Discussion: Clinical Decision Systems Comparative

Discussion: Clinical Decision Systems Comparative ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Clinical Decision Systems Comparative I’m studying and need help with a Nursing question to help me learn. Compare Emory Health University hospital and Grady Memorial Hospital. The link posted below is the compare website for emory and grady hospital. Discussion: Clinical Decision Systems Comparative https://www.medicare.gov/care-compare/compare?prov… Provide recommendations that are supported by the research literature for clinical decision systems that may potentially explain why the healthcare organizations are performing well, including an explanation of what you as a nurse leader might continue doing to sustain a high level of performance. Alternatively, explain why the healthcare organizations are not meeting standards, including an explanation of what you as a nurse leader might do to help reach standards in the selected healthcare organizations. Be sure to cite at least 3 scholarly resources to support your recommendations. Response includes thoughtful and comprehensive evaluation of recommendations for clinical decision systems that will enhance performance measures for healthcare organizations. Response includes a thorough and detailed explanation of the impact of nurses on the performance of a healthcare organization. At least three references to scholarly resources are included. (5-6 years old) https://www.ahrq.gov/cpi/about/otherwebsites/clini… personalizing_nursing_home_compare.pdf how_online_quality_ratings_influence_patients__choice_of_medical_providers_…____walden_university_library.pdf clinical_decision_support_system.pdf Health Services Research © Health Research and Educational Trust DOI: 10.1111/1475-6773.12588 BEST OF THE 2016 ACADEMYHEALTH ANNUAL RESEARCH MEETING Personalizing Nursing Home Compare and the Discharge from Hospitals to Nursing Homes Dana B. Mukamel, Alpesh Amin, David L. Weimer, Heather Ladd, Joseph Sharit, Ran Schwarzkopf, and Dara H. Sorkin Objective. To test whether use of a personalized report card, Nursing Home Compare Plus (NHCPlus), embedded in a reengineered discharge process, can lead to better outcomes than the usual discharge process from hospitals to nursing homes. Data Sources/Setting. Primary data collected in the Departments of Medicine and Surgery at a University Medical Center between March 2014 and August 2015. Study Design. A randomized controlled trial in which patients in the intervention group were given NHCPlus. Participants included 225 patients or their family members/surrogates. Data Collection. Key strokes of NHCPlus users were recorded to obtain information about usage. Users were surveyed about usability and satisfaction with NHCPlus. All participants were surveyed at discharge from the hospital. Survey data were merged with medical records. Principal Findings. About 85 percent of users indicated satisfaction with NHCPlus. Compared to controls, intervention patients were more satis?ed with the choice process (by 40 percent of the standard deviation p < .01), more likely to go to higher ranked ?ve-star nursing homes (OR = 1.8, p < .05), traveled to further nursing homes (IRR = 1.27, p < .10), and had shorter hospital stays (IRR = 0.84, p < .05). Conclusions. Personalizing report cards and reengineering the discharge process may improve quality and may lower costs compared to the usual discharge process. Key Words. Nursing homes, report cards, hospital discharge, nursing home compare, quality The vast majority of patients, about 90 percent, enter nursing homes from hospitals.1 By law (United States Congress 1997) and as per the hospitals’ conditions of participation in Medicare (United States Department of Health & Human Services and Centers for Medicare & Medicaid Services 2013), hospitals must have a discharge plan developed for each patient by a registered nurse, a social worker, or other quali?ed professional. They are required to 2076 Personalizing Nursing Home Compare 2077 provide each patient discharged to a nursing home with a list of nursing homes in the geographic area requested by the patient. The regulations recommend, although do not require, that hospitals obtain the list of nursing homes from the quality report card published by the Centers for Medicare & Medicaid Services (CMS) and Nursing Home Compare (NHC) (Medicare.gov 2016). Hospitals are not required to provide information about quality of nursing homes and, in fact, are not allowed to steer patients to a speci?c nursing home, but they are not prohibited from providing information about quality or making patients and families aware of NHC and discussing the information it provides with patients and families (Raffa 2012). Since the introduction of NHC, the expectation among professionals and policy makers has been that NHC would become a major information resource in the discharge process and would aid patients and families in making their nursing home choices. Discussion: Clinical Decision Systems Comparative The empirical evidence on its use is mixed. On one hand, Castle (2009) reported that 31 percent of families of nursing home residents used report cards. On the other hand, Mukamel et al. (2007) found that most nursing home administrators did not think that their clients were in?uenced by NHC, and a recent paper by Konetzka and Perraillon (2016) found that use of NHC was limited by both awareness and trust. Werner et al. (2012) found that patients tend to seek nursing homes with better reported performance in NHC, but they concluded that the effect was relatively small. We hypothesized that these mixed ?ndings and somewhat unimpressive record of NHC might be traced to two factors. The ?rst is the limited availability of NHC at the time and place when patients and families are making their choice of a nursing home, which for the majority is the hospital bed where a computer with Internet connectivity is not available. While NHC has recently been made available on a smartphone-compatible platform, most patients and families, unless prompted and directed to the NHC site, are not likely to ?nd it on their own and consult it when they need to choose a nursing home Address correspondence to Dana B. Mukamel, Ph.D., Department of Medicine, Division of General Internal Medicine, University of California, Irvine, 100 Theory, Suite 120, Irvine, CA 926971835; e-mail: [email protected]. Alpesh Amin, M.D., is with the Department of Medicine, University of California, Irvine, Orange, CA. David L. Weimer, Ph.D., is with the Department of Political Science, Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI. Heather Ladd, M.S., and Dara H. Sorkin, Ph.D., are with the Department of Medicine, Division of General Internal Medicine, University of California, Irvine, CA. Joseph Sharit, Ph.D., is with the Center on Aging, Mental Health Hospital Center, University of Miami, Miami, FL. Ran Schwarzkopf, M.D., M.Sc., is with the NYU Langone Seaport Orthopaedics, New York, NY. 2078 HSR: Health Services Research 51:6, Part I (December 2016) (Konetzka and Perraillon 2016). Therefore, unless the hospital discharge planner brings the information to patients, the patients remain unaware of NHC and do not access it. The second limitation of NHC is that it has a large number of clinical quality measures (QMs), as well as measures of staf?ng levels and health inspections, about 20 altogether. Studies have shown that the human mind has dif?culty making choices when faced with several options that differ on a large number of attributes (Scammon 1977; Malhotra 1982). To help consumers, CMS has introduced four 5-star measures. These combine the individual QMs into four composites, that is, summary statistics, and assign each provider stars based on their performance—?ve stars to the best performers and one star to the worst (Medicare.gov 2015). The limitation of the ?ve-star measures, however, is that they are “one size ?ts all” measures. The individual QMs that are included in the ?ve-star measures and the rules that determine how to combine the QMs into the star measures re?ect importance weights determined by CMS with the advice of experts (Centers for Medicare & Medicaid Services 2010). These measures do not recognize that patients’ medical needs and preferences vary. A recent study (Mukamel et al. 2016) has shown that when patients are given the opportunity to construct their own composite measures from the NHC QMs, staf?ng and health inspections information, they construct measures that are substantially different from the NHC ?ve-star measures.Discussion: Clinical Decision Systems Comparative These individual composite measures also vary substantially across patients. When using their personal composite measures, only about a third of patients ranked nursing homes in their choice set the same as if they would have used the overall NHC ?ve-star measure. The other two-thirds of patients ranked nursing homes differently than does the NHC ?ve stars. These patients perceive quality and make nursing home choices that differ from choices based on the NHC ?ve-star rankings. To address these two limitations, personalization and accessibility, we developed an alternative to the “usual discharge process,” or UDP, called the NHCPlus discharge. We report here on the results of a randomized controlled trial (RCT) in which we tested the NHCPlus discharge process against the UDP. The NHCPlus Discharge Process The NHCPlus discharge process has two elements: the NHCPlus app and the reengineered discharge process in which the app is embedded. Personalizing Nursing Home Compare 2079 The NHCPlus app has three modules: (1) an educational module provides information to users about each of the QMs, staf?ng, and health inspections measures, and their implications for nursing home residents; (2) a preference elicitation module allows users to identify the QMs, staf?ng, and health inspections measures they wish to include in their composite and their relative importance; and (3) a results module provides a sorted list of nursing homes in the users’ choice sets and the QMs of these nursing homes. NHCPlus has two versions. One is designed for short-stay nursing home patients, which are those who enter primarily for rehabilitation and postacute care with the expectation of returning to the community after 1 or 2 weeks. The other is designed for long-stay patients who are likely to stay in the nursing home inde?nitely. NHCPlus combines the user’s ranking of the QMs with the CMS published values for each QM to obtain the individualized composite quality score. Users have the option to add price information and consider distance as well. NHCPlus allows patients and families to create their own personal composite measure based on their own medical needs and preferences, and the NHC QMs. The reengineered discharge process begins as soon as the decision of discharge to a nursing home has been made by the clinical team. The decision is relayed to the patient and the family, and an iPad with NHCPlus on it is given to the patient and left at the bedside to ensure early engagement and accessibility. The patient and family can continue to use NHCPlus until they have made a decision, at which time they send their ?nal sorted list of nursing homes electronically to the discharge planner to begin placement. This process contrasts with the UDP, in which patients and families often are not aware of the discharge decision for several hours to a day, at which time they are given a list of nursing homes and told to choose one. Under the UDP, they are typically not offered any information about the nursing homes on the list, except for address and phone number. Hypotheses and Outcomes Tested in the RCT The design of the NHCPlus app, which includes both an educational and a preference elicitation module, led us to expect that compared with the UDP, NHCPlus patients will be better informed about their decisions and, therefore, will be more satis?ed with their decision-making process and make better choices. Hence, we expected them to go to better quality nursing homes and to be willing to travel further, as they trade off distance from their home in favor of going to a better nursing home.Discussion: Clinical Decision Systems Comparative Furthermore, we expected them to be 2080 HSR: Health Services Research 51:6, Part I (December 2016) discharged earlier for two reasons. First, by providing patients with access to NHCPlus at the bedside as soon as the decision to discharge to a nursing home was made, patients and families were able to start “shopping” for nursing homes early and were likely to ?nish earlier. Second, having understandable information about what is important when choosing a nursing home, and having gone through the preference elicitation exercise, users were likely to be more con?dent in their decision (as we hypothesize above) and might have required less time to reach a decision. We, therefore, tested the following four main hypotheses. Compared with the UDP group, the NHCPlus group would have on average: H1: More con?dence in the decision/choice of nursing home they made; H2: Higher satisfaction with the decision process; H3: Higher likelihood of discharge to a better quality nursing home in the patient choice set; H3a: Measured by the NHC expert benchmark; H3b: Inferred from travel to further nursing homes; H4: Shorter hospital length of stay. M ETHODS Description of the RCT and Data The NHCPlus discharge was tested in an RCT with 225 patients admitted from the community and discharged to nursing homes from a University Medical Center, Departments of Medicine and Surgery, between March 2014 and August 2015. The discharge process began as soon as the medical team informed the patient that a nursing home discharge was needed. Potential patients or their families (if the patient was unable to consent) were recruited into the study, consented, and then randomized into two groups. The intervention group (118 patients) received NHCPlus to assist them and their families in choosing a nursing home. The control group (107 patients) received the UDP only. For patients randomized to NHCPlus, the project coordinator secured the iPad to the patient’s bed, provided background on NHCPlus and how to use it, and started the patient on the app. Patients and families (the users) were allowed to interact with the app for as long as they needed in order to reach a decision. Patients kept the iPad anywhere from a few hours to a few days. Often NHCPlus was used by patients together with their families or by the Personalizing Nursing Home Compare 2081 families alone. It is not atypical for nursing home placement decisions to be made by family members, as many patients are cognitively unable to make the decision (Castle 2003). Therefore, we view the decision-making unit in this study as the patient and the family. More details about NHCPlus and the RCT were reported in Sorkin et al. (2016). Usability Data. We collected key strokes as well as all other data that were entered into the iPads by the 118 patients and family members randomized to the NHCPlus group. This included information about who were the users (patient or family), information about their use of the educational module, information about their preferences, and information about their choices of nursing homes. Of the 118 in the NHCPlus group, 116 responded to a usability survey about their experience with NHCPlus. Discussion: Clinical Decision Systems Comparative This survey was collected on the iPad and was administered once the users sent their ?nal list of nursing homes to the discharge planner, indicating that they were ?nished using NHCPlus. Medical Records. Data for all 225 patients were obtained. These data included admission and discharge dates, MS-DRG codes, primary and secondary diagnoses and procedure codes, date of birth, gender, zip code of residence, the nursing home that the patients were discharged to, and the patients’ discharge planners. Exit Survey. Of the 225 study participants, 196 responded to an exit survey administered at discharge from the hospital: 29 (13 percent) did not complete the exit survey, with equal attrition rates for both groups. The exit survey included information about the patient and the decision maker (if different), including race and ethnicity, income, and education. It also measured con?ict with the decision and satisfaction with the decision process. To assess decisional con?ict, participants were asked ?ve questions that assessed decision uncertainty, speci?c factors contributing to the uncertainty, and perceived effectiveness of the decision making. For example: “The decision to select [?ll in name of the nursing home] was hard for me to make.” Items were adapted from O’Connor (1995) and Wills and Holmes-Rovner (2003). Ratings were made on a 5-point scale (1 = strongly agree, 5 = strongly disagree). Responses were reverse coded if needed and averaged to create a variable representing less con?ict, which we label as “con?dence in the decision.” Satisfaction with 2082 HSR: Health Services Research 51:6, Part I (December 2016) the decision was assessed using a ?ve-item scale adapted from Wills and Holmes-Rovner (2003). For example: “I am satis?ed with my decision to go to [?ll in name of the nursing home].” Ratings were made on a 5-point scale (1 = strongly agree, 5 = strongly disagree). All responses were reverse coded and averaged. Analyses We performed ?ve analyses, one describing users’ experience with the NHCPlus app and four testing the hypotheses stated above. We assessed users’ experience based on responses to the usability survey. We report the percent of patients who used NHCPlus alone, percent of family members who used it alone, and percent of patients and families who used it together. We also present the percent of users who report using NHCPlus a lot or very little and users’ agreement with several statements about their experience using NHCPlus. Because con?dence in the decision and satisfaction with the decision variables were calculated as averages of ?ve-item scale, we excluded subjects with three or more missing items. Three subjects were, therefore, deleted from the con?dence measure and four from the satisfaction measure. Testing the general hypothesis about discharge to better quality nursing homes is not feasible. Data show that the various dimensions of nursing home quality are not correlated, and, hence, there is no “one best nursing home.” Furthermore, because preferences for which quality dimensions matter differ across individuals (Mukamel et al. 2016), nursing homes’ quality can only be judged within each individual’s framework, which was the motivation for personalizing NHCPlus. Because of this inherent limitation, we address this question by testing two subhypotheses. The ?rst is motivated by the fact that about a third of patients have been shown to agree with the NHC ?ve-star expert ranking of nursing homes (Mukamel et al. 2016). These patients should increase the probability that the NHCPlus group on average would enter nursing homes ranked higher by the NHC ?ve-star measures. Discussion: Clinical Decision Systems Comparative We, therefore, test whether the intervention patients were more likely to be discharged to nursing homes with higher NHC overall ?ve-star measure and the three ?ve-star subcomponents—health inspections, the QMs, and staf?ng. For these tests, we accounted for the fact that nursing home choice is typically made within a small geographic area and that the nursing homes in that area may not all be of four- or ?ve-star ranking. We de?ned an indicator variable for each Personalizing Nursing Home Compare 2083 patient that assumed the value 1 if the patient was discharged to the nursing home with the highest rating among all nursing homes in their geographic area (i.e., their choice set) based on the ?ve-star composite, and 0 otherwise. The choice set for the NHCPlus group was de?ned by the users, as part of using the app. Because we did not have this information for the UDP group, we assumed their search zip code to be their zip code of residence, obtained from the medical record, and imputed their search radius based on the NHCPlus group, conditional on the zip code. The second subhypothesis is that patients in the intervention group will on average choose nursing homes that are further away from their residence. This is likely to be the case if patients using NHCPlus understand that there are quality differences between nursing homes and are able to identify those that are of higher quality and hence are willing to travel further in order to bene?t from the better quality they offer. Travel distance was measured using the geodetic distance between the patient residence zip code centroid reported in the medical record and the nursing home zip code. Eleven patients were excluded from this analysis because they were out of state residents or their travel distance exceeded 60 miles. Hospital length of stay (LOS) was de?ned as the difference between discharge and admission dates as recorded in the medical record. Because initial analyses showed differences between those who did not complete the exit survey (noncompleters) and those who did, we tested all hypotheses using regression models that included indicator variables for the NHCPlus group, noncompleters, and an interaction of the two. The intervention effect for noncompleters was calculated as the sum of the coef?cients for the intervention and the interaction terms. We also controlled for p …= Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10