comparison of community health centers, hospital outpatient departments, and physician offices. Journal of the American Medical Association, 284(16), 2077-2083 (doi:10.1001/jama.284.16.2077). Friedberg, M. W. Coltin, K. L., Safran, D. G., Dresser, M. & Schneider, E. C. (2010). Medical home capabilities of primary care practices that server sociodemographically vulnerable neighborhoods. Archives of Internal Medicine, 170(11), 938-944. Gans, D., Kralewski, J., Hammons, T., & Dowd, B. (2005). Medical groups’ adoption of electronic health record and information systems. Health Affairs, 24(5); 1323-33. Halamka, J. (2009). Making smart investments in health infomration technology: corre principles. Heath Afairs, 28(2), w385-w389, Hansen, D. (2008, May 19). EMR Deadline does not compute: Falling short of 2014 goals. Retrieved November 15, 2008, from American Medical News: http://www.ama-assn.org/amednews/2008/05/19/gvsa0519.htm Hasselman, D. (2010a, September). Leveraging Medicaid to encourage HIT adoption and strengthen primary care (Presentation given that the 2010 Medicand Managed Care Congress. Retrieved online at: http://www.chcs.org/usr_doc/HasselmanMCCongress.pdf Hasselman, D. (2010b, July). Practice transformation in Medicaid: Five levers ti strengthen small, “high-volume, high opportunity” practices. Technical assistance brief from Center for Health Care Strategies, Inc. Heeks, R., Mundy, D., & Salazar, A. (1999). Why Health Care Information Systems Succeed or Fail. Information Systems for Public Sector Management, Paper No. 9. Manchester, UK: Institute for Development Policy and Management. Heyman, J. (2010, March). Health IT and solo practice,: A love-hate relationship. Journal of Law, Medicine, & Ethics, 38, 14-16. HIMSS Anaytics (2010, October). Stage 7 Hospitals. Web page retrieved online from http://www.himssanalytics.org/hc_providers/stage7Hospitals.asp. Hing, E., & Udin, S. (2010, October). Visits to primary care delivery sites: United States, 2008. NCHS Data Brief Number 47. Hyattsville, MD: National Center of Health Statistics. Hsiao, C-J, Beatty, P. C., Hing, E. S., Woodwell, D. A., Rechtsteiner, E. A., & Sisk, J. E. (2009, December). Electronic Medical Record/Electronic Health Record use by office-based physicians: United States, 2008 and Preliminary 2009. http://www.cdc.gov/nchs/data/hestat/emr_ehr/emr_ehr.pdf Insititute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press. Jha, A. K., DesRoches, C. M., Shields, A. E., Miralles, P. D., Zheng, J., Rosenbaum, S., & Campbell, E. G. (2009). Evidence of an emerging digital divide among hospitals that care for the poor. Health Affairs, 28(6), W1160-w1170. Jha, A. K., DesRoches, Kralovec., & Joshi, M. S. (2010, October). A progress report on electronic health records in U.S. hospitals. Health Affairs, 29(10), 1951-1957. Kane, C. K. (2009). The practice arangements of patient care physicians, 2007-2008: An analysis by age cohort and gender. AMA Policy Research Perspectives report. Chicago, IL: American Medical Association. Retrieved online at: http://www.ama-assn.org/ama1/pub/upload/mm/363/prp-200906-phys-prac-arrange.pdf Karsh, B., Weinger, M. B., Abbot, P. A., & Wears, R. L. (2010). Health information technology: fallacies and sober realities. Journal of the American Medical Informatics Association, 17, 617-623. DOI:10/1136/jamia.2010.005637. Ketcham, J. D., Lutfey, K. E., Gerstenberger, E., Link, C. L., & McKinlay, J. B. (2009). Physician clinical information technology and health care disparities. Medical Care Research and Review, 658-681. Kling, R., Rosenbaum, H., & Sawyer, S. (2005). Understanding and communicating social informatics: A framework for studying and teaching the human contexts of information and communication technologies. Medford, NJ: Information Today. Leach, W. D. (2009, January). If you bill it, they will come: A literature review on clinical outcomes, cost-effectiveness, and reimbursement for telemedicine. Report prepared for the California Telemedicine and eHealth Center. Sacramento, CA. Liang, L. L. (Ed.) (2010). Connected for health: Using electronic health records to transform care delivery. San Francisco, CA: Jossey-Bass. Liebovitz, D. L. (2009). Health care information technology: A cloud around the silver lining? Archives of Internal Medicine , 169 (10), 924-926. Maxson, E., Jain, S. H., McKethan, A. N. Brammer, C., Buntin, M. B., Cronin, Mostashari, F., & Blumenthal (2010). Health affairs, 29(9), 1671-1677. Mechanic, D. (2008). Rethinking medical professionalism,: The role of information technology and practice innovations. The Milbank Quarterly, 86(2), 327-358. Medicare and State Health Care Programs: Fraud and Abuse; Safe Harbors for Certain Electronic Prescribing and Electronic Health Records Arrangements Under the Anti-Kickback Statute, 71 Fed. Reg. 45110 (2006) (to be codifiend at 42 CFR pt 1001). Miller, R. H., D’Amato, K., Oliva, N., West, C. E., & Adelson, J. W. (2009). California’s digital divide: Clinical information systems of the haves and have-nots. Health Affairs, 28(2), 505-516. Miller, R. H. & West, C. E. (2007). The value of electronic health records in commmunity health centers: Policy implications. Health Affairs, 68(1), 206-214. Miller, R. H., West, C., Brown, T. M., Sim, I., & Ganchoff, C. (2005, September/October). The value of electronic health records in solo or small group practices. Health Affairs, 24(5), 1127-1137. Millery, M., & Kukafka, R. (2010, October). Health information technology and quality of health care: Strategies for reducing disparities in underresourced settings. Medical Care Research and Review, 67(5), 268S-298S (doi:10.1177/1077558710373769). O'Connor, A. M.-T., & Flood, A. B. (2004). Modifying unwarranted variations in health care: Shared decision making using patient decision aids. Health Affairs , 63-72. Or, C. K., & Karsh, B.-T. (2009). A systematic review of patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association , 16 (4), 550-560. Roblin, D. W., Houston, T. K., Allison, J. J., Joski, P. J., & Becker, E. R. (2009). Disparities in use of a personal health record in a managed care organizations. Journal of the American Medical Informatics Association , 16 (5), 683-689. Rosenbaum, S., Jones, E., Shin, P., & Ku, L. (2009, July 9). National health reform: How will medically underserved communities fare? Geiger Gibson / RCHN Community Health Foundation Research Collaboratice Policy Research Brief No. 10. Washington, DC: The George Washington University School of Public Health and Health Services. Silvestre, A.-L., & Sue, V. M. (2009). If you build it, will they come? The Kaiser Permanente Model of online health care. Health Affairs , 28 (2), 334-344. Starfield, B., Shi, & Macinko, J. (2005). Contribution of primary care to health systems and health. The Milbank Quarterly, 83(3), 457-502. Stead, W. W., & Lin, H. S. (Eds.)(2009). Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. Tang, P. C., & Lansky, D. (2005). The missing link: bridging the patient-provider health information gap. Health Affairs , 24 (4), 1290-1295. Wang, S.J., Middleton, B., Prosser, L.A., Bardon, C. G., Spurr, C. D., Carchidi, P. J., Kittler, A. F., Goldzer, R. C., Fairchild, D. G., Sussman, A. J., Kuperman, G. J., & Bates, D. W. (2003). A cost-benefit analysis of electronic medical records in primary care. American Journal of Medicine, 114(5): 397-403. Wenzlow,L (2010a). Rural health clinic (RHC) physician eligibility for Medicare incentives discrepancy. Rural Health IT (Webpage). Retrieved from http://www.worh.org/hit/2010/09/rural-health-clinic-rhc-physician-eligibility-for-medicare-discrepancy/ Wenzlow,L (2010b). Supplemental “Rural Hospital” REC funding may not reach most rural hospitals. Rural Health IT (Webpage). Retrieved from http://www.worh.org/hit/2010/04/supplemental-%e2%80%9crural-hospital%e2%80%9d-rec-funding-may-not-reach-most-rural-hospitals/ Werner, R. M., Goldman, L. E., & Dudley, R. A (2008). Comparison of change in quality of care between safety-net and non-safety-net hospitals. Journal of the American Medical Association, 299(18): 20180 (doi:10.1001/jama.299.18.2180).