Know how it works before you fix it: a data analysis strategy from an inpatient nephrology patient-flow improvement project.
(Care and treatment)
Hospitals (Admission and discharge)
Hospitals (Quality management)
Medical care (Quality management)
Medical care (Management)
|Author:||Holtby, Murray A.|
|Publication:||Name: CANNT Journal Publisher: Canadian Association of Nephrology Nurses & Technologists Audience: Trade Format: Magazine/Journal Subject: Health care industry Copyright: COPYRIGHT 2007 Canadian Association of Nephrology Nurses & Technologists ISSN: 1498-5136|
|Issue:||Date: Jan-March, 2007 Source Volume: 17 Source Issue: 1|
|Topic:||Event Code: 353 Product quality; 200 Management dynamics Computer Subject: Company business management|
|Geographic:||Geographic Scope: Canada Geographic Code: 1CANA Canada|
This article describes a two-part data analysis strategy that was used as part of a process improvement project on an inpatient renal unit. The goal of the project was to improve patient flow-through from admission to discharge. The Canadian Institute for Health Information reported that the proportion of incident end stage renal disease in the 65+ age group was on the rise, and that the fastest growing segment of incident dialysis patients was in the 75+ age group. Health professionals who provide hospital services to nephrology patients should be alert to this information and anticipate longer mean lengths of hospital stay and more frequent discharge delays. There has been no research in this area specific to inpatient renal units. The author shares his data analysis strategy in hopes that it will spur more research and other process improvement projects.
Key words: quality improvement, length of stay, aged, chronic kidney failure
The use of acute care beds by older people is gaining research attention as a demographic shift to an aging population occurs in industrialized countries around the world (Campbell, Seymour, Primrose, Lynch, Dunstan, Espallargues, et al., 2005, p. 84; Chin, Sahadevan, Tan, Ho, & Choo, 2001; Cooper, 1991; Murashima, Nagata, Toba, Ouchi, & Sagawa, 2000). Stakeholders have expressed concern about the economic and human costs of the disproportionate number of days seniors spend in hospital compared to the rest of the population. In a study conducted in Winnipeg, Canada, De Coster, Bruce and Kozyrskyj (2005) found that 5% of hospitalized patients accounted for 40% of acute care hospital days and that two-thirds of these patients were 75 years or older. The longer-than-average lengths of stay experienced by hospitalized seniors has created system access and flow problems and has resulted in the unfortunate practice of labelling older persons "bed blockers" (Styrborn & Thorslund, 1993).
Access and flow problems are not the only items of concern. Researchers have demonstrated that there is a positive correlation between hospital stays of greater than 90 days for people 65 years and older and the likelihood of hospital death or nursing home placement (Kozyrskyj, Black, Chateau, & Steinbach, 2005). This correlation may have many explanations, but prolonged exposure to iatrogenic risks and the tendency toward increasing functional and cognitive decline associated with length of hospital stay may have some direct explanatory power (Graf, 2006; Potts, Feinglass, Lefevere, Kadah, Branson, & Webster, 1993). Clearly, length of hospital stay for older patients is about more than economics, rationing, and access to care. Length of stay is also a quality-of-care issue. This is particularly true if extended lengths of stay are found to be related to discharge delays and inappropriate days in hospital that, in turn, can be attributed to system inefficiencies. Predictors of length of stay, inappropriate stay, complexity of discharge, and the discharge destinations of seniors admitted to hospital provide valuable information about system attributes amenable to change. To date, there has been no published study that assesses either the incidence or magnitude of these concerns in the population of older adults with end stage renal disease (ESRD) who are admitted to inpatient nephrology units.
Studies conducted to describe how renal patients flow through inpatient renal units would be timely considering both the aging of the population in general and the rising number of Canadians 65 years and older who are being treated for ESRD. The Canadian Institute for Health Information (CIHI) (2004a) reported an increase of 20% in persons being treated for ESRD in Canada over the five years 1997 to 2001. The CIHI (2004a) also reported that of the total number of new diagnoses of ESRD in Canada in 2001, 55% were made in persons 65 years of age or older. This represents an increase in the rate of new diagnoses of ESRD in persons 65 years and over from 51 per 100,000 Canadians in 1997 to 68 per 100,000 in 2001. Add to this the fact that the fastest growing segment of incident dialysis patients is the 75+ age group (Canadian Institute for Health Information, 2005) and there is reason to be concerned that so little is known about how older adults with ESRD negotiate their way through a hospital admission. The data analyses provided by the CIHI in the above reports indicate that health care providers working on inpatient nephrology units can anticipate a rise in the mean age of the patients for whom they provide care. The rise in mean age, if data for seniors in general hold true for seniors with ESRD, will be accompanied by a rise in mean length of hospital stay. Complex health and discharge needs as well as longer-than-average lengths of hospital stay have certainly been my experience in caring for patients who have ESRD. The CIHI (2004b) reported that the mean length of stay in a Canadian hospital over the years 2002-2003 was 7.4 days. The mean length of stay on the author's inpatient renal unit in the months leading up to the Process Improvement Project (PIP) with which this article is concerned was 18.8 days (N= 150; June 9 to October 1, 2003). Research conducted to understand flow patterns, predictors of discharge delays, and inappropriately extended hospital stays in older adults with ESRD may have the potential to save health care dollars and improve both quality of care and quality of life for these patients. Continuous quality improvement projects such as the PIP described in this article may also have an impact (Shortell, Bennett, & Byck, 1998).
The two-part data analysis strategy I describe in this article was part of my contribution to a larger PIP conducted on a hospital unit specialized in renal patient care. The goal of the project, as stated in the team charter, was "to improve patient flow-through on inpatient nephrology from admission to discharge home or to an alternate level of care." The purpose of this article is to describe and share the data analysis strategy employed in the interpretation of the baseline data collected for the inpatient renal PIP.
The nursing unit on which the PIP was conducted is part of a 777-bed tertiary care referral centre that is associated with a major university. The unit is divided into two sections that function as subunits, but which are jointly staffed and managed. One section is used primarily by the transplant program. Patients admitted under the transplant service were not included in the PIP. The other section has 32 beds and is used to care for patients admitted under the renal medicine service. The patients admitted under renal medicine and into the 32-bed subunit were the focus of the PIP. Demographic data were not collected for the PIP, but the staff observation that gave rise to the PIP was that the 32-bed section generally provides services to older adults with ESRD who have complex discharge needs. The impetus for entering a renal team into the regional patient flow collaborative was a perceived problem: unit staff felt they often encountered difficulties in moving these complex patients from admission to discharge in an efficient manner.
The RCSI used the methodological framework created by the Institute for Health Care Improvement (IHI, see www.ihi.org). The quality improvement model adopted by the IHI is called The Model for Improvement. The Model for Improvement consists of three questions: 1. What are we trying to accomplish, 2. How will we know that a change is an improvement, 3. What changes can we make that will result in improvement, and a Plan-Do-Study-Act (PDSA) cycle (Institute for Healthcare Improvement, 2006). Change ideas are tried on a small scale by using the PDSA cycle. Changes are then rejected, refined and recycled, tried on a larger scale, or implemented depending on how successful they have been.
This PIP was conducted as part of a larger RCSI sanction by the CHR. The collaboratives were exempt from ethical review based on their status as quality improvement initiatives using formal guidelines established by the CHR. Patient confidentiality was guarded by stripping identifying information from patient data and by presenting data in aggregate form only.
A two-part measurement strategy
Part one: The flowchart
You cannot improve something, at least not in an intentional and systematic way, if you do not know how it works. Flowcharting is one strategy that is frequently employed in PIPs to help change-agents get a handle on what is happening.
A flowchart helps to clarify how things are currently working and how they could be improved. This tool also assists in finding the key elements of a process, while drawing clear lines between where one process ends and the next one starts. Developing a flowchart establishes communication and common understanding about the process. In addition, flowcharts are used to identify appropriate team members, to identify who provides inputs or resources to whom, to establish important areas for monitoring or data collection, to identify areas for improvement or increased efficiency, and to generate hypotheses about causes. Flowcharts can be used to examine processes for the flow of patients, information flow, flow of materials, clinical care processes, or combinations of these processes (Miller Franco, Newman, Murphy, & Mariani, 1997, p. 84).
The flowchart created for our PIP (see Figure One) represents several possible pathways by which patients have made their way from admission to discharge on our inpatient renal unit. I used a program called OmniGraffle[R] to generate the chart. Visio[R] provides similar capabilities if you are not using a Mac[R] computer. The flowchart was created in a two-step process.
The first step involved a team brainstorming session from which a draft flowchart emerged. Information about the staff experience of the admission-discharge process, as well as the conceptual framework that supposedly guides the process, were captured in these data. For instance, these data provided the logic of the four stages encountered in the admission-discharge process and captured the importance of the concept of functionally and medically stable for discharge (FMSD) (see Figure One). The idea of discharge readiness that is captured in the concept of being FMSD is a necessary component of the discharge process, but it did not arise naturally from chart data: in fact, it was conspicuous by its absence. This important step may have been presumed nonexistent from a strict reading of chart data alone. What we discovered was that a determination of FMSD was most often achieved through verbal consensus at impromptu meetings of renal team members. This process is captured on the flowchart (see Figure One) at the junction of stages two and three by a bi-directional arrow between a documented and an informal FMSD decision. Patients may progress toward discharge by either or both of these processes.
[FIGURE 1 OMITTED]
The second step involved amending the draft flowchart based on patient chart data. The data were collected from a sample of 20 patient charts selected from a list of 150 patients that had been discharged between June 9 and October 1, 2003. I used a purposive sampling method to ensure that the data were rich in terms of the number of unique admission-discharge paths described. Paths terminating in deaths and discharges against medical advice were not of interest to this PIP and were not included. I also intentionally excluded data from patients who were not directly admitted to and discharged from our unit, a choice that, by definition, also excluded patients who may have gone to and returned from other hospital units, such as intensive care, before being discharged. Individual flowcharts were created from data for each of the 20 patient charts. Data saturation was judged to have been substantially obtained by chart number 20 and data collection was discontinued at this time. The final version of the unit flowchart incorporated all of the flow possibilities found in the individual flowcharts.
[FIGURE 2 OMITTED]
The flowchart is composed of one direct and several indirect flow pathways. The shapes running down the centre of the chart and connected by the wide arrows represent the most direct path through the unit: the one idealized by the flow team. A patient following this path presents with a health concern requiring admission to the hospital, receives the appropriate referrals and treatment, recovers, is declared FMSD--on a document accessible to the entire health team--is apprised of a discharge decision and, if the decision is acceptable to patient and family, the patient is discharged home or transferred to the next appropriate level of care. The shapes connected by thin arrows represent indirect flow possibilities. These possibilities include new or recurrent health issues requiring further assessment and treatment, conflict or resistance to the possibility of discharge from patient or family related to a variety of highly individual needs and wants, the late identification of discharge issues, and waiting for placement. If a patient is FMSD, and the reasons for a patient taking an indirect path are not resolved, the patient enters a maintenance mode. A final item added to the flowchart--the dark line running from top to bottom and broken into four numbered segments--represents the four phases of the admission to discharge flow process.
Part two: The spreadsheet
The more novel aspect of the measurement strategy employed in our renal PIP was the Excel[R] spreadsheet I designed (see Figure Two) to operationalize and quantify definitions of the flow concepts used in the flowchart in Figure One. The spreadsheet contains embedded formulas that process raw chart data entered in the first half of the spreadsheet into a meaningful summary that appears in the second half of the spreadsheet. The spreadsheet is designed to calculate several things but, most importantly, it is designed to calculate the number of days of delay that can be attributed to each of six manageable delay factors (see bottom of Figure Two). I have called these factors manageable delays because, in theory at least, each of these factors could be the target of a change strategy to improve patient flow. The spreadsheet automatically calculates the number of days of delay for each of the six delay categories, highlights in red the most significant factor in terms of days of delay, and calculates a single summary variable titled total nonproductive days. I conclude this section by taking a detailed look at each measure in the spreadsheet. Item descriptors are printed in italics to facilitate quick reference and comparison with Figure Two. Please refer to Figure Two for the remainder of the discussion in this section.
For anyone who is interested in using this spreadsheet, I have provided an unlocked copy at http://homepage.mac.com/quipplem/FileSharing9.html. Use the password CANNT06. The password is case sensitive and you may need to enter it twice. You may also e-mail me and I will send it to you. The spreadsheet has undergone no further testing than has been described above. Users must therefore accept responsibility for the tentative nature of a work in progress, and be willing to assess whether her/his data have been processed in a legitimate fashion by the spreadsheet. To make a computer-age analogy, I am offering the spreadsheet as open-source, beta, freeware to anyone who would like to use it and further its development.
The first three data entry cells (B4:B6) are fairly straightforward. Admit stands for the admission date. The value to enter in B4 is simply the date of admission. D/C Decision stands for the discharge decision date. The value to enter in B5 is the date the physician documented as the planned discharge date. If the physician did not document a planned discharge date, enter the date of actual discharge. If several tentative and failed discharge plans were made, enter the date of the last decision. The value of this date must be no earlier than the greater of the dates entered in C25 and C26. Discharge means the date of discharge. Enter the date of discharge in B6.
Understanding what data to enter into cells F3:F6 is less straightforward. Str R TX DT stands for the start of resistance to treatment and/or decision time. Enter the date at which patient and team began to disagree about treatment and could not come to an agreement about a new course of action in F3. Also, use this cell to enter the date when a patient needed to make a treatment decision, but required time to think about the decision. This date, along with Res R TX DT (resolution of resistance to treatment and/or decision time), is used to calculate a portion of the number of nonproductive hospital days during the treatment phase (pre-FMSD). Res R TX DT stands for resolution of resistance to treatment and/or decision time. Enter the date the patient and team came to an agreement about a new course of action or the date at which the patient made a treatment decision in F4.
Res Late ID stands for the resolution of late identification. This means the end-date of resistance to discharge resulting from the late identification of a discharge issue that was part of the patient profile on admission or that could have been anticipated. The first criterion for a date entered in F5 is that it must be later than the date when the patient was declared FMSD. The date at which a patient is considered FMSD is the date entered in C26 or C25 if C26 is blank. The second criterion for a date entered in F5 is that the reason for the resistance must be the result of a failure to identify a discharge issue until a discharge attempt is made.
Res Block stands for the resolution of patient or family blocks to discharge. The first criterion for a date entered in F6 is that it must be later than the date when the patient was declared FMSD. The date at which a patient is considered FMSD is the date entered in C26, or C25 if C26 is blank. The second criterion is that the patient or family must be blocking a discharge for reasons other than those already covered above.
The Problem ID section (rows eight to 22) is concerned with consultations and how quickly they are completed. There are several categories of potential consults: physical therapy (PT), occupational therapy (OT), registered dietitian (RD), social work (SW), and transition services (TS). The first MD (medical doctor) line (row 15) is reserved for the admitting physician. Data entered on this row in B15 are not considered in relation to the discharge planning start time in C29, the rationale being that a patient must be seen by a physician to be admitted. The date in B15 is the date that the attending physician first completes a chart entry. The information in C15 is, however, part of the calculation in C35. The MD completed date in C15 is the date on which the physician documents a complete admission assessment. Cells B15 and C15 should frequently have the same value. The Initiated dates (B10:B15) are the dates when a discipline first becomes involved. The Completed dates (C10:C15) are the dates when a discipline completes an initial assessment. The criterion for an initiation is a documented start date on the chart. The criterion for a completion is the documented identification of all admission concerns pertinent to that discipline. Days from admission to initiation and admission to completion are calculated and provided on the spreadsheet in D10:D15 and E10:E15 respectively. The cells F10:F15 will accept a value of either zero or one. A value of zero generates a yes meaning that a discipline documented when a patient had reached the status of FMSD from the perspective of problem list generated by that discipline. A value of one generates a no and signifies that the discipline did not document FMSD. Any of the cells in the Problem ID section, with the exception of the attending physician's cells, may be left empty. An empty consult cell will generate a value of N/A (see D14 for an example). N/A values are excluded as terms in calculations.
Additionally, there are six rows (16 to 21) for other physician consults. The MD CON (medical doctor consult) cells are used to calculate the AVG. CONSULT TIME (average consult time) in F22. An important difference to data entry for these cells is that initiated refers to the date that the consult was placed or requested and not to the date the consulting physician arrives to start the consultation process. The completed date is the date on which the consult is completed and documented.
The END POINTS section (rows 24 to 25) is used to enter data that indicate treatment completion dates. Admit Problem (admission problem, C24) means the date the physician documents that a patient is FMSD from the perspective of the admitting problem. New/Recurrent Problem(s) refers to a documented date when a patient is declared FMSD from the perspective of any new or recurrent problem(s) encountered during the admission. The criterion used to define a new problem is that the problem occurs following admission, i.e. the problem must not have been a concern on admission. The criteria used to define a recurrent problem are that the problem must have been present on admission, the patient must have been considered FMSD with respect to this problem during the admission and, subsequent to the declaration of FMSD, the problem must have recurred. The value placed in the New/Recurrent Problem(s) (cell C25) must be the date of the resolution of the last new or recurrent problem. The remaining cells in the spreadsheet fall under data summary (rows 26 to 48). The values in these cells are calculated automatically.
The cells in rows 28 through 32 provide some basic information about the admission. The value for D/C Planning Start [less than or equal to] 48h? (was discharge planning initiated within 48 hours?) is generated from data in cells E10:E15. A value of yes will be returned if all the values in E10:E15 are less than or equal to 48 hours, otherwise the cell will return a value of no. The value for D/C (FMSD) Document? (did all involved disciplines document FMSD?) is generated from data in cells F10:F15. A value of yes is returned if all of the disciplines documented FMSD, otherwise the cell returns a value of no. Cells C31 and C32 return values indicating length of admission and maintenance respectively. An additional cell in this section, cell D32, provides a value of the percentage of days a patient spends in maintenance mode with respect to the total stay. A patient is considered to have entered a maintenance state when he/she is not receiving treatment for either the admitting problem or new and recurrent problems. The criteria for maintenance are operationalized in cells D42 and D45:D48. Maintenance days (C32) and Total Non-productive Days (E46) are synonymous.
The cells in the Patient Flow section (rows 34 to 39) correspond to the dark line running from top to bottom and broken into four numbered segments on the flowchart in Figure One. These cells return the number of days and percentage of days that a patient spends in each of the four admission-discharge stages. Cell C35 returns a value for Admit to Comp. D/C Plan (admission to completion of the discharge plan). This value is the return of the greatest value in cells E10:E15. Cell C36 returns a value for D/C plan to FMSD (completion of the discharge plan to functionally and medically stable for discharge). This value is the return of the greater of the values in cells C24 and C25 minus the greatest of the values in cells E10:E15. Cell C37 returns a value for FMSD to Decision D/C (functionally and medically stable for discharge to a discharge decision). This value is the return of the calculation of the lesser of C25 minus B5 and C26 minus B5. Cell C38 returns a value for D/C Decision to Discharge (discharge decision to discharge). This value is the return of the calculation of B6 minus B5.
The cells in the Delays section (rows 40 to 48) calculate the number of days that can be attributed to six different delay factors: two of these factors contribute to an extended stay pre-FMSD and four post. The factor New/Recurrent Problem(s) does not contribute to Total Non-productive Days. New and recurrent problems require productive treatment days to move a patient toward being FMSD. All other delay factors contribute to Total Non-productive Days in sequence such that days are not accumulated by a factor further down the list until delays that can be attributed to factors preceding it have been resolved. This sequencing ensures that days accumulated by any one factor can be attributed to that factor alone. By sequencing delay factors in the order in which they will logically be encountered, and by preventing overlap, it becomes possible to isolate the most significant delay factor, which the spreadsheet then highlights in red. Cell D42 returns a value for Res TX (resistance to treatment). This value is the return of the calculation F4 minus F3. Cell D43 returns a value for New/Recurrent Problem(s). This value is the return of the calculation of C25 minus C24. Cell D45 returns a value for Late ID (late identification of discharge concerns). This value is the return of the calculation of F5 minus B5. Cell D46 returns a value for Committing to a D/C Decision (committing to a discharge decision). This value is the return of the calculation of B6 minus B5. Cell D47 returns a value for Blocked D/C (Conflict) (blocked discharge as a result of conflict). This value is the return of the calculation F6 minus B5 minus D45. This calculation corresponds to the conflict path and to that segment of the resistance path titled personal issues in Figure One. Finally, cell D48 returns a value for Disposition (the amount of time spent waiting post-FMSD until discharge home or to the next level of appropriate care). This value is the return of the lesser of (B6 minus C24) and (B6 minus C25) minus (D45 plus D46 plus D47).
The flowchart and the flow spreadsheet were helpful in setting the stage for a productive PIP. This two-part measurement strategy was a strong complement to IHI methodology. The first part of the measurement strategy, flowcharting, is a well-established component of process improvement projects. We found the work involved in creating a flowchart paid off in terms of the clarity it brought to taken-for-granted aspects of the unit flow process. We learned, for instance, that our evaluation of discharge readiness is primarily verbal.
The second part of the measurement process, the spreadsheet, was designed specifically for this PIP. The process of translating concepts used in the flowchart into mathematical formulas that could be entered into a spreadsheet had several benefits. First, translating a concept into a formula requires precision. Fuzzy concepts took on greater clarity as they were refined by the exacting demands of mathematics. This, in turn, led to improvements in the flowchart. Second, the spreadsheet provides a means of quantifying and analyzing data that the flowchart does not. The spreadsheet makes it possible to assign numbers to each stage of the flow process. We learned, for instance, that we needed to pay more attention to proximal aspects of the flow process on our unit. Third, the spreadsheet provides a standardized method of data collection and analysis. The spreadsheet, in this way, becomes a useful tool for measuring the results of a change in process with respect to its intended target.
Both the flowchart and the spreadsheet worked well for the analysis of baseline data in our small sample of 20 patients. The spreadsheet, however, has not had the benefit of rigorous testing. The tool appears to have at least face validity. Measured nonproductive days did appear to match descriptions of the cases being measured. Other forms of validity have yet to be established.
On a practical level, I found the spreadsheet provided a useful framework for extracting chart data. Data extraction took between 15 and 20 minutes per chart. The major challenge was deciding how to work with missing data, which, in general, was restricted to three areas: discharge decision dates, FMSD documentation, and precise dates for new or recurrent problems. Missing data are always a problem when a researcher takes prepared questions to existing data. Anyone who uses this tool will need to decide in advance how to deal with this problem in a consistent manner. In addition, if missing data are a persistent issue with regard to certain items, anyone involved with future iterations of this tool will need to assess whether these items should be removed or replaced to enhance construct validity.
The construct total nonproductive days is perhaps in and of itself controversial. How does one decide what is nonproductive? I have included patient decision-making days, for instance, in the tally of nonproductive days. The inclusion of these days seemed important in our circumstances because it was not hard to recall patients for whom days or weeks passed before arriving at a treatment decision. Careful consideration needs to be given as to how this construct will be defined in future projects. More research into the nature of the hospital experience for older patients with ESRD would be helpful here. I submit that the construct deserves serious consideration as the outcome measure in future research. An argument of this case would need to be the subject of another paper, but total nonproductive days captures the totality of the flow process that the construct discharge delay does not. Most extant research on delay issues uses the latter construct (refer to the authors in the introduction of this article). A flow orientation to hospital discharge delay issues should prove to be a more comprehensive approach.
To repeat what was stated earlier: you cannot improve something, at least not in an intentional and systematic way, if you do not know how it works. The value for our team in committing to the work involved in generating a flowchart and a flow spreadsheet was that we were able to target change strategies to the segment of the flow process with the greatest potential for improvement. Knowing how it worked set us on the right track to improvement and set us up for success. Our team went on to test various strategies for improving unit flow based on the baseline data I had collected using the spreadsheet and methods described above. A report on the outcomes of the test cycles over the one-year period our team participated in the regional collaborative will have to wait for a team-authored paper.
The author would like to acknowledge the dedicated members of Team Renal who contributed to this project in other ways: Marilyn Visser, Debbie Meilleur, Fatima Keshavjee-Johnson, Colleen McShea, Norma Fellows, Nairne Scott-Douglas, Jennifer Betts, Cheryl Ward, Cathy Molloy and Shauna Mattson. The author would also like to express his gratitude to his doctoral supervisor, Dr. Sandi Hirst, for her feedback on the first draft of this paper, to the CANNT reviewers for their invaluable comments, and to Kris Brown for providing help with the technical aspects of the spreadsheet.
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By Murray A. Holtby, RN, BTh, BScN, CNeph(C)
Murray A. Holtby, RN, BTh, BScN, CNeph(C), is a PhD
student at the University of Calgary.
Address correspondence to Murray A. Holtby at: email@example.com
Submitted for publication: July 18, 2006.
Accepted for publication in revised form: January 15, 2007.
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