Published on in Vol 17, No 3 (2015): March

Information Presentation Features and Comprehensibility of Hospital Report Cards: Design Analysis and Online Survey Among Users

Information Presentation Features and Comprehensibility of Hospital Report Cards: Design Analysis and Online Survey Among Users

Information Presentation Features and Comprehensibility of Hospital Report Cards: Design Analysis and Online Survey Among Users

Original Paper

1Department of Information and Communication, Faculty for Media, Information and Design, University of Applied Sciences and Arts Hannover, Hannover, Germany

2Institute of Management (IFM), School of Business and Economics, Friedrich-Alexander-University Erlangen-Nuremberg, Nuremberg, Germany

3Faculty of Media, University of Applied Sciences FHM, Bielefeld, Germany

*all authors contributed equally

Corresponding Author:

Martin Emmert, MSc, PhD

Institute of Management (IFM)

School of Business and Economics

Friedrich-Alexander-University Erlangen-Nuremberg

Lange Gasse 20

Nuremberg, 90403

Germany

Phone: 49 911 5302 ext 253

Fax:49 911 5302 114

Email: Martin.Emmert@fau.de


Background: Improving the transparency of information about the quality of health care providers is one way to improve health care quality. It is assumed that Internet information steers patients toward better-performing health care providers and will motivate providers to improve quality. However, the effect of public reporting on hospital quality is still small. One of the reasons is that users find it difficult to understand the formats in which information is presented.

Objective: We analyzed the presentation of risk-adjusted mortality rate (RAMR) for coronary angiography in the 10 most commonly used German public report cards to analyze the impact of information presentation features on their comprehensibility. We wanted to determine which information presentation features were utilized, were preferred by users, led to better comprehension, and had similar effects to those reported in evidence-based recommendations described in the literature.

Methods: The study consisted of 5 steps: (1) identification of best-practice evidence about the presentation of information on hospital report cards; (2) selection of a single risk-adjusted quality indicator; (3) selection of a sample of designs adopted by German public report cards; (4) identification of the information presentation elements used in public reporting initiatives in Germany; and (5) an online panel completed an online questionnaire that was conducted to determine if respondents were able to identify the hospital with the lowest RAMR and if respondents’ hospital choices were associated with particular information design elements.

Results: Evidence-based recommendations were made relating to the following information presentation features relevant to report cards: evaluative table with symbols, tables without symbols, bar charts, bar charts without symbols, bar charts with symbols, symbols, evaluative word labels, highlighting, order of providers, high values to indicate good performance, explicit statements of whether high or low values indicate good performance, and incomplete data (“N/A” as a value). When investigating the RAMR in a sample of 10 hospitals’ report cards, 7 of these information presentation features were identified. Of these, 5 information presentation features improved comprehensibility in a manner reported previously in literature.

Conclusions: To our knowledge, this is the first study to systematically analyze the most commonly used public reporting card designs used in Germany. Best-practice evidence identified in international literature was in agreement with 5 findings about German report card designs: (1) avoid tables without symbols, (2) include bar charts with symbols, (3) state explicitly whether high or low values indicate good performance or provide a “good quality” range, (4) avoid incomplete data (N/A given as a value), and (5) rank hospitals by performance. However, these findings are preliminary and should be subject of further evaluation. The implementation of 4 of these recommendations should not present insurmountable obstacles. However, ranking hospitals by performance may present substantial difficulties.

J Med Internet Res 2015;17(3):e68

doi:10.2196/jmir.3414

Keywords



Background

In recent years, many health care systems have implemented strategies to improve the quality of care [1]; nevertheless, quality deficits and variability still remain [1-6]. In general, patients are unlikely to be aware of the existence of quality differences [7,8]. One reason for this is the limited amount of publicly available information on the quality of health care providers [9]. Therefore, improving the transparency of information about health care provider quality is a major challenge [10]. It is assumed that this will improve overall quality by steering patients toward better-performing health care providers [11,12] and by incentivizing providers to improve quality [8,9,13-15].

Public reporting in Germany is partly regulated by law. Since 2005, German hospitals have had to publish quality reports online to help patients and physicians make informed choices of hospitals. The AQUA Institute has been commissioned to make further improvements in quality assurance [16]. In 2011, 1666 hospitals had to participate in an “external quality assurance” process. Since the beginning of 2012, the quality reports of individual hospitals include 182 of 430 quality measures [16]. A possible quality shortfall at a hospital can trigger an evaluation, including a structured quality dialog, which allows a group of experts to conduct a qualitative investigation of discrepant results at individual hospitals. In 2010, a total of 21,053 discrepant results were identified in 4,064,320 datasets. Of these, 8.0% were evaluated as qualitatively discrepant through the structured quality dialog. The AQUA Institute does not report on individual hospitals, but German public reporting portals draw on the information provided in the quality reports of individual hospitals.

Nevertheless, there are several barriers to effective public reporting in Germany. Friedemann et al [17] analyzed quality reports of individual German hospitals and concluded that they were neither readable nor understandable for most patients. In international studies, 1 of the barriers most frequently discussed is that consumers do not understand the formats in which the information is presented. Hibbard [18] noted that we need to find more effective ways to present data to consumers. Similarly, Sinaiko et al [19] argued that the current report cards need to be substantially expanded and refined. Kullgren and Werner [20] added that the problem with current public reporting is partly caused by the limitations imposed by the design of report cards.

Although the number of public reporting websites is likely to continue to rise [21], many argue that in their current state they might confuse consumers. Rothberg and colleagues [22] even argued that it would be better to report nothing at all rather than misleading information. Similarly, Emmert et al [23] stated that patients or physicians should not yet use such information to choose an individual physician. In the rush to make providers accountable, enthusiasm has often outstripped science [22]. Several researchers have pointed to the tremendous diversity in the presentation of quality data [21]. The websites and related reports vary widely in terms of ease of access, ease of use, usefulness of information, timeliness of updates, and credibility [24].

Research Questions

After reviewing the available international recommendations, we analyzed the 10 most commonly used German public report cards and addressed 3 questions:

  1. What information presentation elements were utilized?
  2. Which led to better comprehension?
  3. Which had similar effects to those reported in the evidence-based recommendations described in literature?

We focused on elements of information design that are used to communicate ideas, illustrate information, or express relationships visually, such as pictures, symbols, and colors [25].


Overview

The study consisted of 5 steps: (1) identification of best-practice evidence about the presentation of information on hospital report cards, (2) selection of a single risk-adjusted quality indicator for the study, (3) selection of a sample of the public report card designs used by German hospitals, (4) identification of information presentation elements used in public reporting initiatives in Germany, and (5) conduct of an online-based survey.

Identification of Best-Practice Evidence About the Presentation of Information on Hospital Report Cards

A literature search was conducted in April 2013 by searching the Medline (via PubMed) and Cochrane Library databases using the “Abstract/Title/Keywords” search field: Search (“Public Report$ OR Publicly Report$ OR Publicly Release$ OR Public Disclos$ OR Information Disseminat$ OR Report Card$ OR Consumer Report$ OR Quality Report$ OR Comparative Report$ OR Reporting Instrument$”); limitations: English, German, and Spanish; published in the last 10 years; field: “Title/Abstract/Keywords.” Only peer-reviewed journal articles and 3 types of review were included: (1) studies which compared health care report cards in terms of specific criteria, (2) studies which theoretically or empirically assessed or discussed the distinguishing features of public report cards and provided evidence on how performance data should be published or presented, and (3) studies which summarized or discussed best practice in public reporting for health care.

Selection of a Single Risk-Adjusted Quality Indicator for the Study

Several risk-adjusted outcome quality measures were available from the German Hospital Quality Report 2011 [17]. The selection of the quality indicator for the study was done by assessing case numbers, the number of hospitals using the measure, and its role in quality assurance. We selected an elective procedure because publicly available information on elective treatments might be expected to help patients identify a good health care provider. We selected a risk-adjusted mortality rate (RAMR) measure because these are considered useful indicators of hospital quality [26-28].

Selection of a Sample of the Public Report Card Designs Used by German Hospitals

To identify relevant hospital websites, we used a Google search by searching on several keywords (eg, Kliniksuche [clinic search], Krankenhaussuche [hospital search], Gute Klinik [good clinic], Klinikvergleich [compare clinic]). The most frequently visited hospital rating websites were identified using the Alexa analysis tool.

Identification of Information Presentation Elements Used in Public Reporting Initiatives in Germany

The presentations of the selected quality indicators in 10 report cards were captured with screenshots that included interactive features (mouseovers). Information presentation elements were categorized with nVivo 10 by 2 authors using the previously identified literature-derived categories. Additional categories were added when no literature-derived categories were available.

Conduct of an Online-Based Survey

We applied an online-based cross-sectional study by surveying an online panel to address the following questions:

  1. Were respondents able to identify the hospital with the lowest RAMR?
  2. Were respondents’ hospital choices associated with particular information design elements?

Consultation took place with an online panel in Germany in August-September 2013; each participant received €1 per finished survey. The panelists were recruited through several recruitment channels including online recruitment, direct mailing, and offline recruitment. The panel members were invited by email to participate (the invitation contained a link to the online survey). The survey was administered and conducted by Norstat Germany Ltd (formerly ODC Services Ltd), a fieldwork agency for survey research.

The online questionnaire consisted of 3 parts. First, a short introduction described the background and study objectives. Second, a random selection of 3 of the 10 hospital report cards was presented to prevent training effects (eg, overfamiliarization with presentation techniques leading to biased results). Respondents were asked to select the best quality hospital, to justify their choice in response to an open-ended question, and to assess the comprehensibility of the website (range of 1=very comprehensible to 7=very incomprehensible). Third, respondents provided sociodemographic data. The questionnaire was piloted and descriptive analyses were conducted using SPSS version 21.0 (IBM Corp, Armonk, NY, USA). The statistical significance of differences between responses was calculated using chi-square tests and t tests. Analysis of the open-ended questions was conducted by 2 authors coding and categorizing answers independently using nVivo 10; discrepancies were discussed to achieve a consensus.


Identification of Best-Practice Evidence About the Presentation of Information on Hospital Report Cards

Our search yielded 2506 hits in the Cochrane Library and 1827 in PubMed. After elimination of duplicates, 4302 articles were screened by title and abstract, resulting in exclusion of 4018 articles. In addition, 7 studies were identified through reference search, expert consultation, and Internet searches, giving 291 articles for full-text review. Of these, 13 articles published between 2001 and 2013 met the inclusion criteria. Ten articles described observations in the United States, 2 in the Netherlands, and 1 in Germany.

Table 1 shows the literature-derived categories for the presentation of information on health care report cards. Because this study focused on outcome quality measures, only the information presented on these measures is described here. The methodological quality of the literature used is described in Multimedia Appendix 1.

Table 1. Features of the presentation of information on health care report cards from previous studies.
CategoryRecommendations and results
Evaluative table with symbolsConsider using a table design such as the “evaluative table with stars” rather than a bar chart [29]

Evaluative tables using words or stars are superior to numerical tables [29]

Physicians preferred formats that used traffic light symbols to code the value of indicators (numerical table with traffic lights) [30]
Tables without symbolsGraphic displays were more helpful to users than text-only tables [31]
Bar chartsBar charts were commonly used (43% of public reporting websites) [32]
Bar charts without symbolsComprehension was lowest when data were presented in bar charts [30]

Standard bar charts were not well-liked by respondents and led to the lowest levels of comprehension [29]
Bar charts with symbolsSymbols and bar charts should be used [31]

A combination of bar charts and star ratings facilitated correct interpretation by users [32]

Adding stars to bar charts increases comprehension significantly [33]
SymbolsParticipants liked to use symbols to identify the best surgeon [31]

Physicians preferred formats that used symbols (eg, traffic lights) [30]

Star-only formats should be used in preference to numerical values [34]

Only important information should be made easier to evaluate using symbols [35]
Evaluative word labelsAdding evaluative labels to bar charts did not increase comprehension [33]
HighlightingColor-coding important information improves comprehension [36]

Highlighting information about quality resulted in greater understanding [37]

Presentation formats which highlighted key messages increased comprehension [38]
Order of providersPhysicians prefer presentation formats that combine individual indicator values with evaluative features such as rankings [30]

Comprehension of respondents who were low in numeracy was significantly improved by the ordered compared to the unordered condition [35]

Providers should be ranked by performance [12]

Ranking plans by performance significantly decreased errors in interpreting data [33]

Ranking by performance increased the frequency with which users chose higher-performing services [15]

Providers should be ranked in descending order of quality, as this was valued by participants and increased their comprehension [36]

One of the more powerful display strategies is to rank providers in terms of performance [33]

When providers were ordered alphabetically participants were more likely to make effective use of the data (ie, choose the best provider) than when providers were ordered by performance [32]
High values indicate good performancePerformance data should be displayed such that high values always represent high performance [35]

Numeric tables and bar charts often led respondents to conclude that the worst performing nursing homes (those with the higher percentages) were the best, notwithstanding the warning label at the top [29]
State explicitly whether high or low values indicate good performanceIt should be stated explicitly whether high or low values indicate good performance, regardless of the direction of the scale [8,36]
Incomplete data (“N/A” as a value)Incomplete data (missing values) have a negative influence on provider assessment and the potential to influence a decision [29]

Selection of a Single Risk-Adjusted Quality Indicator for the Study

For our investigation, we selected the risk-adjusted quality indicator coronary angiography and percutaneous coronary intervention (PCI). This procedure was performed 715,469 times in 841 German hospitals in 2011. In 2011, 6369 of 276,866 (2.30%) patients died after PCI; 2.24% mortality had been expected, resulting in a RAMR of 1.03 [17].

Selection of a Sample of the Public Report Card Designs Used by German Hospitals

A total of 63 hospital public reporting websites were identified by a Google search. Several report cards were eliminated because they did not present outcome quality measures or used presentation formats identical to those of sites already included in the sample. Of the remaining report cards, the 10 most frequently visited were used as a sample (Multimedia Appendix 2): Portal A [39], B [40], C [41], D [42], E [43], F [44], G [45], H [46], I [47], and K [48].

Identification of Information Presentation Elements Used in Public Reporting Initiatives in Germany

The formats used to present information about RAMR in coronary catheterization by the 10 public reporting websites (see Multimedia Appendix 2) that we studied are summarized in Table 2. Tables (5 sites) and bar charts (5 sites) were equally popular; 4 sites presented reports with incomplete or missing data (“N/A” as a value). Symbols such as traffic lights were commonly used (7 sites), sometimes in combination with bar charts (4 sites) or tables (5 sites) (see Figure 1). Five report cards used low values (for the mortality rate) to indicate good performance and we identified 2 report cards which indicated a “good quality” range for the RAMR.

Table 2. Features used in the presentation of risk-adjusted mortality rates for coronary catheterization by 10 German portals.
Elements of information presentationnPortals
Table with symbols3B, I, K
Table without symbols2C, E,
Bar chart without symbols1G
Bar chart with symbols4A, D, F, H
Bar chart with traffic light symbols3A, D, H
Bar chart with thumb symbols1F
Symbols only0
Evaluative word labels0
Highlighting0
Providers ranked by performance2D, H
High values indicating good performance0
Explicit statement about whether high or low values indicate good performance5A, D, G, H, I
No statement about scale direction, but a “good quality” range identified2A, H
Incomplete data (“N/A” as a value)4B, C, F, K
Figure 1. Tables without symbols. Top: Portal E; bottom: Portal G. Only results for hospital 1 are displayed. English translations in brackets.
View this figure

Conduct of an Online-Based Survey

In total, 3064 respondents started the online survey and 2027 completed it (completion rate=66.16%), taking a mean 14.51 (SD 9.39) minutes. The overall mean age of respondents was 41.57 (SD 15.87) years; 978 of 2027 respondents were female (48.24%) and 50.71% (1028/2027) graduated from high school or obtained a technical university entrance qualification (see Table 3). A total of 96.69% (1960/2027) used the Internet at least once a day. Table 3 also shows the results of the Arbeitsgemeinschaft Online Forschung (AGOF) Internet Facts 2014-07 survey of the German population who used the Internet in the past 3 months [49]. Comparing our survey results to those of the AGOF, the strongest difference appears to be in the demographics of the 2 surveys, specifically in the rate of persons without school qualifications or with secondary general school. In our survey, this rate was 11.69% (237/2027) compared to 35.2% in the AGOF survey. Differences in age, gender, and household size were weaker.

Table 3. Overview of the study sample in comparison with Internet users in Germany.a
DemographicsStudy sample (N=2027)Internet users in Germany (N=106,677)
Age (years)


Mean (SD)41.57 (15.87)

Range, n (%)



≤20255 (12.58%)(13.6%)


21-30334 (16.47%)(17.1%)


31-40355 (17.51%)(16.3%)


41-50484 (23.87%)(20.6%)


51-60328 (16.18%)(16.8%)


≥61270 (13.32%)(15.5%)
Gender (female), n (%)978 (48.24%)(47.5%)
Household size, n (%)


1 person456 (22.50%)(16.8%)

2 persons725 (35.76)(33.7%)

3 persons and more845 (41.69%)(49.5%)
Education, n (%)


Still at school67 (3.31%)(4.7%)

Without school qualification or secondary general school237 (11.69%)(35.2%)

Intermediate secondary school or equivalent qualification694 (34.24%)(30.6%)

High school graduation/technical university entrance qualification1028 (50.72%)(34.2%)

a As measured by the Arbeitsgemeinschaft Online Forschung (AGOF) Internet Facts 2014-07 survey of the German population who used the Internet in the last past 3 months [49].

Were Respondents Able to Identify the Hospital With the Lowest Risk-Adjusted Mortality Rate?

Table 4 shows the results of the survey. Three of the 10 report cards were presented to each respondent (N=2027) for a total of 6081 observations. For each site, a mean 60.68% (1230/2027) of respondents successfully identified the hospital with the lowest RAMR, and 6.81% (138/2027) (range 0.8%-14.0%) selected the hospital with the highest RAMR. In approximately 14.60% (296/2027) of all observations, respondents stated they were unable to provide an answer. Only 32.02% (649/2027) of respondents selected the hospital with the lowest RAMR on all 3 randomly assigned report cards, whereas 14.01% (284/2027) did not identify the hospital with the lowest RAMR on any report card.

Table 4. Respondents were asked to select the best quality hospital: overview of the selected hospitals (N=6081 observations).
Portal usedWhich hospitals did the respondents select?, n (%)

Hospital 1Hospital 2Hospital 3Hospital 4Hospital 5Could not answer
Portal A123 (2.02)4695 (77.21)a282 (4.64)208 (3.42)195 (3.21)b581 (9.55)
Portal B224 (3.68)b2238 (36.8)480 (7.89)144 (2.37)1909 (31.39)a1088 (17.89)
Portal C766 (12.60)b341 (5.61)1669 (27.45)a274 (4.51)857 (14.09)2174 (35.75)
Portal D4674 (76.86)a207 (3.40)165 (2.71)499 (8.21)145 (2.38)b391 (6.43)
Portal E238 (3.91)255 (4.19)622 (10.23)b166 (2.73)3956 (65.06)a842 (13.85)
Portal F65 (1.07)864 (14.21)84 (1.38)b36 (0.59)4441 (73.03)a585 (9.62)
Portal G3925 (64.55)a541 (8.90)b239 (3.93)109 (1.79)157 (2.58)1113 (18.3)
Portal H4129 (67.90)a347 (5.71)681 (11.20)297 (4.88)49 (0.81)b572 (9.41)
Portal I584 (9.60)663 (10.9)b3537 (58.16)a209 (3.44)222 (3.65)870 (14.31)
Portal K851 (13.99)b91 (1.5)4020 (66.11)a321 (5.28)122 (2.01)666 (10.95)

a Hospital with the lowest RAMR.

b Hospital with the highest RAMR.

Were Respondents’ Hospital Choices Associated With Particular Information Design Elements?

Overview

To explore whether respondents’ hospital choices were influenced by the design, we asked the following questions:

  1. Did respondents who used websites that included a given feature choose the hospital with the lowest RAMR significantly more or less often than respondents using portals not including these features?
  2. How did respondents who used the portals including this feature rate the overall comprehensibility of the site?
  3. Based on their answers to the open-ended question about reasons for their decision, did respondents regard this design feature as useful (appreciative comments) or confusing (critical comments)?
Table Without Symbols

Two of 10 portals presented tables without symbols (Table 3). Respondents using these portals chose the hospital with the lowest RAMR significantly less often than respondents using other portals did (46.18%, 575/1245 vs 64.50%, 3119/4836, P<.001). They also rated the comprehensibility of the presentation significantly lower (mean 3.07, SD 1.85 vs mean 3.77, SD 1.92; Table 5). This corresponded with negative comments about tables given in response to the open-ended question. Most responses to the open-ended question that mentioned tables without symbols were disapproving:

I cannot decide as the table is not understandable even after reading the explanations.
I think this table is very confusing; it is difficult to interpret the various measures.
This presentation is much too incomprehensible; therefore, I cannot choose 1 of the hospitals.
Table 5. Choice of the hospital with the lowest risk-adjusted mortality rate (RAMR).
Information presentation featureFeature includedFeature not includedChoice of lowest RAMRComprehensibility

Respondents, nSelected hospital with the lowest RAMR, n (%)Comprehensibility,a mean (SD)Respondents, nSelected hospital with the lowest RAMR, n (%)Comprehensibility, a mean (SD)χ2 (df)Pt (df)P
Table without symbols1245575 (46.18)3.07 (1.85)48363119 (64.50)3.77 (1.92)139.2 (1)<.001-11.657 (6079)<.001
Table with symbols1787928 (51.93)3.58 (1.84)42942766 (64.42)3.65 (1.96)82.5 (1)<.001-1.300 (6979).19
Bar chart without symbols608392 (64.47)2.99 (1.84)54733302 (60.33)3.70 (1.92)3.9 (1).047-8.626 (6079)<.001
Bar chart with symbols24411799 (73.70)4.11 (1.92)36401895 (52.06)3.31 (1.86)286.9 (1)<.00116.289 (6079)<.001
Bar chart with traffic light symbols18141341 (73.93)4.25 (1.93)42672353 (55.14)3.36 (1.86)188.3 (1)<.00116.774 (6079)<.001
Bar chart with thumb symbols627458 (73.05)3.70 (1.83)54543236 (59.33)3.62 (1.94)44.4 (1)<.0011.030 (6079).30
Providers ranked by performance1221883 (72.32)4.29 (1.91)48602811 (57.84)3.46 (1.89)85.8 (1)<.00113.620 (6079)<.001
Explicit statement about whether higher or lower values indicate better performance30172079 (68.91)3.85 (1.97)30641615 (52.71)3.41 (1.86)167.3 (1)<.0019.112 (6079)<.001
No statement about scale direction, but range for good quality presented1220884 (72.46)4.04 (1.91)48612810 (57.81)3.52 (1.92)87.8 (1)<.0018.440 (6079)<.001
*Incomplete data (N/A labels)24451212 (49.57)3.32 (1.88)36362483 (68.30)3.84 (1.93)214.2 (1)<.001-10.436 (6079)<.001

a Based on a 7-point Likert scale with a range of 1=not at all comprehensible to 7=very comprehensible.

Table With Symbols

Three of 10 portals presented tables with evaluative symbols (Table 2). Respondents using these portals chose the hospital with the lowest RAMR significantly less often than respondents using other portals (51.93%, 928/1787 vs 64.42%, 2766/4294; P<.001) and rated the comprehensibility of the presentation lower (without meeting statistical significance) (Table 5). This corresponded to mainly negative comments about tables and positive ones about symbols given in response to the open-ended question (Table 6). In combination with tables or bar charts, 6 of 10 portals (Table 5) used green, yellow, or red symbols; 1 (Portal F) presented thumbs (thumbs up or thumbs down) symbols in these colors (see Figure 2). Red (3/51) and yellow (4/51) symbols were displayed less often than green ones (44/51, 86%). Three portals presented green symbols for all 5 displayed hospitals.

Table 6. Responses to the open-ended question about information presentation.
Information presentation featureResponses, n
Table without symbols39 (38 incomprehensible)
Table with symbolsHelpful green symbol: n=59; table: n=36 (35 incomprehensible)
Bar chart without symbols25 (23 incomprehensible)
Bar chart with symbolsSymbol helpful: n=79; bar chart (incomprehensible): n=39; bar chart (helpful): n=24
Bar chart with traffic light symbolsBar chart: n=50 (30 incomprehensible, 20 helpful); symbol: n=30 (28 helpful)
Bar chart with thumb symbolsHelpful thumb: n=51; bar chart (incomprehensible): n=9; bar chart (helpful): n=4
Providers ranked by performanceRanking (helpful): n=3
Explicit statement about whether higher or lower values indicate better performanceHigher values as a reason for hospital choice: n=67
No statement about scale direction, but range for good quality presented
Incomplete data (N/A labels)Complaints about incomplete or missing data: n=59
Figure 2. Symbols used by 7 of the 10 portals.
View this figure
Bar Chart Without Symbols

One report card used bar charts without symbols (Table 3 and Figure 3). Respondents using this portal chose the hospital with the lowest RAMR significantly more often than respondents using other portals (64.5%, 392/608 vs 60.3%, 3302/ 5473; P=.047), but rated the comprehensibility of the presentation lower (mean 2.99, SD 1.84 vs mean 3.70, SD 1.92; P<.001) (Table 5). This corresponded to mostly negative comments about bar charts given in response to the open-ended question (Table 6).

The bars displayed on Portals G and H were too narrow for evaluation. Portal G presented 4 numbers (benchmarking information, median, mean, highest mortality rate for the hospital) in the bar chart, which may have led to information overload. All bar charts in the German portals used longer bars to indicate lower quality, which led to some confusion.

Most responses to the open-ended question that mentioned bar charts without symbols were disapproving:

Diagram is unclear and incomprehensible.
According to this graphic, I would choose clinic 1, even without really knowing which one is better. The graphics are not very useful.
It has the lowest value, but I find the charts very difficult to understand because I cannot work out what they mean.
Figure 3. Bar chart presentation taken from 5 portals.
View this figure
Bar Chart With Symbols

Four of 10 portals used bar charts with symbols (Table 2 and Figure 2). Respondents using these portals chose the hospital with the lowest RAMR significantly more often than respondents using other portals (73.70%, 1799/2441 vs 52.06%, 1895/3640; P<.001) and rated the comprehensibility of the presentation higher (mean 4.11, SD 1.92 vs mean 3.31, SD 1.86; P<.001) (Table 5). Comments about symbols were mostly positive, but comments about bar charts were mixed (Table 6). Respondents testing Portal A (bar chart with symbols) referred to the bar chart with a blue bar for their decision (see Figure 2), which attracted 10 approving comments, such as:

I didn’t rely on percentages, but the longest blue bar in the diagram.
Longest bar and lowest result.

But, more comments (n=19) were disapproving, such as:

What is the meaning of the bar—is a lot of blue good?
0.9% is the smallest number, even if the blue bar is the longest.
What does this blue bar mean?
Bar Chart With Traffic Light Symbols

Three of 10 portals used bar charts with traffic light symbols (Table 3 and Figure 2). Respondents using these portals chose the hospital with the lowest RAMR significantly more often than respondents using other portals (73.70%, 1341/1814 vs 52.06%, 2353/4267; P<.001); they also rated the comprehensibility of the presentation higher (mean 4.25, SD 1.93 vs mean 3.36, SD 1.86; P<.001) (Table 5). Comments about symbols were mostly positive, but comments about bar charts were mixed (Table 6).

Bar Chart With Thumb Symbols

One of 10 portals used bar charts with thumb symbols (Table 3 and Figure 2). Respondents using this portal chose the hospital with the lowest RAMR significantly more often than respondents using other portals (73.05%, 458/627 vs 59.33%, 3236/5454, P<.001), but assigned a similar rating to the comprehensibility of the presentation (mean 3.70, SD 1.83 vs mean 3.62, SD 1.94; P=.30) (Table 5). Comments about symbols were positive, but the few comments about bar charts were mixed (Table 6).

Ranking Providers by Performance

Two of the 10 portals ranked providers by performance (Table 2 and Figure 2). Respondents using these portals chose the hospital with the lowest RAMR significantly more often than respondents using other portals (72.32%, 883/1221 vs 57.84%, 2811/4860; P<.001) and rated the comprehensibility of the presentation higher (mean 4.29, SD 1.91 vs mean 3.46, SD 1.89; P<.001) (Table 5).

Explicit Statement That Low Values Indicate Good Performance

Five of 10 portals explicitly stated that lower values indicated better performance (Table 3). Respondents using these portals chose the hospital with the lowest RAMR significantly more often than respondents using other portals (68.91%, 2079/3017 vs 52.71%, 1615/3064; P<.001) and rated the comprehensibility of the presentation higher (mean 3.85, SD 1.97 vs mean 3.41, SD 1.86; P<.001) (Table 5).

No Explicit Statement About Scale Direction, But “Good Quality” Range Identified

Two of 10 portals explicitly gave a “good quality” range (Table 3 and Figure 2). Respondents using these portals chose the hospital with the lowest RAMR significantly more often than respondents using other portals (72.46%, 884/1220 vs 57.81%, 2810/4861; P<.001) and rated the comprehensibility of the presentation higher (mean 4.04, SD 1.91 vs mean 3.52, SD 1.92; P<.001) (Table 5).

Incomplete Data (N/A as a Value)

Four of the 10 portals presented incomplete data (N/A as a value) (Table 3) for 1 or more measures (confidence intervals, frequency of cases treated, frequency of mortality, mortality rates, comments about hospital or quality controlling agency). Respondents using these portals chose the hospital with the lowest RAMR significantly less often than respondents using other portals (49.57%, 1212/2445 vs 68.30%, 2483/3636; P<.001) and rated the comprehensibility of the presentation lower (mean 3.32, SD 1.87 vs mean 3.84, SD 1.93; P<.001) (Table 5). A total of 59 responses to the open-ended question complained about incomplete or missing data (Table 6). Comments of respondents about missing values on Portal B (hospitals 4 and 5 had missing data in a number of cases; hospital 5 had the lowest RAMR) included:

As I do not know how many cases hospital 4 or 5 have, I decided against them.
Hospitals 4 and 5 seem to be suspect as they do not show the frequency of cases as the basis for the observed to expected rate.

Because the 2 remaining best-practice elements, evaluative word labels and highlighting best providers, were not included in any of the report cards we studied, we were not able to not investigate their effect on hospital choice.


Overview

The 3 research questions answered are as follows:

  1. What information presentation elements were utilized?
  2. Which led to better comprehension?
  3. Which information presentation elements had similar effects to those reported in the evidence-based recommendations described in the literature?

What Information Presentation Elements Were Utilized?

We identified 10 elements that were used in the presentation of RAMR for coronary catheterization by 10 German portals (see Table 2).

Which Led to Better Comprehension?

Report cards using the following 7 presentation elements were more comprehensible, because respondents choose the hospital with the lowest RAMR significantly more often: (1) bar chart without symbols, (2) bar chart with symbols, (3) bar chart with traffic light symbols, (4) bar chart with thumb symbols, (5) providers ranked by performance, (6) an explicit statement about whether higher or lower values indicated better performance, and (7) no statement about scale direction, but a presented range for good quality.

Furthermore, respondents rated presentations as more comprehensible when they contained the following 4 elements: (1) bar chart with symbols, (2) bar chart with traffic light symbols, (3) providers ranked by performance, (4) explicit statement about whether higher or lower values indicated better performance, and (5) no statement about scale direction, but range for good quality presented.

Report cards using the following 2 presentation elements were less comprehensible, because respondents using these elements choose the hospital with the lowest RAMR significantly less often. This was true for tables without symbols or for incomplete data (N/A labels). Moreover, respondents rated presentations as less comprehensible when they contained tables without symbols or incomplete data (N/A labels).

Which Information Presentation Elements Had Similar Effects to Those Reported in the Evidence-Based Recommendations Described in the Literature?

Based on the 13 identified international reports about the presentation of information on report cards, 14 information presentation elements were identified. These studies relied on a variety of methods, including in-depth or cognitive interviews and focus groups, plus online, telephone, or paper-based surveys as well as controlled laboratory experiments (see Multimedia Appendix 1). This variety of methods made it possible to obtain insights from different perspectives about the behavior of users, but also made it more difficult to systematically compare the results. Additionally, samples used in the studies varied widely; sizes ranged from 59 to 2052. Sample selections also had limitations, such as the use of convenience samples, limited geographic locations, low response rates, or a possible selection bias because of questioning by mail and Internet. These limitations have to be taken into account when conclusions are drawn from those studies.

Did the Respondents to Our Survey Make Choices and Give Reasons That Corresponded to the Results of the 13 Identified International Reports?

Our respondents, like those in the study by Gerteis et al [29], made more interpretive errors with the numeric formats (table without symbols) than with the graphical formats (bar charts). They had more difficulty understanding numeric tables than other presentation formats, a finding similar to that of Donelan et al [31].

Similar to Geraedts et al [30], we observed that the hospital with the lowest mortality was chosen more often when tables with symbols were used than when tables without symbols were used. In our survey, tables with symbols were rated better for comprehension (mean 3.58, SD 1.84) than bar charts without symbols (mean 2.99, SD 1.84). However, we cannot fully endorse Gerteis et al’s [29] recommendation (in a study limited to 90 respondents) that tables with symbols should be used rather than a standard bar chart without symbols. In our survey, bar charts without symbols more frequently resulted in the choice of the hospital with the lowest mortality (64.47%) than tables with symbols (51.93%), but as discussed previously, missing data rather than the presentational format may have been the major reason.

Similar to Damman et al [32], we found that bar charts were used quite often and, like Gerteis et al [29], we found that graphical formats (standard bar chart without symbols) were not liked by respondents. However, we did not confirm the findings of Geraedts et al, who presented the formats of presentations to physicians, that bar charts without symbols did not assist the comprehension of data on hospital quality [30] or were the format least well understood by participants [29]. In our sample, more respondents who used a bar chart without symbols (64.47%) chose the hospital with the lowest mortality rate than respondents who did not (60.33%). As Figure 3 shows, Portal G displayed bars that were too narrow for evaluation, but numbers were also included. Thus, respondents may still have been able to evaluate the presentation by evaluating the numerical value instead of using the bar chart (“It has the lowest value, but I find the charts very difficult to understand”).

The respondents in our sample who used portals that displayed bar charts with symbols were more likely than average to choose the hospital with the lowest mortality rate, a result which is in accordance with other studies [31-33]. Two portals ranked hospitals by performance, as recommended in several studies [12,15,30,33,35,36]. This was also valued by our participants.

Our results support the recommendation that whether high or low values indicate good performance should be explicitly stated [8,36]. Incomplete data (N/A given as a value) had a negative influence on provider assessment, as Gerteis et al [29] suggested.

To summarize, 5 of 14 information presentation elements were found to be associated with better comprehension as reported in literature: (1) bar charts with symbols, (2) explicit statement about whether higher or lower values indicated better performance, and (3) providers ranked by performance. A reduction of user comprehension was associated with (4) incomplete data (N/A labels) and (5) tables without symbols, again in accordance with literature-based evidence. Reasons have been given for the 2 discrepant findings on (6) bar charts without symbols (improvement of comprehension observed in our German survey, but not described in international literature) and (7) tables with symbols (improvement of comprehension described in international literature, but not observed in our German survey).

Conclusions

To our knowledge, this is the first study to systematically analyze the most commonly used public report card designs in Germany. It is also the first study to analyze German report cards using real-world applications [15] instead of controlled laboratory studies. The best-practice evidence found in 13 international studies led to 14 findings about information presentation elements. However, due to limitations of these studies, conclusions have to be drawn carefully. Five of these findings were in agreement with our findings about German report card designs: (1) avoid tables without symbols, (2) include bar charts with symbols, (3) state explicitly whether high or low values indicate good performance or provide a “good quality” range, (4) avoid incomplete data (N/A given as a value), and (5) rank hospitals by performance. However, due to limitations of our study as described subsequently, these recommendations are preliminary and should be subject to further evaluation.

The implementation of 4 of these findings should not present an insurmountable obstacle to public reporting instruments because they can be achieved by redesigning the format. However, ranking hospitals by performance may present substantial difficulties because ordering by performance is often resisted by providers [7]. Ordering makes report sponsors responsible for determining what constitutes a meaningful difference in mortality rates. The benchmarking information available in Germany might provide a basis for ranking providers. However, ranking might also require measures of statistical significance to be provided that have the potential to confuse users [8,31,36,50,51].

Externally validated measures of hospital quality could also be used as the basis for ranking hospitals and are readily available in Germany [17]; indeed, most German portals use these results to assign their traffic light symbols. However, because only approximately 0.1% of quality measures are finally validated as being qualitatively discrepant, the ranking of hospitals in these terms is difficult. As a respondent put it: “All the symbols for hospital quality are green.”

Limitations

Several limitations are relevant for this study. This report only investigated the design of report cards. Other important features of report cards (eg, the quality of explanations, use of technical terms, and provision of a quality framework) will be the subject of further studies. Respondents in our study only saw a small part of the public reporting portals because we focused on the presentation of 1 RAMR measure. Five report cards (Portals B, E, F, G, I) did not allow a performance comparison of hospitals in the way we presented it in this study, but were adapted to enable respondents to make the comparison we were investigating. We limited our search for recommendations on best practice in the presentation of information in public report cards to the PubMed and Cochrane medical databases and did not take into account other databases, such as PsychINFO.

Because our study was designed as a Web survey, the results presented might be influenced by self-selection of the study participants. Better-educated persons were overrepresented. Because education levels influence comprehension of public report cards, this might have influenced our results; certain features of presented information might have been misunderstood depending on the education of respondents. Hibbard et al [52] stated that higher education levels were related to improved performance. Emmert et al [53] found that those with a higher level of education tended to comprehend public reporting better. Damman et al [32] stated that consumers’ educational level was positively related to the correct interpretation. Donelan et al [33] found that respondents with at least some college education were significantly more likely to identify surgeons with the lowest risk-adjusted mortality, compared with respondents having no college education.

Acknowledgments

We would like to thank Laura Polster, Elena Kreß, Petra Nölp, and Mareike von der Ahe for their assistance in coding open-ended questions, and Markus Reichert for his assistance in the literature research. This study was financed by the Dr Theo and Friedl Schoeller Research Center at the Friedrich-Alexander-University of Erlangen-Nuremberg.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Methodological quality of used literature.

PDF File (Adobe PDF File), 130KB

Multimedia Appendix 2

Screenshots of report cards.

PDF File (Adobe PDF File), 1MB

References

  1. Lebrun LA, Shi L, Zhu J, Sharma R, Sripipatana A, Hayashi AS, et al. Racial/ethnic differences in clinical quality performance among health centers. J Ambul Care Manage 2013;36(1):24-34. [CrossRef] [Medline]
  2. McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, et al. The quality of health care delivered to adults in the United States. N Engl J Med 2003 Jun 26;348(26):2635-2645. [CrossRef] [Medline]
  3. Asch SM, Kerr EA, Keesey J, Adams JL, Setodji CM, Malik S, et al. Who is at greatest risk for receiving poor-quality health care? N Engl J Med 2006 Mar 16;354(11):1147-1156. [CrossRef] [Medline]
  4. Laschet H. ÄrzteZeitung 21. The German GBA targets five challenging indications for treatment   URL: http:/​/www.​aerztezeitung.de/​politik_gesellschaft/​versorgungsforschung/​article/​829958/​qualitaet-gba-knoepft-fuenf-problem-indikation.​html [accessed 2015-03-07] [WebCite Cache]
  5. Merchant RM, Yang L, Becker LB, Berg RA, Nadkarni V, Nichol G, American Heart Association Get With the Guideline-Resuscitation Investigators. Variability in case-mix adjusted in-hospital cardiac arrest rates. Med Care 2012 Feb;50(2):124-130 [FREE Full text] [CrossRef] [Medline]
  6. Tsai J, Rosenheck RA. Racial differences among supported housing clients in outcomes and therapeutic relationships. Psychiatr Q 2012 Mar;83(1):103-112. [CrossRef] [Medline]
  7. Hibbard JH, Sofaer S. Best Practices in Public Reporting No. 1: How To Effectively Present Health Care Performance Data To Consumers. Rockville, MD: Agency for Healthcare Research and Quality; Jun 2010.
  8. Hibbard JH, Cunningham PJ. How engaged are consumers in their health and health care, and why does it matter? Res Brief 2008 Oct(8):1-9. [Medline]
  9. Porter ME, Teisberg EO. Redefining competition in health care. Harv Bus Rev 2004 Jun;82(6):64-76, 136. [Medline]
  10. Lagu T, Lindenauer PK. Putting the public back in public reporting of health care quality. JAMA 2010 Oct 20;304(15):1711-1712. [CrossRef] [Medline]
  11. Faber M, Bosch M, Wollersheim H, Leatherman S, Grol R. Public reporting in health care: how do consumers use quality-of-care information? A systematic review. Med Care 2009 Jan;47(1):1-8. [CrossRef] [Medline]
  12. Emmert M, Gemza R, Schöffski O, Sohn S. [Public reporting in health care: the impact of publicly reported quality data on patient steerage]. Gesundheitswesen 2012 Jun;74(6):e25-e41. [CrossRef] [Medline]
  13. Fung CH, Lim YW, Mattke S, Damberg C, Shekelle PG. Systematic review: the evidence that publishing patient care performance data improves quality of care. Ann Intern Med 2008 Jan 15;148(2):111-123. [Medline]
  14. Hibbard JH, Peters E. Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice. Annu Rev Public Health 2003;24:413-433. [CrossRef] [Medline]
  15. Werner RM, Asch DA. The unintended consequences of publicly reporting quality information. JAMA 2005 Mar 9;293(10):1239-1244. [CrossRef] [Medline]
  16. AQUA-Institut G. German Hospital Quality Report. German Hospital Quality Report 2011;2012:50-53.
  17. Friedemann J, Schubert HJ, Schwappach D. [On the comprehensibility of German hospital quality reports: systematic evaluation and need for action]. Gesundheitswesen 2009 Jan;71(1):3-9. [CrossRef] [Medline]
  18. Grantmakers In Health‚ Washington‚ DC‚ USA. Considering quality: engaging consumers to make better health care decisions. Issue Brief (Grantmakers Health) 2007 Jan(27):i-v, 1. [Medline]
  19. Sinaiko AD, Eastman D, Rosenthal MB. How report cards on physicians, physician groups, and hospitals can have greater impact on consumer choices. Health Aff (Millwood) 2012 Mar;31(3):602-611 [FREE Full text] [CrossRef] [Medline]
  20. Kullgren JT, Werner RM. Counterpoint: will public reporting of health-care quality measures inform and educate patients? No. Chest 2011 Nov;140(5):1117-20; discussion 1120. [CrossRef] [Medline]
  21. Damman OC, van den Hengel YKA, van Loon AJM, Rademakers J. An international comparison of web-based reporting about health care quality: content analysis. J Med Internet Res 2010;12(2):e8 [FREE Full text] [CrossRef] [Medline]
  22. Rothberg MB, Benjamin EM, Lindenauer PK. Public reporting of hospital quality: recommendations to benefit patients and hospitals. J Hosp Med 2009 Nov;4(9):541-545. [CrossRef] [Medline]
  23. Emmert M, Sander U, Esslinger AS, Maryschok M, Schöffski O. Public reporting in Germany: the content of physician rating websites. Methods Inf Med 2012;51(2):112-120. [CrossRef] [Medline]
  24. Amini A, Birnbaum DW, Black B, Hyman DA. Public reporting of hospital infection rates: ranking the states on credibility and user friendliness. Stud Health Technol Inform 2013;183:87-92. [Medline]
  25. Emmerson J. Visualizing Information for Advocacy: An Introduction to Information Design. Berlin: Tactical Technology Collective; 2008.
  26. Consortium of Chief Quality Officers. Using hospital standardized mortality ratios for public reporting: a comment by the consortium of chief quality officers. Am J Med Qual 2009;24(2):164-165. [CrossRef] [Medline]
  27. The Lancet. Public reporting of surgical outcomes. Lancet 2011 Apr 2;377(9772):1126. [CrossRef] [Medline]
  28. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv Res 2010 Dec;45(6 Pt 1):1614-1629 [FREE Full text] [CrossRef] [Medline]
  29. Gerteis M, Gerteis JS, Newman D, Koepke C. Testing consumers' comprehension of quality measures using alternative reporting formats. Health Care Financ Rev 2007;28(3):31-45 [FREE Full text] [Medline]
  30. Geraedts M, Hermeling P, de CW. Communicating quality of care information to physicians: a study of eight presentation formats. Patient Educ Couns 2012 Jun;87(3):375-382. [CrossRef] [Medline]
  31. Donelan K, Rogers RS, Eisenhauer A, Mort E, Agnihotri AK. Consumer comprehension of surgeon performance data for coronary bypass procedures. Ann Thorac Surg 2011 May;91(5):1400-5; discussion 1405. [CrossRef] [Medline]
  32. Damman OC, Hendriks M, Rademakers J, Spreeuwenberg P, Delnoij DMJ, Groenewegen PP. Consumers' interpretation and use of comparative information on the quality of health care: the effect of presentation approaches. Health Expect 2012 Jun;15(2):197-211. [CrossRef] [Medline]
  33. Hibbard JH, Peters E, Slovic P, Finucane ML, Tusler M. Making health care quality reports easier to use. Jt Comm J Qual Improv 2001 Nov;27(11):591-604. [Medline]
  34. Hibbard JH, Slovic P, Peters E, Finucane ML. Strategies for reporting health plan performance information to consumers: evidence from controlled studies. Health Serv Res 2002 Apr;37(2):291-313 [FREE Full text] [Medline]
  35. Peters E, Dieckmann N, Dixon A, Hibbard JH, Mertz CK. Less is more in presenting quality information to consumers. Med Care Res Rev 2007 Apr;64(2):169-190. [CrossRef] [Medline]
  36. Mazor KM, Dodd KS. A qualitative study of consumers' views on public reporting of health care-associated infections. Am J Med Qual 2009;24(5):412-418. [CrossRef] [Medline]
  37. Uhrig JD, Harris-Kojetin L, Bann C, Kuo TM. Do content and format affect older consumers' use of comparative information in a Medicare health plan choice? Results from a controlled experiment. Med Care Res Rev 2006 Dec;63(6):701-718. [CrossRef] [Medline]
  38. Harris-Kojetin LD, McCormack LA, Jaël EF, Sangl JA, Garfinkel SA. Creating more effective health plan quality reports for consumers: lessons from a synthesis of qualitative testing. Health Serv Res 2001 Jul;36(3):447-476 [FREE Full text] [Medline]
  39. krankenhaussuche.berlin.de.   URL: http://krankenhaussuche.berlin.de/ [accessed 2015-03-01] [WebCite Cache]
  40. BKK.   URL: https://www.bkk-klinikfinder.de/ [accessed 2015-03-07] [WebCite Cache]
  41. Deutsches Krankenhaus Verzeichnis.   URL: http://www.deutsches-krankenhaus-verzeichnis.de/de/19/Suche.html [accessed 2015-03-07] [WebCite Cache]
  42. Hamburger Krankenhausspiegel.   URL: http://www.hamburger-krankenhausspiegel.de/ [accessed 2015-03-07] [WebCite Cache]
  43. IQM.   URL: http://www.initiative-qualitaetsmedizin.de/ [accessed 2015-03-07] [WebCite Cache]
  44. Krankenhaus.de.   URL: http://www.krankenhaus.de/ [accessed 2015-03-07] [WebCite Cache]
  45. Qualitaetskliniken.de.   URL: http://www.qualitaetskliniken.de/ [accessed 2015-03-07] [WebCite Cache]
  46. Klinikfuehrer Rheinland.   URL: http://www.klinikfuehrer-rheinland.de/ [accessed 2015-03-07] [WebCite Cache]
  47. TK-Klinikfuehrer.   URL: https://www.tk.de/tk/klinikfuehrer/114928 [accessed 2015-03-07] [WebCite Cache]
  48. Weisse Liste.   URL: http://www.weisse-liste.de/ [accessed 2015-03-07] [WebCite Cache]
  49. Internet Facts 2014-07. Arbeitsgemeinschaft Online Forschung (AGOF) 2014   URL: http://www.agof.de/aktuelle-studie-internet/ [accessed 2015-03-07] [WebCite Cache]
  50. Dudley A, Hibbard J, Shaller D. Model Public Report Elements: A Sampler. Rockville, MD: Agency for Healthcare Research and Quality; 2010:10-0088.
  51. Shahian DM, Edwards FH, Jacobs JP, Prager RL, Normand SLT, Shewan CM, et al. Public reporting of cardiac surgery performance: Part 1--history, rationale, consequences. Ann Thorac Surg 2011 Sep;92(3 Suppl):S2-11. [CrossRef] [Medline]
  52. Hibbard JH, Slovic P, Peters E, Finucane ML, Tusler M. Is the informed-choice policy approach appropriate for medicare beneficiaries? Health Affairs 2001 May 01;20(3):199-203. [CrossRef]
  53. Emmert M, Hessemer S, Meszmer N, Sander U. Do German hospital report cards have the potential to improve the quality of care? Health Policy 2014 Dec;118(3):386-395. [CrossRef] [Medline]


AGOF: Arbeitsgemeinschaft Online Forschung
PCI: percutaneous coronary intervention
RAMR: risk-adjusted mortality rate


Edited by G Eysenbach; submitted 21.03.14; peer-reviewed by M Geraedts, C Schaefer; comments to author 21.08.14; revised version received 03.10.14; accepted 22.01.15; published 16.03.15

Copyright

©Uwe Sander, Martin Emmert, Jochen Dickel, Nina Meszmer, Benjamin Kolb. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.03.2015.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.