ASTM E2171-2002(2008) Standard Practice for Rating-Scale Measures Relevant to the Electronic Health Record《与电子健康记录相关的等级划分测量的标准实施规程》.pdf

《ASTM E2171-2002(2008) Standard Practice for Rating-Scale Measures Relevant to the Electronic Health Record《与电子健康记录相关的等级划分测量的标准实施规程》.pdf》由会员分享，可在线阅读，更多相关《ASTM E2171-2002(2008) Standard Practice for Rating-Scale Measures Relevant to the Electronic Health Record《与电子健康记录相关的等级划分测量的标准实施规程》.pdf（23页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: E 2171 02 (Reapproved 2008)An American National StandardStandard Practice forRating-Scale Measures Relevant to the Electronic HealthRecord1This standard is issued under the fixed designation E 2171; the number immediately following the designation indicates the year oforiginal adoption

2、or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This standard addresses the identification of data ele-ments from the EHR def

3、initions in Practice E 1384 that haveordinal scale value sets and which can be further defined tohave scale-free measurement properties. It is applicable to datarecorded for the Electronic Health Record and its papercounterparts. It is also applicable to abstracted data from thepatient record that o

4、riginates from these same data elements. Itis applicable to identifying the location within the EHR wherethe observed measurements shall be stored and what is themeaning of the stored data. It does not address either the usesor the interpretations of the stored measurements.2. Referenced Documents2.

5、1 ASTM Standards:2E 177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE 456 Terminology Relating to Quality and StatisticsE 691 Practice for Conducting an Interlaboratory Study toDetermine the Precision of a Test MethodE 1169 Practice for Conducting Ruggedness TestsE 1384 Pract

6、ice for Content and Structure of the ElectronicHealth Record (EHR)3. Terminology3.1 DefinitionsFull definitions and discussion of Scale-Free Measurement Terms are given in Annex A1.3.2 Definitions of Terms Specific to This Standard:3.2.1 adaptive measurementadvantage of measurement toaccount for mis

7、sing data.3.2.2 additivityrating scale adherence to associativity andcommutability.3.2.3 bias analysisinvestigation of considerations relativeto subject or area of performance.3.2.4 calibrationprocess of establishing additivity andreproducability of a data set.3.2.5 concatenationprocess of measureme

8、nt uses enumer-ated physical unit quantities equal to the magnitude of themeasured item.3.2.6 constructname of the conceptual domain measured.3.2.7 convergenceclosing of the differences in sequentialmeasure estimates.3.2.8 countingbasic activity upon which measurement isbased and utilizes enumeratio

9、n.3.2.9 dataobservation made in such a way that they leadto generalization.3.2.10 data quality/ statistical consistency/ model fitestablishment of whether the measuring instrument is affectedby the object of measurement.3.2.11 determinismmeasurement model that requirescounts to be sufficient for rep

10、roducing the pattern of theresponses over the length of the instrument.3.2.12 dimensionalityproperty of having multiple compo-nents of a measured value.3.2.13 equality/cocalibrationprocess of ensuring that dif-ferent instruments measure the same property.3.2.14 erroruncertainty of measured propertie

11、s.3.2.15 estimation algorithmsmathematical specificationof an observational framework.3.2.16 incommensurable/commensurablemeasure valueof the same quantity does/does not depend upon rating/responses of the rating construct and does not/does remainconstant.3.2.17 instrumentsensing device having a def

12、ined scale.3.2.18 intra and inter-laboratory testingvariability testingusing the same setting/measure/operator as opposed to differentsetting/measure/operators.3.2.19 item response/latent trait theoryanalytic modelsthat forego prescriptive parameter separation, sufficiency andscale and sample free d

13、ata standards for additional descriptiveparameters.3.2.20 items/item-bankpart of survey statements/testquestions for adaptive administration.1This practice is under the jurisdiction of ASTM Committee E31 on HealthcareInformatics and is the direct responsibility of Subcommittee E31.25 on HealthcareDa

14、ta Management, Security, Confidentiality, and Privacy.Current edition approved Sept. 15, 2008. Published December 2008. Originallyapproved in 2002. Last previous edition approved in 2002 as E 2171 02.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Servic

15、e at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.3.2.21 levels of measurementnature of scale of

16、 measure-ment.3.2.22 logitscale unit using logarithms of odds ratios.(P/1P).3.2.23 mathematical entitiesconcepts that can be taughtor learned through what is already known.3.2.24 measurementdetermining in units the value of aproperty in a scale having magnitude (that is, ratio or differ-ence).3.2.25

17、 metaphor in measurementsuspension of disbeliefof some areas or properties in the name of estimating magni-tude.3.2.26 metricmeasure of a property in defined units.3.2.27 missing datause of uncalibrated data in instru-ments with varying numbers of items.3.2.28 multi-faceted measurementuse of measure

18、mentmodels that have more than two basic parameters.3.2.29 ordinal dataone scale for measurement.3.2.30 populationuniverse of elements relevant to mea-surement of a particular construct.3.2.31 probabilistic conjoint measurementframework fordemonstrating data quality, statistical consistency, and mod

19、elfit of non-deterministic measures with a stable order of facets.3.2.32 quantificationcocalibration of different constructswith respect to the same property (variable) in a commonmetric.3.2.33 Rasch analysis measurement and modelsanalyticmodel specifying the observational framework and data quality

20、measures for quantification.3.2.34 raw scoresum of ratings or count of direct re-sponses in a given measurement event.3.2.35 reliabilityratio of variation to error or signal tonoise.3.2.36 repeatabilityvariability of measurements in asingle setting by a single operator using the same measuringinstru

21、ment.3.2.37 reproducibilityvariability of measurements in dif-ferent settings.3.2.38 root mean square errormathematical algorithm fordetermining the variation due to error of the estimates.3.2.39 samplesubset of measured population.3.2.40 sample sizemagnitude of the measured population.3.2.41 scale-

22、free/scale-dependentmeasures not affectedby the instrument employed as opposed to measures that are soaffected.3.2.42 separability theorem/parameter separationabilityof measures to be independent of the instrument selected andability of the instruments item calibrations to be independentof the sampl

23、e measured.3.2.43 softwarepackages of machine code used for dataanalysis.3.2.44 specific objectivitydata satisfying the separabilitytheorem.3.2.45 standardizedcommon conventions for instruments,reference measurement material, scales and units of measurefor a measurement process.3.2.46 suffciencystat

24、istics that extract all available infor-mation from the data.3.2.47 targetinglack of floor an/or ceiling effects in mea-surement.3.2.48 transparencyability to “look through” raw scoresto the composite ratings producing that score (see also suff-ciency).3.2.49 unit of measurementcommon conventions fo

25、r theappropriate smallest basic measures for a given construct.3.2.50 validity/construct/contentboth content and con-struct must make sound theoretical sense to be consideredvalid.3.2.51 variableattribute of the property being measured.4. Significance and Use4.1 The simplicity and practicality of Ra

26、schs probabilisticscale-free measurement models have brought within reachuniversal metrics for educational and psychological tests, andfor rating scale-based instruments in general. There are at least3 implications to the application of Raschs models to thehealth-related calibration of universal met

27、rics for each of thevariables relevant to the Electronic Health Record (EHR) thatare typically measured using rating scale instruments.4.1.1 First, establishing a single metric standard with adefined range and unit will arrest the burgeoning proliferationof new scale-dependent metrics.4.1.2 Second,

28、the communication of the information per-taining to patient status represented by these measures (physi-cal, cognitive, and psychosocial health status, quality of life,satisfaction with services, etc.) will be simplified.4.1.3 Third, common standards of data quality will be usedto evaluate and impro

29、ve instrument performance. The vastmajority of test and survey data quality is currently almostcompletely unknown, and when quality is evaluated, it is viamany different methods that are often insufficient to the task,misapplied, misinterpreted, or even contradictory in their aims.4.1.4 Fourth, curr

30、ently unavailable economic benefits willaccrue from the implementation of measurement methodsbased on quality-assessed data and widely accepted referencestandard metrics. The potential magnitude of these benefits canbe seen in an assessment of 12 different metrological improve-ment studies conducted

31、 by the National Science and Technol-ogy Council (Subcommittee on Research, 1996). The averagereturn on investment associated with these twelve studies was147 %. Is there any reason to suppose that similar instrumentimprovement efforts in the psychosocial sciences will result inmarkedly lower return

32、s?4.2 Until now, it has been assumed that the Practice E 1384would necessarily have to stipulate fields for the EHR thatwould contain summary scores from commonly used func-tional assessment, health status, quality of life, and satisfactioninstruments. This is because standards for rating scale inst

33、ru-ments to date have been entirely content-based. Those whohave sought “gold” or criterion standards that would commanduniversal respect and relevance have been stymied by theimpossibility of identifying content (survey questions andrating categories) capable of satisfying all users needs. Com-muni

34、cation of patient statistics between managers and clini-cians, or payors and providers, may require one kind ofinformation; between providers and referral sources, otherkinds; between providers and accreditors, yet another; amongE 2171 02 (2008)2clinicians themselves, still another; and even more ki

35、nds ofinformation may be required for research applications.4.2.1 For instance, payors may want to know outcomeinformation that tells them what percentage of patients dis-charged can function independently at home. A hospital man-ager, referral source, or accreditor might want to know moredetail, su

36、ch as percentages of patients discharged who candress, bathe, walk, and eat independently. Clinicians will wantto know still more detail about amounts of independence, suchas whether there are safety issues, needs for assistive devices,or specific areas in which functionality could be improved.Resea

37、rchers may seek even more detail yet, as they evaluatedifferences in outcomes across treatment programs, diagnosticgroups, facilities, levels of care, etc.4.2.1.1 Members of each of these groups have, at sometime, felt that their particular information needs have not beenmet by the tools designed an

38、d developed by members ofanother group. Despite the fact that the information providedby these different tools appears in many different forms and atdifferent levels of detail, to the extent that they can be shownto measure the same thing, they can do so in the same metric.This is the primary result

39、 of the introduction of Raschsprobabilistic scale-free measurement models. The differentpurposes guiding the design of the instruments will stillcontinue to impact the two fundamental statistics associatedwith every measure: the error and model fit. More general, andalso less well-designed instrumen

40、ts, will measure with moreerror than those that make more detailed and consistentdistinctions. Data consistency is the key to scale-free measure-ment.4.3 The remainder of this document (1) identifies, in Section5, the fields in the current Practice E 1384 targeted for changefrom a scale-dependent to

41、 a scale-free measurement orienta-tion; (2) lists referenced ASTM documents; (3) defines scale-free measurement terms, often contrasting them with theirscale-dependent counterparts; (4) addresses the significanceand use of scale-free measures in the context of the EHR; (5)lists, inAnnexA2, scientifi

42、c publications documenting relevantinstrument calibrations; (6) briefly presents some basic opera-tional considerations; (7) lists minimum and comprehensivearrays of EHR database fields; and (8) lists, in Annex A3, thereferences made in presentation of the measurement theory,estimation methods, etc.

43、4.4 Publications of calibration studies referencing this prac-tice and the associated standard practice should require:4.4.1 The use of measures, not scores, in all capture of datafrom the EHR for statistical comparisons;4.4.2 The reporting of both the traditional reliability statis-tics (Cronbachs

44、alpha or the KR20) and the additive, linearseparation statistics (Wright 4.4.3 Aqualitative elaboration of the variable defined by theorder of the survey questions or test items on the measurementcontinuum, preferably in association with a figure displayingthe variable;4.4.4 Reporting of means and s

45、tandard deviations for eachof the three essential measurement statistics, the measure, theerror, and the model fit;4.4.5 Statement of the full text of at least a significantsample of the questions included on the instrument;4.4.6 Specification of the mathematical model employed,with a justification

46、for its use;4.4.7 Specification of the error estimation and model fitestimation algorithms employed, with mathematical details andjustification provided when they differ from those routinelyused;4.4.8 Evaluation of overall model fit, elaborated in a reporton the details of one or more of the least a

47、nd most consistentresponse patterns observed;4.4.9 Graphical comparison of at least two calibrations ofnew instruments from different samples of the same populationto establish the invariance of the item calibration order acrosssamples;4.4.10 Graphical comparison of measures produced by atleast two

48、subsets of items on new instruments to establish theinvariance of the person measure order across scales (collec-tions of items);4.4.11 Graphical comparison of new instrument calibrationswith the calibrations produced by other instruments intended tomeasure the same variable in the same population,

49、to establishthe potential for sample-free equating of the instruments andestablishment of reference standards;4.4.12 At least a useable prototype of the instrument em-ployed, with the worksheet laid out to produce informativequantitative measures (not summed scores) as soon as it isfilled out; and4.4.13 Graphical presentation of the treatment and controlgroups measurement distributions, for the purpose of facili-tating a substantive interpretations of differences significance.5. Applicable Data Elements5.1 The data eleme