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Home / Quality and Performance Improvement in Healthcare Chapter 11 Review Questions

Quality and Performance Improvement in Healthcare Chapter 11 Review Questions

iii.ane. Introduction

The field of quality measurement in healthcare has developed considerably in the past few decades and has attracted growing interest among researchers, policy-makers and the general public (Papanicolas & Smith, 2013; EC, 2016; OECD, 2019). Researchers and policy-makers are increasingly seeking to develop more systematic means of measuring and benchmarking quality of care of dissimilar providers. Quality of care is now systematically reported as office of overall wellness system performance reports in many countries, including Australia, Belgium, Canada, Italia, United mexican states, Kingdom of spain, the Netherlands, and well-nigh Nordic countries. At the same fourth dimension, international efforts in comparing and benchmarking quality of care across countries are mounting. The Organisation for Economic Co-operation and Development (OECD) and the EU Commission have both expanded their efforts at assessing and comparing healthcare quality internationally (Carinci et al., 2015; EC, 2016). Furthermore, a growing focus on value-based healthcare (Porter, 2010) has sparked renewed involvement in the standardization of measurement of outcomes (ICHOM, 2019), and notably the measurement of patient-reported outcomes has gained momentum (OECD, 2019).

The increasing interest in quality measurement has been accompanied and supported by the growing ability to measure and analyse quality of intendance, driven, among others, past significant changes in it and associated advances in measurement methodology. National policy-makers recognize that without measurement information technology is difficult to clinch loftier quality of service provision in a land, every bit information technology is incommunicable to identify good and bad providers or good and bad practitioners without reliable information about quality of care. Measuring quality of intendance is important for a range of different stakeholders within healthcare systems, and it builds the footing for numerous quality assurance and comeback strategies discussed in Part Ii of this volume. In particular, accreditation and certification (see Chapter 8), audit and feedback (see Chapter 10), public reporting (encounter Chapter 13) and pay for quality (see Affiliate 14) rely heavily on the availability of reliable information about the quality of care provided by unlike providers and/or professionals. Common to all strategies in Role Ii is that without robust measurement of quality, information technology is impossible to determine the extent to which new regulations or quality improvement interventions really work and improve quality every bit expected, or if there are also adverse effects related to these changes.

This chapter presents different approaches, frameworks and information sources used in quality measurement as well as methodological challenges, such as run a risk-adjustment, that demand to be considered when making inferences nearly quality measures. In line with the focus of this volume (see Affiliate ane), the chapter focuses on measuring quality of healthcare services, i.e. on the quality dimensions of effectiveness, patient safety and patient-centredness. Other dimensions of health system performance, such every bit accessibility and efficiency, are not covered in this chapter as they are the focus of other volumes about wellness organisation performance assessment (see, for example, Smith et al., 2009; Papanicolas & Smith, 2013; Cylus, Papanicolas & Smith, 2016). The chapter too provides examples of quality measurement systems in place in different countries. An overview of the history of quality measurement (with a focus on the United States) is given in Marjoua & Bozic (2012). Overviews of measurement challenges related to international comparisons are provided past Forde, Morgan & Klazinga (2013) and Papanicolas & Smith (2013).

three.two. How tin quality exist measured? From a concept of quality to quality indicators

About quality measurement initiatives are concerned with the development and assessment of quality indicators (Lawrence & Olesen, 1997; Mainz, 2003; EC, 2016). Therefore, it is useful to step back and reflect on the thought of an indicator more than generally. In the social sciences, an indicator is defined as "a quantitative measure that provides data about a variable that is difficult to mensurate directly" (Calhoun, 2002). Obviously, quality of intendance is difficult to measure direct because it is a theoretical concept that can encompass different aspects depending on the exact definition and the context of measurement.

Chapter 1 has defined quality of care as "the degree to which wellness services for individuals and populations are effective, condom and people-centred". However, the chapter also highlighted that at that place is considerable confusion about the concept of quality because dissimilar institutions and people often mean dissimilar things when using it. To a certain degree, this is inevitable and even desirable because quality of care does hateful different things in different contexts. Still, this context dependency also makes clarity about the exact conceptualization of quality in a particular setting particularly important, earlier measurement can be initiated.

In line with the definition of quality in this book, quality indicators are defined as quantitative measures that provide information nearly the effectiveness, safety and/or people-centredness of care. Of course, numerous other definitions of quality indicators are possible (Mainz, 2003; Lawrence & Olesen, 1997). In addition, some institutions, such as the National Quality Forum (NQF) in the USA, use the term quality measure instead of quality indicator. Other institutions, such as the NHS Indicator Methodology and Assurance Service and the German Establish for Quality Assurance and Transparency in Health Care (IQTIG), define further attributes of quality indicators (IQTIG, 2018; NHS Digital, 2019a). Co-ordinate to these definitions, quality indicators should provide:

  1. a quality goal, i.eastward. a articulate statement near the intended goal or objective, for example, inpatient bloodshed of patients admitted with pneumonia should exist as low as possible;

  2. a measurement concept, i.e. a specified method for information collection and calculation of the indicator, for example, the proportion of inpatients with a primary diagnosis of pneumonia who died during the inpatient stay; and

  3. an appraisal concept, i.eastward. a description of how a mensurate is expected to be used to approximate quality, for case, if inpatient bloodshed is beneath 10%, this is considered to be adept quality.

Often the terms measures and indicators are used interchangeably. However, it makes sense to reserve the term quality indicator for measures that are accompanied by an appraisal concept (IQTIG, 2018). This is because measures without an appraisal concept are unable to point whether measured values represent good or bad quality of care. For example, the readmission rate is a mensurate for the number of readmissions. However, it becomes a quality indicator if a threshold is divers that indicates "higher than normal" readmissions, which could, in turn, indicate poor quality of care. Some other term that is frequently used interchangeably with quali ty indicator, in detail in the United states of america, is quality metric. However, a quality metric also does not necessarily define an appraisement concept, which could potentially distinguish it from an indicator. At the same time, the term qua l ity metric is sometimes used more broadly for an entire system that aims to evaluate quality of intendance using a range of indicators.

Operationalizing the theoretical concept of quality by translating it into a set of quality indicators requires a articulate understanding of the purpose and context of measurement. Chapter 2 has introduced a five-lens framework for describing and classifying quality strategies. Several of these lenses are also useful for amend agreement the different aspects and contexts that demand to exist taken into business relationship when measuring healthcare quality. First, it is articulate that different indicators are needed to appraise the three dimensions of quality, i.e. effectiveness, safety and/or patient-centredness, because they relate to very different concepts, such as patient wellness, medical errors and patient satisfaction.

Secondly, quality measurement has to differ depending on the concerned function of the healthcare system, i.eastward. depending on whether i is aiming to measure quality in preventive, acute, chronic or palliative care. For example, changes in health outcomes due to preventive care will often exist measurable but afterward a long fourth dimension has elapsed, while they will be visible more quickly in the area of acute care. Thirdly, quality measurement will vary depending on the target of the quality measurement initiative, i.e. payers, provider organizations, professionals, technologies and/or patients. For example, in some contexts it might be useful to assess the quality of intendance received by all patients covered by unlike payer organizations (for instance, different wellness insurers or regions) but more often quality measurement volition focus on care provided by different provider organizations. In international comparisons, entire countries volition constitute another level or target of measurement.

In addition, operationalizing quality for measurement will always require a focus on a limited set of quality aspects for a detail grouping of patients. For example, quality measurement may focus on patients with hip fracture treated in hospitals and define aspects of care that are related to effectiveness (for example, surgery performed within 24 hours of admission), prophylactic (for example, anticoagulation to prevent thromboembolism), and/or patient-centredness of intendance (for example, patient was offered choice of spinal or general amazement) (Voeten et al., 2018). However, once more, the choice of indicators – and too potentially of different appraisal concepts for indicators used for the same quality aspects – will depend on the exact purpose of measurement.

3.3. Different purposes of quality measurement and users of quality data

It is useful to distinguish between two main purposes of quality measurement: The offset purpose is to use quality measurement in quality balls systems as a summative mec h anism for external accountability and verification. The second purpose is to use quality measurement as a formative mechanism for quality improvement. Depending on the purpose, quality measurement systems face different challenges with regard to indicators, data sources and the level of precision required.

Table 3.1 highlights the differences between quality assurance and quality improvement (Freeman, 2002; Gardner, Olney & Dickinson, 2018). Measurement for quality assurance and accountability is focused on identifying and overcoming problems with quality of care and assuring a sufficient level of quality across providers. Quality balls is the focus of many external assessment strategies (meet too Affiliate 8), and providers of bereft quality may ultimately lose their licence and be prohibited from providing care. Assuring accountability is i of the main purposes of public reporting initiatives (meet Chapter 13), and measured quality of care may contribute to trust in healthcare services and allow patients to cull higher-quality providers.

Table 3.1. The purpose of quality measurement: quality assurance versus quality improvement.

Tabular array 3.one

The purpose of quality measurement: quality balls versus quality comeback.

Quality measurement for quality balls and accountability makes summative judgements most the quality of care provided. The thought is that "existent" differences will be detected as a result of the measurement initiative. Therefore, a high level of precision is necessary and advanced statistical techniques may demand to be employed to make sure that detected differences between providers are "real" and attributable to provider functioning. Otherwise, measurement volition encounter significant justified resistance from providers because its potential consequences, such as losing the licence or losing patients to other providers, would be unfair. Appraisement concepts of indicators for quality balls will usually focus on assuring a minimum quality of intendance and identifying poor-quality providers. However, if the purpose is to incentivize high quality of care through pay for quality initiatives, the appraisal concept will likely focus on identifying providers delivering splendid quality of care.

Past contrast, measurement for quality improvement is alter oriented and quality information is used at the local level to promote continuous efforts of providers to ameliorate their performance. Indicators accept to be actionable and hence are ofttimes more procedure oriented. When used for quality improvement, quality measurement does not necessarily need to be perfect because it is merely informative. Other sources of information and local data are considered as well in society to provide context for measured quality of intendance. The results of quality measurement are only used to showtime discussions about quality differences and to motivate change in provider behaviour, for example, in audit and feedback initiatives (see Affiliate 10). Freeman (2002) sums upwardly the described differences between quality comeback and quality balls as follows: "Quality improvement models employ indicators to develop discussion further, assurance models apply them to foreclose it."

Different stakeholders in healthcare systems pursue different objectives and as a result they accept unlike information needs (Smith et al., 2009; EC, 2016). For example, governments and regulators are usually focused on quality assurance and accountability. They use related information generally to clinch that the quality of care provided to patients is of a sufficient level to avert harm – although they are conspicuously likewise interested in assuring a certain level of effectiveness. By contrast, providers and professionals are more interested in using quality data to enable quality improvement past identifying areas where they deviate from scientific standards or benchmarks, which point to possibilities for improvement (come across Chapter 10). Finally, patients and citizens may demand quality information in club to exist bodacious that acceptable health services will be available in instance of need and to be able to cull providers of skillful-quality care (run into Affiliate 13). The stakeholders and their purposes of quality measurement have, of grade, an important influence on the pick of indicators and information needs (run across below).

While the distinction between quality assurance and quality improvement is useful, the difference is not always clear-cutting. Offset, from a societal perspective, quality assurance aims at stamping out poor-quality care and thus contributes to improving average quality of care. Secondly, proponents of several of the strategies that are included under quality assurance in Table 3.i, such equally external assessment (see also Chapter 8) or public reporting (see also Chapter 13), in fact merits that these strategies do contribute to improving quality of care and assuring public trust in healthcare services. In fact, equally pointed out in the relevant chapters, the rationale of external assessment and public reporting is that these strategies will lead to changes inside organizations that volition ultimately contribute to improving quality of care. Conspicuously, there likewise need to be incentives and/or motivations for change, i.e. while internal quality improvement processes frequently rely on professionalism, external accountability mechanisms seek to motivate through external incentives and disincentives – just this is beyond the scope of this chapter.

3.4. Types of quality indicators

In that location are many options for classifying different types of quality indicators (Mainz, 2003). 1 option is to distinguish between rate-based indicators and elementary count-based indicators, ordinarily used for rare "sentinel" events. Rate-based indicators are the more common course of indicators. They are expressed as proportions or rates with clearly divers numerators and denominators, for case, the proportion of hip fracture patients who receive antibiotic prophylaxis before surgery. Count-based indicators are often used for operationalizing the safety dimension of quality and they identify individual events that are intrinsically undesirable. Examples include "never events", such as a foreign trunk left in during surgery or surgery on the wrong side of the body. If the measurement purpose is quality improvement, each individual outcome would trigger further analysis and investigation to avoid similar problems in the time to come.

Another option is to distinguish between generic and disease-specific indicators. Generic indicators measure aspects of care that are relevant to all patients. One example of a generic indicator is the proportion of patients who waited more than six hours in the emergency section. Affliction-specific indicators are relevant merely for patients with a particular diagnosis, such equally the proportion of patients with lung cancer who are alive 30 days after surgery.

Yet other options relate to the dissimilar lenses of the framework presented in Affiliate 2. Indicators can be classified depending on the dimension of quality that they assess, i.due east. effectiveness, patient safety and/or patient-centredness (the first lens); and with regard to the assessed function of healthcare, i.e. prevention, astute, chronic and/or palliative care (the second lens). Furthermore, it is possible to distinguish between patient-based indicators and issue-based indicators. Patient-based indicators are indicators that are developed based on data that are linked beyond settings, allowing the identification of the pathway of care provided to private patients. Event-based indicators are related to a specific event, for instance, a infirmary admission.

However, the most frequently used framework for distinguishing betwixt dissimilar types of quality indicators is Donabedian's nomenclature of structure, procedure and outcome indicators (Donabedian, 1980). Donabedian's triad builds the fourth lens of the framework presented in Chapter 2. The idea is that the structures where health care is provided have an effect on the processes of care, which in turn volition influence patient wellness outcomes. Table 3.ii provides some examples of structure, process and outcome indicators related to the different dimensions of quality.

Table 3.2. Examples of structure, process and outcome quality indicators for different dimensions of quality.

Table 3.ii

Examples of construction, procedure and outcome quality indicators for different dimensions of quality.

In general, structural quality indicators are used to assess the setting of intendance, such as the capability of facilities and equipment, staffing ratios, qualifications of medical staff and administrative structures. Structural indicators related to effectiveness include the availability of staff with an appropriate skill mix, while the availability of safe medicines and the volume of surgeries performed are considered to be more related to patient safe. Structural indicators for patient-centredness can include the organizational implementation of a patients' rights lease or the availability of patient information. Although institutional structures are certainly important for providing high-quality care, information technology is often difficult to establish a articulate link between structures and clinical processes or outcomes, which reduces, to a certain extent, the relevance of structural measures.

Process indicators are used to appraise whether actions indicating high-quality intendance are undertaken during service provision. Ideally, process indicators are congenital on reliable scientific bear witness that compliance with these indicators is related to improve outcomes of care. Sometimes process indicators are adult on the footing of clinical guidelines (see as well Chapter nine) or some other gold standard. For case, a process indicator of effective care for AMI patients may assess if patients are given aspirin on arrival. A procedure indicator of safety in surgery may assess if a safety checklist is used during surgery, and process indicators for patient-centredness may analyse patient-reported experience measures (PREMs). Process measures account for the majority of most quality measurement frameworks (Cheng et al., 2014; Fujita, Moles & Chen, 2018; NQF, 2019a).

Finally, issue indicators provide information about whether healthcare services aid people stay alive and good for you. Outcome indicators are commonly concrete and highly relevant to patients. For case, effect indicators of effective ambulatory care include hospitalization rates for preventable weather. Indicators of effective inpatient care for patients with acute myocardial infarction ofttimes include mortality rates within 30 days afterward access, preferably calculated as a patient-based indicator (i.e. capturing deaths in whatsoever setting outside the hospital) and non as an event-based indicator (i.e. capturing death only within the hospital). Outcome indicators of patient safe may include complications of treatment, such as hospital acquired infections or foreign bodies left in during surgery. Outcome indicators of patient-centredness may assess patient satisfaction or patients' willingness to recommend the infirmary. Outcome indicators are increasingly used in quality measurement programmes, in item in the USA, because they are of greater interest to patients and payers (Baker & Chassin, 2017).

3.5. Advantages and disadvantages of different types of indicators

Different types of indicators accept their various strengths and weaknesses:

  • Generic indicators have the advantage that they assess aspects of healthcare quality that are relevant to all patients. Therefore, generic indicators are potentially meaningful for a greater audience of patients, payers and policy-makers.

  • Disease-specific indicators are better able to capture different aspects of healthcare quality that are relevant for improving patient care. In fact, most aspects of healthcare quality are illness-specific because effectiveness, prophylactic and patient-centredness mean dissimilar things for different groups of diseases. For example, prescribing aspirin at belch is an indicator of providing effective care for patients after acute myocardial infarction. However, if older patients are prescribed aspirin for extended periods of time without receiving gastro-protective medicines, this is an indicator of safety bug in primary care (NHS BSA, 2019).

Likewise, construction, process and outcome indicators each have their comparative strengths and weaknesses. These are summarized in Tabular array 3.3. The strength of structural measures is that they are easily available, reportable and verifiable because structures are stable and easy to observe. However, the master weakness is that the link between structures and clinical processes or outcomes is often indirect and dependent on the actions of healthcare providers.

Table 3.3. Strengths and weaknesses of different types of indicators.

Table iii.three

Strengths and weaknesses of different types of indicators.

Process indicators are also measured relatively easily, and estimation is ofttimes straightforward considering at that place is oft no need for risk-adjustment. In addition, poor performance on procedure indicators can exist directly attributed to the actions of providers, thus giving clear indication for comeback, for example, by meliorate adherence to clinical guidelines (Rubin, Pronovost & Diette, 2001). Withal, healthcare is complex and process indicators usually focus only on very specific procedures for a specific grouping of patients. Therefore, hundreds of indicators are needed to enable a comprehensive analysis of the quality of care provided by a professional or an institution. Relying merely on a small ready of procedure indicators carries the risk of distorting service provision towards a focus on measured areas of intendance while disregarding other (potentially more) of import tasks that are harder to monitor.

Consequence indicators identify the focus of quality assessments on the actual goals of service provision. Result indicators are oftentimes more meaningful to patients and policy-makers. The utilize of result indicators may also encourage innovations in service provision if these lead to better outcomes than following established processes of intendance. However, attributing health outcomes to the services provided by private organizations or professionals is oft difficult considering outcomes are influenced past many factors outside the control of a provider (Lilford et al., 2004). In addition, outcomes may require a long time before they manifest themselves, which makes result measures more than difficult to use for quality measurement (Donabedian, 1980). Furthermore, poor operation on effect indicators does non necessarily provide directly indication for activity as the outcomes may exist related to a range of actions of unlike individuals who worked in a particular setting at a prior point in time.

3.6. Aggregating data in composite indicators

Given the complication of healthcare provision and the wide range of relevant quality aspects, many quality measurement systems produce a large number of quality indicators. However, the availability of numerous different indicators may make it difficult for patients to select the best providers for their needs and for policy-makers to know whether overall quality of healthcare provision is improving. In addition, purchasers may struggle with identifying practiced-quality providers if they practise not take a metric for accumulation conflicting results from different indicators. As a result, some users of quality data might base their decisions on merely a few selected indicators that they understand, although these may not be the most important ones, and the information provided by many other relevant indicators will exist lost (Goddard & Jacobs, 2009).

In response to these bug, many quality measurement initiatives have developed methods for combining different indicators into blended indicators or composite scores (Shwartz, Restuccia & Rosen, 2015). The use of blended indicators allows the assemblage of unlike aspects of quality into one measure out to give a clearer picture show of the overall quality of healthcare providers. The advantage is that the indicator summarizes data from a potentially broad range of private indicators, thus providing a comprehensive assessment of quality. Blended indicators tin serve many purposes: patients tin can select providers based on blended scores; hospital managers tin can use composite indicators to benchmark their hospitals confronting others, policy-makers can use composite indicators to assess progress over time, and researchers can use composite indicators for further analyses, for example, to identify factors associated with good quality of care. Tabular array iii.iv summarizes some of the advantages and disadvantages of composite indicators.

Table 3.4. Advantages and disadvantages of composite indicators.

Table 3.4

Advantages and disadvantages of composite indicators.

The principal disadvantages of composite indicators include that there are dissimilar (valid) options for accumulation individual indicators into composite indicators and that the methodological choices made during indicator construction will influence the measured functioning. In addition, composite indicators may lead to simplistic conclusions and disguise serious failings in some dimensions. Furthermore, because of the influence of methodological choices on results, the option of constituting indicators and weights could become the subject of political dispute. Finally, composite indicators practise non allow the identification of specific trouble areas and thus they need to exist used in conjunction with individual quality indicators in order to enable quality comeback.

In that location are at to the lowest degree iii of import methodological choices that accept to exist made to construct a blended indicator. Get-go, individual indicators have to be called to be combined in the composite indicator. Of class, the selection of indicators and the quality of chosen indicators volition be decisive for the reliability of the overall blended indicator. Secondly, individual indicators take to exist transformed into a common scale to enable aggregation. There are many methods available for this rescaling of the results, including ranking, normalizing (for example, using z-scores), calculating the proportion of the range of scores, and grouping scores into categories (for example, 5 stars) (Shwartz, Restuccia & Rosen, 2015). All of these methods have their comparative advantages and disadvantages and there is no consensus almost which ane should be used for the structure of composite indicators.

Thirdly, weights take to be fastened to the individual indicators, which signal the relative importance of the different components of the blended indicator. Potentially, the ranking of providers can change dramatically depending on the weights given to individual indicators (Goddard & Jacobs, 2009). Once more, several options exist. The most straightforward manner is to use equal weights for every indicator but this is unlikely to reverberate the relative importance of individual measures. Another choice is to base of operations the weights on expert judgement or preferences of the target audience. Further options include opportunity-based weighting, too called denominator-based weights because more weight is given to indicators for more than prevalent atmospheric condition (for case, higher weights for diabetes-related indicators than for acromegaly-related indicators), and numerator-based weights which requite more weight to indicators covering a larger number of events (for example, higher weight on medication interaction than on incorrect-side surgery). Finally, yet another option is to employ an all-or-none approach at the patient level, where a score of one is given only if all requirements for an individual patient have been met (for case, all five recommended pre-operative processes were performed).

Once more, at that place is no clear guidance on how all-time to construct a composite indicator. However, what is of import is that indicator construction is transparent and that methodological choices and rationales are clearly explained to facilitate agreement. Furthermore, different choices volition provide different incentives for improvement and these need to be considered during composite construction.

3.7. Selection of indicators

A wide range of existing indicators is available that tin grade the basis for the development of new quality measurement initiatives. For example, the National Quality Forum (NQF) in the USA provides an online database with more than than a thousand quality indicators that can be searched by type of indicator (structure, process, issue), by clinical area (for example, dental, cancer or heart care), by target of measurement (for example, provider, payer, population), and by endorsement status (i.e. whether they meet the NQF's measure evaluation criteria) (NQF, 2019a). The OECD Health Intendance Quality Indicator Projection provides a list of 55 quality indicators for cross-country analyses of the quality of primary care, acute care and mental care, as well equally patient prophylactic and patient experiences (OECD HCQI, 2016). The Australian Commission on Safety and Quality in Health Care has adult a wide set of indicators for hospitals, primary intendance, patient safety and patient experience, among others (ACSQHC, 2019).

The English language Quality and Outcomes Framework (QOF) includes 77 indicators for evaluating the quality of chief care (NHS Employers, 2018), and these indicators take inspired several other countries to develop their own quality indicators for primary care. The NHS also publishes indicators for the assessment of medication safety (NHS BSA, 2019). In add-on, several contempo reviews have summarized available quality indicators for unlike areas of care, for example, palliative care (Pfaff & Markaki, 2017), mental health (Parameswaran, Spaeth-Rublee & Alan Pincus, 2015), master intendance for patients with serious mental illnesses (Kronenberg et al., 2017), cardiovascular care (Campbell et al., 2008), and for responsible utilize of medicines (Fujita, Moles & Chen, 2018). Different capacity in this book will refer to indicators as role of specific quality strategies such equally public reporting (see Chapter xiii).

In fact, there is a plethora of indicators that can potentially exist used for measurement for the various purposes described previously (see section higher up: Different purposes of quality measurement and users of quality information). Nonetheless, because information collection and analysis may eat considerable resources, and considering quality measurement may accept unintended consequences, initiatives have to carefully select (or newly develop) indicators based on the identified quality problem, the interested stakeholders and the purpose of measurement (Evans et al., 2009).

Quality measurement that aims to monitor and/or accost problems related to specific diseases, for case, cardiovascular or gastrointestinal diseases, or detail groups of patients, for example, geriatric patients or paediatric patients, will probable require disease-specific indicators. By contrast, quality measurement aiming to accost issues related to the organization of care (for case, waiting times in emergency departments), to specific providers (for example, falls during inpatient stays), or professionals (for example, insufficiently qualified personnel) will likely require generic indicators. Quality bug related to the effectiveness of intendance are likely to require rate-based disease-specific indicators, while safe problems are more than likely to be addressed through (often generic) lookout man event indicators. Problems with regard to patient-centredness volition probable crave indicators based on patient surveys and expressed as rates.

The interested stakeholders and the purpose of measurement should determine the desired level of particular and the focus of measurement on structures, processes or outcomes. This is illustrated in Tabular array three.5, which summarizes the information needs of different stakeholders in relation to their different purposes. For example, governments responsible for assuring overall quality and accountability of healthcare service provision will require relatively few aggregated composite indicators, mostly of health outcomes, to monitor overall system level functioning and to assure value for money. By contrast, provider organizations and professionals, which are mostly interested in quality improvement, are likely to demand a high number of affliction-specific process indicators, which allows identification of areas for quality improvement.

Table 3.5. Information needs of health system stakeholders with regard to quality of care.

Tabular array iii.five

Information needs of health system stakeholders with regard to quality of care.

Another issue that needs to exist considered when choosing quality indicators is the question of finding the right residual between coverage and practicality. Relying on merely a few indicators causes some aspects of care quality to be neglected and potentially to distract attention abroad from non-measured areas. It may also exist necessary to have more than one indicator for ane quality aspect, for instance, mortality, readmissions and a PREM. However, maintaining too many indicators volition exist expensive and impractical to utilize. Finally, the quality of quality indicators should be a determining factor in selecting indicators for measurement.

3.eight. Quality of quality indicators

There are numerous guidelines and criteria available for evaluating the quality of quality indicators. In 2006 the OECD Health Care Quality Indicators Project published a list of criteria for the pick of quality indicators (Kelley & Hurst, 2006). A relatively widely used tool for the evaluation of quality indicators has been developed at the University of Amsterdam, the Appraisement of Indicators through Research and Evaluation (AIRE) instrument (de Koning, Burgers & Klazinga, 2007). The NQF in the U.s. has published its mensurate evaluation criteria, which form the basis for evaluations of the eligibility of quality indicators for endorsement (NQF, 2019b). In Germany still another tool for the assessment of quality indicators – the QUALIFY instrument – was developed by the Federal Role for Quality Balls (BQS) in 2007, and the Plant for Quality Balls and Transparency in Health Care (IQTIG) defined a similar set of criteria in 2018 (IQTIG, 2018).

In general, the criteria defined by the different tools are quite similar but each tool adds certain aspects to the list. Box iii.1 summarizes the criteria divers by the various tools grouped forth the dimensions of relevance, scientific soundness, feasibility and meaningfulness. The relevance of an indicator can be determined based on its effect on health or health expenditures, the importance that it has for the relevant stakeholders, the potential for improvement (for example, as adamant by available evidence nigh practice variation), and the clarity of the purpose and the healthcare context for which the indicator was developed. The latter point is important because many of the following criteria are dependent on the specific purpose.

Box Icon

For example, the desired level for the criteria of validity, sensitivity and specificity will differ depending on whether the purpose is external quality assurance or internal quality improvement. Similarly, if the purpose is to assure a minimum level of quality across all providers, the appraisal concept has to focus on minimum acceptable requirements, while it will have to distinguish between expert and very good performers if the aim is to reward high-quality providers through a pay for quality arroyo (see Chapter 14).

Important aspects that need to be considered with regard to feasibility of measurement include whether previous experience exists with the use of the measure, whether the necessary information is available or can exist collected in the required timeframe, whether the costs of measurement are adequate, and whether the data will allow meaningful analyses for relevant subgroups of the population (for instance, by socioeconomic status). Furthermore, the meaningfulness of the indicator is an of import criterion, i.due east. whether the indicator allows useful comparisons, whether the results are user-friendly for the target audience, and whether the distinction between high and low quality is meaningful for the target audience.

3.nine. Data sources for measuring quality

Many unlike kinds of data are available that tin potentially be used for quality measurement. The nearly often used data sources are authoritative information, medical records of providers and data stored in different – often disease-specific – registers, such as cancer registers. In addition, surveys of patients or healthcare personnel can be useful to gain additional insights into particular dimensions of quality. Finally, other approaches, such as direct observation of a doc'due south activities past a qualified colleague, are useful under specific conditions (for example, in a enquiry context) but usually not possible for continuous measurement of quality.

There are many challenges with regard to the quality of the available data. These challenges tin can be categorized into four fundamental aspects: (1) completeness, (2) comprehensiveness, (3) validity and (four) timeliness. Completeness means that the data properly include all patients with no missing cases. Comprehensiveness refers to whether the information contain all relevant variables needed for analysis, such as diagnosis codes, results of laboratory tests or procedures performed. Validity means that the data accurately reflect reality and are costless of bias and errors. Finally, timeliness means that the data are available for use without considerable delay.

Data sources differ in their attributes and have different strengths and weaknesses, which are presented beneath and summarized in Table three.vi. The availability of data for enquiry and quality measurement purposes differs substantially between countries. Some countries have more restrictive data privacy protection legislation in place, and also the possibility of linking unlike databases using unique personal identifiers is non bachelor in all countries (Oderkirk, 2013; Mainz, Hess & Johnsen, 2019). Healthcare providers may too use patient data only for internal quality improvement purposes and prohibit transfer of data to external bodies. Nonetheless, with the increasing improvidence of IT technology in the form of electronic health records, authoritative databases and clinical registries, opportunities of data linkage are increasing, potentially creating new and better options for quality measurement.

Table 3.6. Strengths and weaknesses of different data sources.

Table 3.6

Strengths and weaknesses of dissimilar data sources.

3.9.ane. Administrative data

Administrative data are not primarily generated for quality or enquiry purposes but by definition for administrative and management purposes (for case, billing data, routine documentation) and have the reward of being readily available and hands accessible in electronic form. Healthcare providers, in particular hospitals, are usually mandated to maintain authoritative records, which are used in many countries for quality measurement purposes. In addition, governments usually have registers of births and deaths that are potentially relevant for quality measurement but which are often not used by existing measurement systems.

Authoritative discharge data from hospitals usually include a patient identifier, demographic information, primary and secondary diagnoses coded using the International Classification of Diseases (ICD), coded information about medical and surgical procedures, dates of services provided, provider identifiers and many other bits of data (Iezzoni, 2009).

However, more detailed clinical information on severity of disease (for example, available from lab test results) or data about functional harm or quality of life are not available in administrative data. The strength of administrative information is that they are comprehensive and complete with few problems of missing information. The about important problem of authoritative data is that they are generated by healthcare providers, usually for payment purposes. This ways that coding may be influenced by the incentives of the payment system, and – once used for purposes of quality measurement – as well by incentives attached to the measured quality of intendance.

3.9.ii. Medical record data

Medical records incorporate the about in-depth clinical data and document the patient's status or trouble, tests and treatments received and follow-upwardly intendance. The abyss of medical record information varies greatly between and inside countries and healthcare providers. Especially in principal care where the GP is familiar with the patient, proper documentation is often defective. Too, if the patient changes provider during the handling procedure and each provider keeps their own medical records, the different records would demand to be combined to get a complete picture of the procedure (Steinwachs & Hughes, 2008).

Abstracting information from medical records can be expensive and time-consuming since medical records are rarely standardized. Another important aspect is to make sure that the information from medical records is gathered in a systematic way to avert information bias. This tin can be done past defining clinical variables explicitly, writing detailed brainchild guidelines and grooming staff to maintain data quality. Medical tape review is used mostly in internal quality comeback initiatives and inquiry studies.

With the growth of electronic medical and electronic health records, the use of this information for more than systematic quality measurement will likely increment in the future. The potential benefits of using electronic records are considerable as this may allow real-time routine analysis of the well-nigh detailed clinical data available, including information from imaging tests, prescriptions and pathology systems (Kannan et al., 2017). However, it volition be necessary to address persisting challenges with regard to accuracy, abyss and comparability of the information collected in electronic records to enable reliable measurement of quality of care on the basis of this data (Chan et al., 2010).

3.ix.three. Disease-specific registries

At that place are many affliction-specific registries containing information that can be used for healthcare quality measurement purposes. Cancer registries exist in virtually adult countries and, while their chief purpose is to register cancer cases and provide information on cancer incidence in their catchment area, the data can also exist used for monitoring and evaluation of screening programmes and estimating cancer survival by follow-upward of cancer patients (Bray & Parkin, 2009). In Scandinavian countries significant efforts have gone into standardizing cancer registries to enable cross-country comparability. Yet, numerous differences persist with regard to registration routines and classification systems, which are important when comparing fourth dimension trends in the Nordic countries (Pukkala et al., 2018).

In some countries at that place is a big number of clinical registries that are used for quality measurement. For case, in Sweden there are over a hundred clinical quality registries, which piece of work on a voluntary basis as all patients must be informed and have the right to opt-out. These registries are mainly for specific diseases and they include illness-specific data, such as severity of disease at diagnosis, diagnostics and treatment, laboratory tests, patient-reported issue measures, and other relevant factors such as trunk mass index, smoking status or medication. Most of the clinical registries focus on specialized care and are based on reporting from hospitals or specialized day intendance centres (Emilsson et al., 2015).

With increasing diffusion of electronic health records, it is possible to generate and feed affliction-specific population registries based on electronic abstraction (Kannan et al., 2017). Potentially, this may significantly reduce the costs of data collection for registries. Furthermore, linking of data from different registries with other authoritative data sources tin can increasingly be used to generate datasets that enable more profound analyses.

3.9.4. Survey information

Survey data are another widely used source of quality data. Surveys are the only option for gaining information about patient experiences with healthcare services and thus are an of import source of information about patient-centredness of intendance. Substantial progress has been made over recent years to improve standardization of both patient-reported experience measures (PREMs) and patient-reported event measures (PROMs) in order to facilitate international comparability (Fujisawa & Klazinga, 2017).

Surveys of patient experiences capture the patients' views on health service delivery (for instance, communication with nurses and doctors, staff responsiveness, discharge and intendance coordination). Most OECD countries have developed at least 1 national survey measuring PREMs over the by decade or so (Fujisawa & Klazinga, 2017), and efforts are under style to further increase cooperation and collaboration to facilitate comparability (OECD, 2017).

Surveys of patient-reported outcomes capture the patient's perspective on their wellness status (for instance, symptoms, functioning, mental wellness). PROMs surveys can utilise generic tools (for instance, the SF-36 or EQ-5D) or illness-specific tools, which are commonly more sensitive to modify (Fitzpatrick, 2009). The NHS in the United Kingdom requires all providers to report PROMs for two elective procedures: hip replacement and articulatio genus replacement. Both generic (EQ-5D and EQ VAS) and disease-specific (Oxford Hip Score, Oxford Knee Score and Aberdeen Varicose Vein Questionnaire) instruments are used (NHS Digital, 2019b).

Finally, several countries also use surveys of patient satisfaction in lodge to monitor provider operation. However, satisfaction is difficult to compare internationally because information technology is influenced by patients' expectations nigh how they will be treated, which vary widely across countries and likewise inside countries (Busse, 2012).

3.9.5. Directly observation

Direct observation is sometimes used for inquiry purposes or equally part of peer-review processes. Directly observation allows the study of clinical processes, such equally the adherence to clinical guidelines and the availability of basic structures. Ascertainment is normally considered to be also resource-intensive for continuous quality measurement. Notwithstanding, site visits and peer-reviews are often added to routine monitoring of secondary (administrative) information to investigate providers with unexplained variation in quality and to meliorate empathize the context where these data are produced.

3.10. Attribution and risk-aligning

Two further conceptual and methodological considerations are essential when embarking on quality measurement or making use of quality data, in particular with regard to outcome indicators. Both are related to the question of responsibleness for differences in measured quality of care or, in other words, related to the question of attributing causality to responsible agents (Terris & Aron, 2009). Ideally, quality measurement is based on indicators that have been purposefully developed to reflect the quality of care provided past individuals, teams, provider organizations (for example, hospitals) or other units of analysis (for example, networks, regions, countries) (see also higher up, Quality of quality indicators). Notwithstanding, many existing quality indicators practice not reverberate only the quality of care provided by the target of measurement but also a host of factors that are outside the direct command of an individual provider or provider system.

For example, surgeon-specific bloodshed information for patients undergoing coronary artery bypass graft (CABG) accept been publicly reported in England and several states of the USA for many years (Radford et al., 2015; Romano et al., 2011). Yet contend continues whether results actually reflect the private surgeon's quality of care or rather the quality of the wider hospital squad (for example, including anaesthesia, intensive intendance unit quality) or the system and direction of the hospital (for example, the system of resuscitation teams within hospitals) (Westaby et al., 2015). Nevertheless, with data released at the level of the surgeon, responsibility is publicly attributed to the individual and not to the system.

Other examples where attributing causality and responsibility is difficult include outcome indicators defined using time periods (for example, xxx-day bloodshed after hospitalization for ischemic stroke) because patients may be transferred betwixt different providers and because measured quality will depend on intendance received after belch. Similarly, attribution can be problematic for patients with chronic weather, for example, attributing causality for hospitalizations of patients with heart failure – a quality indicator in the United states – is difficult because these patients may meet numerous providers, such equally ane (or more) primary care physician(s) and specialists, for example, nephrologists and/or cardiologists.

What these examples illustrate is that attribution of quality differences to providers is difficult. Withal, it is important to accurately attribute causality because it is unfair to concord individuals or organizations answerable for factors outside their control. In addition, if responsibility is attributed incorrectly, quality improvement measures volition be in vain, as they will miss the appropriate target. Therefore, when developing quality indicators, information technology is important that a causal pathway tin be established betwixt the agents under assessment and the outcome proposed as a quality measure. Furthermore, possible confounders, such as the influence of other providers or college levels of the healthcare organisation on the outcome of interest, should be carefully explored in collaboration with relevant stakeholders (Terris & Aron, 2009).

Of class, many of import confounders outside the command of providers have not however been mentioned as the most important confounders are patient-level clinical factors and patient preferences. Prevalence of these factors may differ across patient populations and influence the outcomes of intendance. For example, severely ill patients or patients with multiple coexisting conditions are at risk of having worse outcomes than healthy individuals despite receiving high-quality intendance. Therefore, providers treating sicker patients are at risk of performing poorly on measured quality of care, in item when measured through outcome indicators.

Risk-adjustment (sometimes called case-mix adjustment) aims to control for these differences (take chances-factors) that would otherwise lead to biased results. About all outcome indicators crave risk-adjustment to adjust for patient-level gamble factors that are outside the control of providers. In addition, healthcare processes may be influenced past patients' attitudes and perceptions, which should be taken into business relationship for risk-adjustment of process indicators if relevant. Ideally, risk-adjustment assures that measured differences in the quality of intendance are not biased by differences in the underlying patient populations treated by different providers or in different regions.

An overview of potential patient (risk-) factors that may influence outcomes of intendance is presented in Table 3.7. Demographic characteristics (for instance, age), clinical (for example, co-morbidities) and socioeconomic factors, health-related behaviours (for example, alcohol use, nutrition) and attitudes may potentially have an result on outcomes of care. Past controlling for these factors, risk-adjustment methods will produce estimates that are better comparable beyond individuals, provider organizations or other units of assay.

Table 3.7. Potential patient risk-factors.

The field of take a chance-adjustment is developing rapidly and increasingly sophisticated methods are bachelor for ensuring fair comparisons across providers, peculiarly for weather condition involving surgery, risk of death and mail-operative complications (Iezzoni, 2009). Presentation of specific risk-adjustment methods is across the scope of this chapter but some full general methods include straight and indirect standardization, multiple regression analysis and other statistical techniques. The selection of potential confounding factors needs to exist done carefully, taking into account the ultimate purpose and utilize of the quality indicator that needs aligning.

In fact, the pick of risk-adjustment factors is not a purely technical exercise but relies on assumptions that are often not clearly spelled out. For case, in several countries the infirmary readmission rate is used equally a quality indicator in pay for quality programmes (Kristensen, Bech & Quentin, 2015). If information technology is believed that age influences readmission rates in a way hospitals cannot affect, historic period should exist included in the risk-adjustment formula. However, if information technology is thought that hospitals can influence elderly patients' readmission rates by special discharge programmes for the elderly, historic period may non exist considered a "take a chance" only rather an indicator for the hospitals to use for identifying patients with special needs. The aforementioned arguments use also for socioeconomic condition. On the one hand, there are good reasons to adjust for socioeconomic variables because patients living in poorer neighbourhoods tend to take higher readmission rates. On the other paw, including socioeconomic variables in a risk-aligning formula would implicitly mean that information technology was adequate for hospitals located in poorer areas to have more readmissions.

The assumptions and methodological choices made when selecting variables for risk-adjustment may have a powerful effect on chance-adjusted measured quality of care. Some critics (for example, Lilford et al., 2004) have argued that comparative outcome data should not be used externally to make judgements about quality of hospital intendance. More recent criticism of risk-aligning methods has suggested that take chances-adjustment methods of current quality measurement systems could exist evaluated by assigning ranks similar to those used to charge per unit the quality of evidence (Braithwaite, 2018). Accordingly, A-level risk-adjustment would adjust for all known causes of negative consequences that are across the control of clinicians even so influence outcomes. C-level risk-adjustment would fail to control for several of import factors that cause negative consequences, while B-level risk-adjustment would exist somewhere in betwixt.

3.xi. Decision

This chapter has introduced some basic concepts and methods for the measurement of healthcare quality and presented a number of related challenges. Many different stakeholders have varying needs for information on healthcare quality and the development of quality measurement systems should e'er have into account the purpose of measurement and the needs of different stakeholders. Quality measurement is important for quality assurance and accountability to make certain that providers are delivering good-quality care but they are also vital for quality improvement programmes to ensure that these interventions lead to increases in care quality.

The development and use of quality measures should always be fit-for-purpose. For example, outcome-based quality indicators, such as those used by the OECD, are useful for international comparisons or national agenda-setting simply providers such as hospitals or wellness centres may need more specific indicators related to processes of care in society to enable quality improvement. The Donabedian framework of construction, process and effect indicators provides a comprehensive, easily understandable model for classifying different types of indicator, and it has guided indicator development of nearly existing quality measurement systems.

Quality indicators should exist of high quality and should be carefully chosen and implemented in cooperation with providers and clinicians. The increasing availability of clinical data in the course of electronic health records is multiplying possibilities for quality measurement on the ground of more detailed indicators. In addition, take a chance-aligning is important to avoid loftier-quality providers existence incorrectly and unfairly identified as providing poor quality of care – and vice versa, to avoid that poor providers appear to be providing good quality of care. Again, the increasing availability of data from electronic medical records may expand the options for ameliorate gamble-adjustment.

Notwithstanding, almost quality measurement initiatives volition keep to focus – for reasons of practicality and information availability – just on a limited set of quality indicators. This means that 1 of the fundamental risks of quality measurement will continue to be important: quality measurement will always direct attention to those areas that are covered past quality indicators, potentially at the expense of other important aspects of quality that are more difficult to assess through quality measurement.

Nevertheless, without quality information policy-makers lack the knowledge base to steer health systems, patients can only rely on personal experiences or those of friends for choosing healthcare providers, and healthcare providers accept no fashion of knowing whether their quality comeback programmes have worked as expected.

Quality data is a tool and it can do serious damage if used inappropriately. Seven basic principles of using quality indicators are summarized in Box iii.2. Information technology is critical to be aware of the limitations of quality measurement and to be cautious of using quality data for quality strategies that provide powerful incentives to providers, such equally public reporting (see Chapter thirteen) or P4Q schemes (see Chapter 14), as these may atomic number 82 to potential unintended consequences such as gaming or patient selection.

Box Icon

Box 3.2

Seven principles to accept into account when using quality indicators.

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Source: https://www.ncbi.nlm.nih.gov/books/NBK549260/