Health IT Survey Compendium
The Health IT Survey Compendium provides a centralized resource of publically available health IT surveys, many of which were developed by AHRQ-funded projects. Surveys may be used as is, serve as templates to create new surveys, or questions pulled out and used on their own.
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Description: Target Population:, Pediatric * , Persons with Disabilities *, Summary:, Developmental disabilities affect between 12 and 16 percent of the pediatric population in the United States. “Best practices” guidelines require that children receive appropriate and timely screening and treatment for these disabilities. Electronic computer decision support strategies (CDSS) offer a promising aid for implementing a standardized approach to developmental surveillance and screening. Researchers at Indiana University have developed an electronic CDSS for pediatric practices called CHICA (Child Health Improvement Through Computer Automation) to deliver appropriate guidelines to physicians during the patient visit. CHICA will be modified to incorporate developmental surveillance and screening within the existing practice workflow without requiring additional time of the physician or other office staff. The CHICA CDSS system includes elements such as: 1) pediatric guidelines encoded in Arden Syntax, a common computer language representing medical conditions and recommendations; 2) a dynamic, scan form interface for the user; and 3) a Health Level 7-compliant interface to existing medical record systems. The proposed work extends the CHICA software by incorporating the 2006 American Academy of Pediatrics (AAP) guidelines into the surveillance and screening algorithm, and evaluates the effect of the CHICA system on developmental surveillance, screening, referral, and early intervention and early childhood services. This evaluation follows a cohort of children with developmental disabilities to compare the proportion of children who undergo developmental screening at 9-, 18-, and 30-month visits at four practice sites, two of which have implemented the CDSS system and two of which have not. This evaluation will identify how implementation of the AAP recommendations into CHICA affects adherence to clinical guidelines. In addition, documentation of long-term outcomes will contribute to knowledge about the impact of early surveillance and screening on child health. Qualitative aspects of child screening surveillance will also be explored. These include elements of the child’s management plan, such as family involvement in treatment decisions and planning, treatment that is based on the initial assessment versus treatment that is continuously modified using data-driven decisionmaking, and whether management strategies build on the strengths of the child., Specific Aims:, Expand and modify an existing computer-based decision support system (CHICA) to include the 2006 AAP developmental surveillance and screening algorithm. (, Ongoing, ) Evaluate the effect of the CHICA system on the developmental surveillance and screening practices of four pediatric clinics. (, Ongoing, ) Evaluate the effect of the CHICA system on referrals for developmental and medical evaluations, and for early developmental intervention and early childhood services. (, Ongoing, ) Develop and follow a cohort of children with identified developmental disabilities to look at the end results and effects of developmental screening. (, Upcoming, ), 2010 Activities:, The intervention using the CHICA system to facilitate screening for developmental delay at 9-, 18-, and 30-month well-child visits was initiated in 2010. At the technical level, the team made the Ages and Stages questionnaire (ASQ) into a scan document that could be fed into CHICA and scored. The two intervention and two control practices began in July 2010. The grant team collected baseline information on the participating practices, partially through chart review. These practices began surveillance at acute care visits as well as well-child visits. This type of surveillance is a significant change in process for providers. They are used to screening at regular intervals but the concept of screening at any age is new for them. Families typically self-administer the ASQ in the physicians’ waiting rooms. Screening using the ASQ has required some changes in physician workflow. If a family is positively screened, a form is auto-filled to support the referral process to further care and treatment with specialists and other services. The research team has begun the evaluation phase and has started to pull and review clinical charts to assess each practice’s screening and diagnosis practices. In 2011 they will begin giving providers feedback on their screening rates through report cards. The team is also preparing sessions for families when a child receives a diagnosis. The team currently plans to begin publishing the research findings in 2012. The team is concurrently working on the AAP guidelines for general developmental screening and autism. These guidelines call for a comprehensive screening at the 18-month well-child visit. CHICA was designed to encourage integration and avoid duplication. Because there is need for screening of multiple conditions, there is currently a discussion weighing the various benefits of screening for autism versus general developmental screening., Grantee’s Most Recent Self-Reported Quarterly Status (as of December 2010):, The team is mostly on track with all project milestones. The one area that is somewhat behind the original schedule is the chart review process. Budget spending is on target., Preliminary Impact and Findings:, The team originally planned auto scanning and scoring of the ASQ but found that providers prefer to score the screening tool themselves. Qualitatively, they have been looking at the factors that contribute to use of the CHICA system, such as practice type, and provider characteristics. In general they are finding that younger physicians are quicker to adopt the system., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management., Business Goal:, Knowledge Creation *, AHRQ Priority Population, .
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Description: Target Population:, Adults, Summary:, Medication errors are a major source of patient injury, hospitalization, and death. Medication management in primary care is extremely complicated, given the continually expanding array of available therapies, fragmentation of care, proliferation of information sources, and numerous obstacles experienced by patients (e.g., cost). This study integrates interventions that target patients, providers, and the overall practice system in an effort to improve the medication management process. The overarching objective of this multicomponent intervention is to develop a protocol to reconcile medications through the phases of the patient-provider clinical encounter. The project provides patient education materials and medication lists that are automatically extracted from the Certification Commission for Health Information Technology-certified Epic Systems’ electronic medical record (EMR), EpicCare. Patients receive the materials in advance of their physician visit at the multispecialty primary care center. Patients then review the medication information contained within the system, indicating if there are any discrepancies or if they have any related questions or concerns. The nurse reviews the patient-provided information and places the output into the rooming sheet for the physician. The system encourages physicians to engage in shared decisionmaking by including prompts for eliciting questions and concerns as well as tailoring treatment plans to match patients’ needs and abilities. The physician will then clarify any issues with the patient and update the patient’s medication list in the EMR. If a new medication is prescribed, the system will generate a plain-language medication information sheet for the patient. The information sheet is automatically generated through project-developed “dot phrases” (system macros that automatically fill in descriptive text prompted by key words) in the EMR, an enhancement to the existing functionality of the Epic EMR. The clustered, controlled clinical trial will be randomized at the “pod” level to reflect the clinic’s organization into four areas (pods) with separate nursing staff and physicians. Through post-visit interviews and data extracted from the EMR, the project will assess post-visit discrepancies in the medication list, the patient’s functional understanding of their medication regimen, questions on adherence and safety, and a series of process measures to assure that the intervention is translatable to other organizations., Specific Aims:, Develop and test a multimedia program (which has been since revised to an educational print piece) to help patients understand the importance of both giving and receiving accurate information about medications (pre-visit patient intervention). (, Achieved, ) Use the EMR to encourage patient-centered medication management and extend the EMR medication management capability by training nurses to engage in a patient-centered review of current medications immediately before a patient sees the doctor. Leverage the EMR by developing a template that physicians can easily access to engage in a patient-centered discussion about new medications under consideration. (, Achieved, ) Work with the practice-based research network to disseminate and track the use of effective interventions, and create pathways for facilitating national distribution to other practices. (, Ongoing, ), 2010 Activities:, The team continued to engage in discussions with the Information Technology (IT) Leadership Team and General Internal Medicine practice directors to discuss options for pre-populating the EMR with medication information sheets and how they will be used by the physicians during the intervention. The study team also utilized health literacy experts to provide interim and final feedback on content and format. Once completed, the medication information sheets were pre-populated into the EMR by the IT team. A second pilot test was completed in January 2010 with two physicians and feedback was collected from the physicians to refine the intervention. A training session was performed at a physician meeting and followup e-mails were sent out to clarify any concerns. A trial run of the intervention was implemented in February 2010 to work out final problems and address physicians' concerns before starting recruitment. The team modified the intervention so patients could receive the educational print folder at the end of their visit. The previous protocol had patients obtain this folder when they checked in for their visit and many patients lost or misplaced it by the end of the visit. Data collection for the medication reconciliation portion of this study started in February and was completed in July. A total of 163 patients were recruited; 88 control and 75 intervention. The data are currently being analyzed. Data collection for the patient knowledge portion of this study began in December 2010 and will take approximately 6 months to complete., Grantee’s Most Recent Self-Reported Quarterly Status (as of December 2010):, Progress is mostly on track and the project is meeting most of its milestones. Project spending is roughly on target., Preliminary Impact and Findings:, One hundred and forty-four patients were enrolled with 69 in the control group and 75 in the intervention group. An additional 19 patients were excluded from analysis because they were seen by residents. Overall, 85 percent of patients had some type of discrepancy in their EMR medication list; however, no significant differences were found between the control and intervention groups in overall discrepancies. Types of discrepancies have been broken down to omissions, such as medications taken that are not on their list; commissions, such as medications on their list that they are not taking; and duplications. Overall, 18 percent of patients had at least one omission, 44 percent had at least one commission, and 29 percent had at least one duplication. No significant differences were found between control and intervention groups in type of discrepancy. The current medications evaluation study data are still being analyzed., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to support patient-centered care, the coordination of transitions across care settings, and the use of electronic exchange of health information to improve quality of care., Business Goal:, Implementation and Use
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Description: Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to improve the quality and safety of medication management via the integration and utilization of medication management systems and technologies., Business Goal:, Knowledge Creation, Summary:, Medical imaging informatics (MII) is responsible for a substantial portion of the total health care budget allocated to health information technology (IT). MII systems—usually comprising a combination of digital imaging systems, picture archiving and communication systems (PACS), radiology information systems, and voice recognition transcription technology—are now available from a number of commercial vendors. The commercial availability of MII systems makes IT in radiology somewhat unique compared with IT in most other sectors of health care. Nevertheless, a minority of radiology departments in U.S. hospitals have deployed comprehensive MII systems. MII systems have the potential to improve health care quality in at least four of the six focus areas described by the Institute of Medicine in its 2001 report Crossing the Quality Chasm; these include safety, effectiveness, efficiency, and timeliness. Deployment of a comprehensive MII system at Massachusetts General Hospital (MGH) began in 1995. The potential for cost savings was a deciding factor in the decision to proceed, and preliminary analysis suggested there was a substantial return on investment for these technologies. Widescale MII deployment at New York University (NYU) Medical Center began just prior to the start of data collection for this project which was to evaluate MII deployment at both MGH and NYU. The opportunity to study MII deployment at two large academic medical centers that went through the process almost a decade apart presented a unique opportunity to better understand the value of MII and to isolate the effects of MII from other secular trends in health care. The analysis identified the financial implications of deploying MII systems, including the costs and savings attributable to their use. The investigators also determined the effect of MII on health care quality and safety by examining outcomes such as process times, provider and capital utilization efficiency, throughput, and other metrics., Specific Aims, Determine the financial impact, including initial cost, savings, and rate of return, of the deployment of a comprehensive MII system in two large academic radiology departments. (, Achieved, ) Determine the impact of MII on health care quality, focusing on dimensions of quality, including process times, duplicate studies, and efficiency of provider utilization as defined by the Institute of Medicine. (, Achieved, ), 2008 Activities:, Data collection was completed prior to 2008. In 2008, focus was shifted to generalizing project results to make a financial and clinical practice model describing MII deployment and then to projecting possible effects of implementing MII technology in other settings., Impact and Findings:, The payback period for the MII system deployed by Academic Medical Center (AMC) 1 in 1995 was 48.8 months, and the internal rate of return in the year of payback was 56 percent. While the savings on film realized by AMC1’s deployment of the MII system positively contributed to the financial return, the payback period of the initial capital investment was not realized until the fifth year of operations (64.5 months), yielding an 18 percent internal rate of return. Alternatively, the net profitability of the MII volume, considered alone, yielded a 16 percent internal rate of return in just the 4th year of operations (54.2 months). The linear regression models of each of the four measures of productivity demonstrated a significant (p < 0.0001) relationship to the modeled extent of penetration of PACS at AMC1. No other terms were significant. None of the models for the mean times, nor the fraction of AMC2 exams reported within 3 days, demonstrated any significant relationship to the implementation of PACS at AMC2 or PACS at AMC1. Results demonstrate a strong business case for the use of MII systems in radiology departments and hospitals and show that the implementation and use of these systems is associated with measurable quality and efficiency improvements. These results should encourage institutions that have yet to implement these systems to do so, sooner rather than later., Selected Outputs, This project has no outputs to date., Grantee’s Most Recent Self-Reported Quarterly Status (as of August 2008):, This project is complete with all aims achieved., Milestones:, Progress is mostly on track., Budget:, On target.
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Description: Target Population:, Adults, Medicaid, Pediatric * , Safety Net, Summary:, The Health Information Technology for Economic and Clinical Health Act offers financial incentives for Medicaid providers to adopt and meaningfully use certified electronic health record (EHR) technologies. To ensure that eligible professionals, including physicians, dentists, certified nurse-midwifes, nurse practitioners, and some physician assistants, are able to qualify for and access these incentives, this 2-year project was initiated to study the barriers that Medicaid providers encounter when they try to achieve meaningful use as defined in the Centers for Medicare and Medicaid Services’ (CMS) EHR Incentive Program. The project is designed to develop actionable recommendations to help Medicaid providers take advantage of incentive payments, achieve meaningful use, and ultimately use health information technology (IT) to improve health care for the Medicaid population. The data collection methods for this project include both in-person and virtual focus groups with physicians, pediatricians, dentists, and mid-level providers. A technical expert panel (TEP) comprised of key stakeholders, including staff from the Office of the National Coordinator for Health IT, CMS, and the Health Resources and Services Administration, will provide guidance on the research plan, data collection design and implementation, data analysis, and the final report recommendations. These activities will help Federal stakeholders understand the barriers to meaningful use among Medicaid providers and will inform future Federal regulations, particularly in the development of meaningful use criteria for Stages 2 and 3. This project will yield actionable recommendations to increase effective EHR use by Medicaid providers to improve health care quality and will inform the development of recommendations for technical assistance to overcome identified barriers., Project Objectives:, Identify the barriers to eligibility for the incentive payments; barriers to adoption, implementation, or upgrading of EHR systems; and barriers to achieving meaningful use. (, Ongoing, ) Develop actionable recommendations to overcome barriers identified above, including but not limited to, technical assistance that could be made available to Medicaid providers. (, Upcoming, ) Provide data to inform the meaningful use objectives being developed by CMS for Stages 2 and 3 of the EHR Incentive Program. (, Upcoming, ), 2010 Activities:, The key project activities included development of the work plan and research plan, development of the data collection instruments, preparations for clearance for data collection from the Office of Management and Budget, planning for pilot testing, and developing recruitment strategies for both the pilot test and main study. In addition, the TEP was convened in September 2010 to provide input into the research plan and draft data collection instruments. Pilot testing will be completed in January 2011 with data collection for the main study anticipated to begin in summer 2011., Preliminary Impact and Findings:, There are no findings to report at this time., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to support patient-centered care, the coordination of care across transitions, and the electronic exchange of health information to improve quality of care., Business Goal:, Synthesis and Dissemination *, AHRQ Priority Population, .
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Description: Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to improve the quality and safety of medication management via the integration and utilization of medication management systems and technologies., Business Goal:, Knowledge Creation, Summary:, Today, hospital-based health information technology (IT) encompasses a wide range of quality and patient safety applications including: electronic medical records, personal health records, e-mail communication, clinical alerts and reminders, computerized physician order entry, computerized decision support systems, hand-held computers, electronic information resources technology, electronic monitoring systems, and telehealth consultative and diagnostic services. However, very few rural hospitals have developed or implemented these health IT capacities because of factors including expense, limited in-house IT expertise and staffing, and the fact that many health IT applications benefit from economies of scale that are unavailable to them. Currently, there are significant gaps in knowledge about the value of health IT in general, but they are especially pronounced in rural applications. There has been little systematic study of whether existing health IT technologies, or investment in the commonly implemented health IT projects, readily lend themselves to quality enhancement in rural hospitals. For a rural hospital with limited resources, there needs to be a better understanding of the fit between actual quality and safety problems and the health IT solutions under consideration. Rural hospitals could benefit substantially from assistance and tools to aid in their health IT decisionmaking. This grant was designed to address these knowledge gaps, using an in-depth study of Iowa’s 89 rural hospitals with a particular focus on its 80 critical access hospitals (CAHs). A major component of this research was focused on identifying and prioritizing the quality-of-care and patient safety issues facing rural hospitals; this was assessed by surveys, interviews with key personnel, and quantitative analysis. Related to this was the aim of identifying challenges and barriers facing rural hospitals embarking on health IT projects, using research methods including expert panels, case studies, and a literature review. Assessment work also included investigations of the correspondence between various types of health IT technologies and improvements in patient safety, as well as the cost-effectiveness of health IT for rural providers. The information gathered through the project’s research efforts was then synthesized into toolkits for rural providers in Iowa., Specific Aims, Characterize patient safety and health care quality issues in rural hospitals. (, Achieved, ) Characterize the health IT capacity and barriers of rural hospitals. (, Achieved, ) Identify which health IT capacities are most strongly related to patient safety and health care quality issues in rural hospitals. (, Achieved, ) Identify the cost of health IT in rural hospitals. (, Achieved, ) Develop toolkits to help rural hospitals make informed health IT investments. (, Achieved, ), 2008 Activities:, With data collection complete, the primary 2008 activities were analysis and dissemination of knowledge products., Impact and Findings:, The Iowa Hospital Association and the Iowa Department of Public Health–Iowa Medicare Rural Hospital Flexibility Program (FLEX) created a workgroup, the Iowa CAH Data Workgroup, of representatives from CAHs to focus on identifying “rurally relevant” patient safety and quality issues. The “rurally relevant” patient safety and quality issues that the Iowa CAH Data Workgroup identified as having the highest priority for Iowa CAHs were: medication errors, falls, appropriate assessment and treatment of chest pain presenting in the emergency department, and births for those hospitals that have obstetric services. They established a Web-based reporting tool for all CAHs to report on these five topics on a quarterly basis for benchmarking within Iowa’s CAHs. The Iowa CAHs have been participating in this voluntary reporting and benchmarking effort since 2005. Quantitative analysis showed that the only Agency for Healthcare Research and Quality Patient Safety Indicators (PSI) for which Iowa was substantially worse than the national benchmark involved maternal trauma during vaginal deliveries. An in-depth analysis of these procedures determined that a number of factors were involved, including maternal risk factors (e.g., higher prevalence of teenage mothers), baby risk factors (e.g., higher prevalence of large babies), and procedure risk factors. This compounding of risk factors occurred more often in rural hospitals and appeared to be related to emergency deliveries in rural hospitals that were not staffed to handle unplanned cesarean deliveries. Analysis of PSIs in rural hospitals before and after conversion to CAH status indicated improvement in indicator rates for prevalent complications coincident with enhanced financial performance. The team also found that the raw in-hospital mortality rate for acute myocardial infarction (AMI) in Iowa rural hospitals (14 percent) was twice the rate of Iowa urban hospitals (6.4 percent). However, AMI patients admitted to rural hospitals were a decade older and were sicker than those admitted to urban hospitals, in part because many AMI patients in rural hospitals are transferred to urban hospitals, and this sub-population of transfers is younger and healthier than those who remain at rural facilities. An instrumental variable approach to control for this trend caused the difference in in-hospital mortality rates to disappear. In a published review of existing literature, the project concluded that to expedite the spread of health IT in rural America, Federal and State governments, along with private payers—who are important beneficiaries of health IT—must make difficult decisions as to who pays for the investment in this technology. They must also drive standards, simplify approaches for reductions in risk, and create a workable operational plan. Toolkits developed included an algorithm to optimize AMI patient referrals, a health IT cost calculator, and online toolkit offering information on health IT implementation and best practices., Selected Outputs, Allareddy V, Ward MM, Allareddy V, et al. Effect of meeting Leapfrog volume thresholds on complication rates following complex surgical procedures. Ann Surg 2010;251(2):377-83. Li P, Schneider J, Ward MM. Converting to critical access status: how does it affect rural hospitals' financial performance?. Inquiry 2009 Spring;46(1):46-57. Li P, Ward MM, Schneider JE. Factors associated with Iowa rural hospitals' decision to convert to critical access hospitals status. J Rural Health 2009 Winter;25(1):70-6. Bahensky JA, Jaana, M, Ward MM. Health care information technology in rural America: electronic medical record adoption status in meeting the national agenda. J Rural Health 2008;24(2):101-5. Bahensky J, Moreau B, Frieden R, et al. Critical access hospital informatics: how two rural Iowa hospitals overcame challenges to achieve IT excellence. J Healthc Inf Manag 2008;22(2):16-22. Chi CL, Street WN, Ward MM. Building a hospital referral expert system with a Prediction and Optimization-Based Decision Support System algorithm. J Biomed Inform 2008;41(2):371-86. Clabaugh G, Ward MM. Cost-of-illness studies in the United States: a systematic review of methodologies used for direct cost. Value Health 2008;11(1):13-21. Li P, Bahensky J, Jaana M, et al. Role of multihospital system membership in electronic medical record adoption. Health Care Manage Rev 2008;33(2):169-77. James PA, Li P, Ward MM. Myocardial infarction mortality in rural and urban hospitals: rethinking measures of quality of care. Ann Fam Med 2007;5(2):105-11. Li P, Ward MM, Schneider JE. Effect of critical access hospital conversion on patient safety. Health Serv Res 2007;42(6 Pt 1):2089-108. Roberts LL, Ely J, Ward MM. Factors contributing to maternal birth-related trauma. Am J Med Qual 2007;22(5):334-43. Wakefield DS, Ward MM, Wakefield BJ. A 10-Rights framework for patient care quality and safety. Am J Med Qual 2007;22(2):103-11. Jaana M, Ward MM, Pare G, et al. Antecedents of clinical information technology sophistication in hospitals. Health Care Manage Rev 2006;31(4):289-99. Ward MM, Evans TC, Spies AJ, et al. National Quality Forum 30 safe practices: priority and progress in Iowa hospitals. Am J Med Qual 2006;21(2):101-8. Ward MM, Jaana M, Bahensky JA, et al. Clinical information system availability and use in urban and rural hospitals. J Med Syst 2006;30(6):429-38., Grantee’s Most Recent Self-Reported Quarterly Status:, This project is complete. All aims have been met and outputs have been developed to help rural providers assess their health IT needs and possibilities., Milestones:, Progress is mostly on track., Budget:, Somewhat underspent, approximately 5 to 20 percent.
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Description: Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to improve the quality and safety of medication management via the integration and utilization of medication management systems and technologies., Business Goal:, Knowledge Creation, Summary:, Patients in adult intensive care units (ICUs) require close monitoring, frequent invasive procedures, multiple medications, complicated decisionmaking processes, and multidisciplinary care. This complex care coupled with inadequate nurse-patient ratios, provider fatigue, on-the-job training, and poor communication can result in substantial morbidity, mortality, and costs. A powerful influence on the quality of ICU care is the presence of critical care physicians (intensivists) in the unit. Telemedicine, a common form of health information technology (IT), has been used to provide remote intensivist monitoring for ICUs. Remote ICUs connected via telemedicine technology (tele-ICUs) allow intensivists to simultaneously monitor more patients than possible by standard onsite care, and to extend intensivist care to patients in ICUs where intensivists would otherwise be unavailable, such as rural and small community hospitals. Furthermore, tele-ICUs may have decision support software to help identify subtle trends like rising creatinine and falling oxygen saturation that need to be addressed to prevent complications. The tele-ICU also makes intensivists more available to nurses. A preliminary study was conducted over a 4-week period between November and December 2005 to determine how changes to the user interface in a tele-ICU could be improved. An ICU remote monitoring facility affiliated with a large health care system located in the Gulf Coast region was selected. The facility had been using the proprietary eICU® technology developed by VISICU, Inc., for 21 months and remotely monitored nine ICUs with a total of 132 beds in five of the health care system’s hospitals at the time of the study. An electronic data collection tool was developed and implemented as a Microsoft Access Form application and installed on a tablet PC. The information collected included time-stamped tasks and activities, information resources (i.e., artifacts), participants, and any additional information manually entered by the observer. The survey of attitudes about safety and teamwork was given to the three ICUs that implemented the system at or around the beginning of the project—several others had either adopted the system earlier or had no plans to do so during the term of the grant. The results were compared with a baseline established by safety surveys administered annually to the whole network of hospitals. Quantitative data were captured, as well, highlighting the effect of tele-ICU on patient outcomes, including length of stay, conditions developed while admitted, and mortality rates (controlled for differences in severity of the initial diagnosis), as well as on hospital costs., Specific Aims, Use human factors engineering techniques to determine how changes to the user interface of the tele-ICU may increase the value of the technology. (, Achieved, ) Measure changes in health care provider attitudes about teamwork and safety climate after implementation of the tele-ICU. (, Achieved, ) Measure the effect of a tele-ICU on mortality, complications, and length of stay in ICUs in a tertiary care teaching hospital, and in seven community (including two “small”) hospitals using a before-and-after study design. (, Achieved, ) Measure the cost-effectiveness of the tele-ICU. (, Achieved, ), 2008 Activities:, Analyses of the data concluded in 2008, and papers were prepared for publication., Impact and Findings:, The preliminary study of tele-ICU workflows was valuable in familiarizing researchers with the function of the remote monitoring unit. It did not lead to changes made to the interface. The teamwork and safety surveys demonstrated significant improvements, which recommend this model for larger-scale trial implementations. The initial sample for patient outcomes consisted of 4,167 subjects. Elimination of cases with missing data yielded a final sample of 4,142 subjects, of whom 2,034 were pre-tele-ICU and 2,108 were post-tele-ICU. The crude ICU mortality rates were 12 percent pre- and 9.9 percent post-tele-ICU (p = .03). The dominant variable was severity of initial diagnosis, as measured by the Simplified Acute Physiologic Score (SAPS) II standards for intensive care, which assigns a value ranging from 0 to 150 to each patient, with higher numbers indicating more serious diagnoses. Patients with SAPS II > 50 (17 percent of the sample) had approximately 20 to 50 percent reduction in risk of hospital mortality depending upon the precise SAPS II score. These improvements in mortality did not hold for patients with SAPS II < 50. Of the 4,142 patients, the 3,789 patients who survived to ICU transfer were analyzed for ICU length of stay (LOS). The crude mean hospital LOS for survivors in the pre-tele-ICU group was 9.8 days versus 10.7 days for the post-tele-ICU group (p = .006). The tele-ICU effect (p = .19) was moderated by linear and quadratic SAPS components (p = 3.8×10-6) such that only surviving patients with SAPS > 70 had shorter hospital LOS in the post-tele-ICU intervention. Secular trends toward declining mortality among ICU patients could have accounted for the reductions observed here. However, the fact that the observed mortality reduction occurred only among the patients most likely to benefit from this intervention (the most severely ill patients), and the large magnitude of the reduction, argues against the secular trend hypothesis and supports the hypothesis that the tele-ICU intervention caused the reduction in mortality. The sickest patients are those most likely to have unexpected changes in their medical condition that require rapid intervention (arrhythmia, hypotension, sepsis, hypoxia). The tele-ICU can provide this rapid response due to the constant monitoring (including computerized alerts for changes in key physiologic parameters) and availability of nurse and physician intensivists. Even among the sickest patients, this research needs replication, and there is a need to compare use of tele-ICU technology with less expensive, but also powerful, quality improvement interventions., Selected Outputs, Chu-Weininger M, Wueste L, Lucke J, et al. The impact of a tele-ICU on provider attitudes about teamwork and safety climate. Qual Saf Health Care. 2010 Apr 27. [Epub ahead of print] Thomas EJ, Lucke JF, Wueste L, et al. Association of telemedicine for remote monitoring of intensive care patients with mortality, complications, and length of stay. JAMA 2009;302(24):2671-8. Tang Z, Weavind L, Mazabob J, et al. Workflow in intensive care unit remote monitoring: a time-andmotion study. Crit Care Med 2007;35(9):2057–63., Grantee’s Most Recent Self-Reported Quarterly Status:, The project has completed., Milestones:, Progress is mostly on track., Budget:, Somewhat underspent, approximately 5 to 20 percent.
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Description: Target Population:, Chronic Care * , Diabetes, Elderly * , Medically Underserved, Veterans, Summary:, This project tests whether an automated self-management monitor (ASMM) that reminds patients to self-monitor their blood glucose (SMBG), prompts them to take medications, and provides education on the impact lifestyle choices has on glycemic control and self-management behaviors. The ASMM, which was developed by the project team, is composed of a simple personal computer-glucometer interface unit with specialized software. The software receives data downloaded through the glucometer interface; interprets the measures; matches them with individualized profiles for glycemic monitoring and control; and provides appropriate, individualized audio feedback. Feedback is based on a fuzzy logic algorithm, which is feedback on one value while taking into account previous values, as well as the Common Sense Model of Illness, or experience-based beliefs. These provide information on long-term control, as well as single glucose measures. To demonstrate the effectiveness of the intervention, the project team is recruiting adults from community health centers and the Veteran’s Health Administration to participate in a randomized, controlled trial. To be eligible for participation, subjects must have poorly controlled diabetes, defined as hemoglobin A1c (HbA1c) levels greater than 8 percent. Once participants are recruited, the project team contacts providers to obtain information about patients’ glucose checking schedules and glycemic targets. A team member visits a participant’s home to collect baseline data, and provide the glucometer and supplies necessary to perform SMBG. At a second home visit 3 months later, a member of the team provides the participant with a standard set of educational materials, administers study surveys, determines any self-reported change in medication regimen, and downloads glucometer data. Patients are then randomized into intervention and usual care groups. For intervention group participants, the researcher also installs the ASMM, trains the participants to use the system, and reviews the reminders provided by the system. Additional home visits are conducted by the research team at 9 and 15 months after enrollment. The primary outcome measure is change in HbA1c. Secondary measures include self-management behaviors such as SMBG frequency, nutritional choices, physical activity, medication adherence, and patient use of diabetes educational materials., Specific Aims:, Demonstrate that use of the ASMM improves glycemic control in inadequately-controlled people with Type 2 Diabetes. (, Ongoing, ) Demonstrate that this effect is sustained over longer term followup. (, Ongoing, ) Identify self-management practices that improve in people using the ASMM. (, Ongoing, ), 2010 Activities:, The trial was completed in October 2010. A total of 201 participants were randomized, with 102 individuals in the intervention group and 99 in the usual care group. Of these, 71 intervention participants and 89 usual care participants completed the 15-month study with analyzable ASMM data. During the year, significant effort was dedicated to data cleaning, coding, and analysis. Statisticians reviewed the data files to merge and reconcile data recorded by the glucometers and data collected by the ASMM docking system. Additionally, statisticians started to look at differences between the data collected from the Veteran’s Health Administration and community-based care, including differences in loss-to-followup between the two study arms. The study team is also trying to determine which patients engaged the intervention. While some patients decided that they did not want to keep the ASMM system, other patients kept the system but did not use it. Therefore the analysis team will perform an intent-to-treat analysis as well as a second analysis of patients who actively engaged with the intervention to determine whether those who actually received feedback from the ASMM demonstrated improved glycemic control. The team will also assess characteristics of individuals who used the intervention to help inform clinicians about patients who are most likely to benefit from this approach to diabetes care., Grantee’s Most Recent Self-Reported Quarterly Status (as of December 2010):, All aims and milestones are on track and data collection is in process. The trial has been completed and data have been collected and cleaned. Analyses to evaluate the intervention will be the focus of the next year, Preliminary Impact and Findings:, Randomization was successful, with no significant differences in demographic variables or baseline HbA1c levels between the usual care and intervention groups. Efforts to distinguish between intervention group participants who used the ASMM and those who did not indicate that seven participants refused at the installation stage but stayed in the study, three had the system installed but never used it, three were unable to use the system for other reasons, and 18 dropped out of the study. Preliminary analyses of dietary data show no significant relationships between broad dietary components (total carbohydrates, total calories, total protein, total fiber, calories from fat) and HbA1c. However, participants with initial HbA1c levels lower than 14 appear to have lower total carbohydrates, total fiber, and fat-derived calories than those with baseline HbA1c greater than 14. Analyses of patient characteristics and glycemic control identified a statistically significant correlation between commonsense beliefs about diabetes and glycemic control., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to support patient-centered care, the coordination of care across transitions, and the electronic exchange of health information to improve quality of care., Business Goal:, Implementation and Use *, AHRQ Priority Population, .
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Description: Target Population:, General, Summary:, The patient-centered medical home (PCMH) is emerging as a new model for providing population-based, patient-centered primary care. Despite professional interest and endorsement however, there is limited evidence on what impact the PCHM model has on the cost and quality of health care services, so questions remain on how to best implement, measure, and pay for this new service delivery model. Further implementation requires research to better understand the model's impact on providers and policymakers. Specifically, there is a need to understand the marginal effectiveness of the PCMH over the traditional primary care model. The Research Agenda for a Patient-Centered Medical Home conference convened national researchers, representatives of major primary care professional organizations, health care purchasers, payers, patient advocates, and policymakers to discuss the research agenda needed to move PCMH from a demonstration model to an evidenced-based standard of care., Specific Aims:, Inform and advance the state-of-the-art-and-science and real-world experience about the PCMH. (, Achieved, ) Develop partnerships and build capacity to implement a practical evaluation model that can be used by health plans, government payers, and policymakers to assess components of the PCMH and alternative models. (, Achieved, ) Develop and recommend a research agenda to inform the development and broad implementation of the PCMH model. The research agenda will specifically address the “business case” for adopting the PCMH model as well as the clinical and cost consequences of implementing PCMH. (, Achieved, ) Disseminate the synthesis of the conference, including background and descriptive information, via peer-reviewed literature, the Web, and presentations at relevant national health policy and professional association meetings.(, Achieved, ), 2010 Activities:, Several manuscripts developed out of the 2009 conference were published in 2010. The conference was held on July 27 th and 28 th , 2009 in Washington, DC. It was cohosted by the Society of General Internal Medicine, the Society of Teachers of Family Medicine, and the Academic Pediatrics Association, with support from the Agency for Healthcare Research and Quality, the American Board of Internal Medicine Foundation, and the Commonwealth Fund., Grantee’s Most Recent Self-Reported Quarterly Status (as of December 2010):, The conference was held in 2009 on schedule and on budget., Preliminary Impact and Findings:, Findings from the conference were discussed in several manuscripts, seven of which were published in the Journal of General Internal Medicine , and one which was published in Health Affairs ., Strategic Goal:, Not Applicable, Business Goal:, Synthesis and Dissemination
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Description: Target Population:, Adults, Chronic Care * , Heart Disease, Summary:, The complexity of patients’ medical conditions is increasing, making preventive care and disease management more difficult. There is growing interest in integrating personal health records (PHRs) with providers’ electronic medical records (EMRs) to assist patient self-management and improve care for complex diseases. However, studies that evaluate the impact of PHRs on care outcomes are few. This project seeks to improve health care outcomes in patients who have or are at high risk for developing cardiovascular disease (CVD) by promoting patient self-management at more than 80 primary care practices, including small and large practices. Major activities include development of a patient-specific, active and interactive component to an existing electronic PHR; a randomized controlled trial to determine the effectiveness of passive and active PHR systems for improving adherence and clinical outcomes; and cataloging the facilitators and barriers to PHR implementation and use. To accomplish the first task, a user group was assembled to determine which features of an active PHR are considered to be most acceptable and useful. To facilitate the second task, target enrollment for the trial has been set at 1,200 patients with complex chronic disease leading to increased cardiovascular risk. This target allows for a 20 percent drop-out rate to arrive at a sample of 1,000 participants to be randomized to a passive PHR (n = 500) or an active PHR (n = 500) at four sites where the PHR currently is installed and in use. All participants will be surveyed using the PHR, along with nurses and physicians at the study sites. Focus groups will also be conducted among PHR participants, nurses, and physicians to determine the barriers to and facilitators of PHR use. Outcomes to be assessed include improvement in control of risk factors, frequency of compliance with testing guidelines, and clinical outcomes. The PHR for this project, Health Trak, interfaces with EpiCare Electronic Health Record, the organization’s Certification Commission for Health Information Technology-certified EMR system. The passive PHR allows patients to view portions of their EMR—including problem lists, medication lists, and test results—to communicate electronically with their physician’s office and to track values of home-monitored blood pressure and glucose. This is the standard form of a PHR for many EMRs. The active PHR has the features of the passive PHR but also electronically advises patients to check a secure Web site when disease self-management tasks or preventive services are necessary. This project will help determine if the use of an active patient self-management version of an existing PHR can reduce cardiovascular risk factors., Specific Aims:, Develop a patient-specific, active and interactive component to an existing electronic PHR for patients with complex illnesses and conditions that contribute to the development of cardiovascular disease. (, Achieved, ) Conduct a randomized controlled trial of the effectiveness of passive and active PHR systems for improving adherence and clinical outcomes of these patients in an ambulatory environment. (, Ongoing, ) Enumerate and catalog the barriers and facilitators to implementation and use of an electronic PHR among providers and patients in an ambulatory setting. (, Upcoming, ), 2010 Activities:, Patient user groups and focus groups were conducted to inform the development of the interactive component of the PHR, which was activated in 2010 in both EpicCare and Health Trak. E-mail and text alerts are being transmitted to the patients in the intervention group based on the specific cardiovascular health maintenance activities for which the patient is due. Study recruitment for the randomized controlled trial went live June 2010. In the first 6 months of recruitment, over 400 patients were enrolled. Although the rate of recruitment was slightly lower than the desired levels, active and passive recruitment strategies continue to be utilized. To help increase recruitment the project team and providers met one-on-one to answer their questions and encourage participation. The study team anticipates reaching the enrollment target by spring 2011. The task of writing the EMR reports has also been initiated. These reports will be used to extract the EMR data such as demographics, PHR usage statistics, and outcome variables., Grantee's Most Recent Self-Reported Quarterly Status (as of December 2010):, No reports were submitted to the AHRQ Research Reporting System in 2010. However, Dr. Roberts provided information that, as of June 2010, the project was underspent due to hiring challenges and because the data center had not yet been invoiced., Preliminary Impact and Findings:, This project has no findings to date., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to support patient-centered care, the coordination of care across transitions in care settings, and the use of electronic exchange of health information to improve quality of care., Business Goal:, Knowledge Creation *, AHRQ Priority Population, .
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Description: Target Population:, Adults, Chronic Care * , Mental Health/Depression, Summary:, Providing care for people with mental health illness poses unique and difficult challenges. Without electronic communication, behavioral health providers cannot follow the full treatment path of patients as they move among various providers in urban and rural outpatient settings, mental health hospitals, protective custody, and crisis mental health facilities. This project explores how the exchange of health information between rural and urban providers in the behavioral health field can improve ambulatory patient care coordination and safety across treatment settings. Specifically, the project examines provider barriers to technology acceptance in the behavioral health setting, behavioral health care technology acceptance and adoption, and the effects of health information exchange (HIE) on clinical outcomes. The development and implementation of a regional HIE in southeast Nebraska will decrease the time it takes for providers to access comprehensive and accurate information, thus creating better access to patient information between and among the provider care team serving an individual with mental illness. This, in turn, will improve continuity of care by providing an electronic link between the multiple service settings that serve Nebraska residents. The provision of basic electronic information to coordinate patient care between behavioral health providers, rural hospitals, and the emergency behavioral health system, will improve the long-term health outcomes of individuals with serious, persistent mental illness. During the first phase of the project, a committee issued a request for proposals, researched vendor qualifications, and ultimately selected products from NextGen Healthcare, certified by the Certification Commission for Health Information Technology. At the same time, the team began to design the HIE and also conducted a behavioral health provider survey focused on technology acceptance. In the second phase of the project, currently underway, the team will develop the HIE infrastructure, equip provider offices with new or updated technology, and provide training to participating providers. In phase three, the team will implement the HIE in three provider facilities. Once the environment is established, data will be collected to evaluate how timely access to accurate information might improve the quality of care for those experiencing a behavioral health crisis and who have an immediate need for entrance into the emergency behavioral health care system., Specific Aims:, Identify provider barriers to technology acceptance. (, Ongoing, ) Implement an HIE among three major behavioral health provider facilities. (, Ongoing, ) Collect data on how timely access to accurate information relates to quality of care. (, Upcoming, ), 2010 Activities:, System design activities continued to focus on organizational development of the Southeast Nebraska Behavioral Health Information Network’s Regional Health Information Organization and on system implementation. The State's Operational Plan for statewide HIE was approved by the Office of the National Coordinator for Health Information Technology. Early in the year, the electronic Behavioral Information Network (eBHIN) encountered system design challenges as well as problems in recruiting appropriate project management personnel, delaying system implementation by 6 months. However, a new project manager, working with an information technology consultant and NextGen, has been able to adapt the project plan and a new “Go-live” date has been established for June 2011. In collaboration with the University of Nebraska, Dr. Baker sponsored an “HIE Kick-Off Celebration.” The President of NextGen Healthcare Information Systems joined a group of approximately 100 stakeholders for a presentation highlighting system capabilities and outlining plans for implementation. A data center hosting timeline was developed to facilitate a production environment available in preparation for go-live. An HIE implementation team has been established representing all of the organizations that will be participating in the network. The project manager has been working with this team and NextGen on finalizing the record design and functionality. The referral management and waitlist management functionality has been defined. Working with a core group of providers identified “super users,” the application has been built with provider-specific information. The eBHIN team continues to work with NextGen and the Magellan Behavioral Healthcare system to design the file transfer protocol for the upload of registration and authorization information to the State of Nebraska. The file transfer structure has been designed and preliminary testing has begun. Research activities focused on dissemination, data collection, data analysis, and interpretation of a statewide survey, and on completing analysis and interpretation on a provider survey conducted in 2009. The statewide survey focused on the benefits and barriers to electronic sharing of client information. This survey was sent to all practicing behavioral health providers in Nebraska. A total of 2,010 surveys were sent out with 667 respondents. The grant team was pleased with the response rate given the population. Highlights of these findings were presented at the HIE Kick-Off Celebration. A manuscript is being developed to disseminate the findings from the statewide survey. A manuscript summarizing findings from the 2009 provider survey has been published., Grantee’s Most Recent Self-Reported Quarterly Status (as of December 2010):, The project was significantly underspent during this calendar year due to pending acquisition of the Data Center equipment, but project progress is close to schedule with some deviations. Dr. Baker is now moving ahead at a very rapid pace. A go-live date has been set for March 2011, a Data Center hosting timeline is in place, and the application has been built with provider-specific information., Preliminary Impact and Findings:, Most providers reported feeling positively disposed to adopting electronic health records. Many expressed the belief that the decisionmaking about electronic health records is different in behavioral health than other sectors of the medical community. For instance, most providers believed that information in behavioral health records is more sensitive and the client more vulnerable. Also, some were concerned that the subjectivity of behavioral health information can make electronic sharing a complicated process. Benefits and barriers to technology acceptance, as articulated by providers, were grouped into six theme areas: security and privacy; delivery of behavioral health care; quality of care; adoption and implementation; financial impacts; and business operations., Strategic Goal:, Develop and disseminate health IT evidence and evidence-based tools to support patient-centered care, the coordination of care across transitions in care settings, and the use of electronic exchange of health information to improve quality of care., Business Goal:, Implementation and Use *, AHRQ Priority Population, .