Background: Outcome measurement i s essenti al to understand the impact of clinical interventions and the performance of services. Despite national and professional body encouragement, and successful examples of system level outcome measurement within some health care settings, many barrier s still e xist preventing outcome measurement from becoming embedded in clinical practice. Objectives: To explore the status of outcome measurement in prosthetic rehabilitation, as applied in clinical practice, with a view to identifying areas of future work aim ed at making outcome measurement in prosthetic rehabilitation a meaningful and useful reality. Study Design: Narrative review Methods: A literature search of four databases was undertaken, following the PRISMA principals appropriate to narrativ e reviews , and using the search terms outcome, measure*, tool, scale, instrument, prosthe*, amput* and limb loss. A total of 1116 papers were identified. Following screening 35 papers, focusing on four main themes, were included in the review. Results: The four t hemes were 1) What outcomes should be measured? 2) How can these outcomes be measured? 3) What are the barriers to outcome measurement? and 4) What examples of routine outcome measurement currently exist in prosthetic practice? Conclusions: Succes sful outc ome measurement in clinical practice is multifaceted. Understanding and embedding value at every step is key to success. Addressing the questions of `why’, `what’ and `how’ we measure outcome may move us closer to a consensus. Routine outcome meas urement a t the clinical level should ensure data collection is valuable to clinical practice, makes use of IT solutions and has all important organisational buy in. Key words: Outcome measurement, Prosthetic limbs, Amputation, R ehabilitation, Meaningful measureme nt, Clinical practice 2 Background Measuring health outcomes is especially relevant in today’s increasingly evidence based health services. When facing increasing demands on resources, as well as the expectation of a culture of continual improvement, serv ices need to consistently demonstrate their value and impact.1 Outcome information can provide an understanding of the impact health services and interventions have on the health and wellbeing of patients. The term outc ome measurement can be better understood by breaking it down into the outcome being measured and the measurement tool used for the task. A health outcome can be defined as a change in a person’s health status as a result of an intervention2 and a measurement tool as a standardised instrument used in research and clinical practice to capture and evaluate this change.3 Within clinical practice, outcom e measurement in the form of observed performance measures or patient reported measures (PROMs), can be used in a number of different ways. On an individual basis, the use of an outcome measure (OM) can be helpful to capture changes in a patient’s status f ollowing an intervention or when monitoring patients over time.3 This information can be shared with t he patient to demonstrate progress throughout rehabilitation, and increase motivation, or can be used by the clinician to direct treatment planning or inform funding requests.4 A more co -ordinated approach to outcome measure ment along a pathway of care can be described as routine outcome measurement (ROM). ROM is define d as “the systematic use of a standardised OM(s) in clinical practice with every patient as part of a standardised assessment practice guideline”.5 This systematic approach to outcome measurement can provide individual services with a wealth of information on the quality of care and interventions they are delivering.6 Data can be used to direct and inform improvement work, and evaluate the changes resulting from new ways of working. When co -ordinated ROM is undertaken in a number of services, benchmarking c an be undertaken7 and a system -wide appr oach adopted. This system -level approach to ROM can be useful in several contexts, especially if widely accessible. It allows comparison of the performance o f different health care providers and gives patients valuable information on the best performing trusts, enabling informed decisions on where to receive care.1 Benchmarking can also help to identify services where outcom es are good and use those as exemplars to raise standards across the NHS, reducing variation in t he system. Collating large datasets concerning a specific population, such as lower limb amputation, can also be useful for research purposes to strive to unde rstand, and influence, the factors that may have an impact on clinical outcome. Outcome measur ement is crucial to inform and therefore improve clinical decision -making daily. If health care services want to ensure they are delivering the most effective care they need information, specifically about the outcome of care, that can both direct the focu s of meaningful improvement programmes and evaluate their impact.8 The potential for using ROM to evaluate, inform and improve clinical practice at the system level has been realised in several UK clinical settings. Two notable examples are the NHS PROMs programme and the UK Rehabilitation Outcomes Collab orative (UKROC). The NHS England PROMs programme,9 collects PROMs data for hip and knee replacements, before and after surgery. Data is collected nationally and allows comparison of surgical centres and informed patient choic e of centres with the best outcomes. Crucially this programme is linked to the National Joint Registry (NJR) which collects implant safety data from across the UK on all joint replacement surgery.10 Combining thes e data sets allows the value of joint replacement on patients’ lives to be assessed, as well as a comparison of the performance of different implant componentry, providing evidence for interventions, service performance, patient safety and component select ion.11 3 UKROC applies system -wide ROM in the context of rehabilitation and aims to provide data that can inform the provision of cost effective neuro -rehabilitation service s in the NHS.12 The collaborative developed a national clinical database combining data on rehabilitation needs, the interventions patients received and importantly their outcomes. Data collected was used to demonstrate that specialist neuro rehabili tation was a highly cost -effective intervention compared to potential lifelong care needs. This evidence led to significant investment in rehabilitation services throughout the NHS, alongside development of evidence based standards of care that have reduce d variation across the system and driven up quality.13 However, even within these valuable examples of system wide ROM, engaging clinicians in outcome measurement is a challenge,14 and it is widely acknowledged that OMs a re not routinely used in clinical practice.4 NHS policy15 as well as prosthetic rehabilitation professional bodies, such as the British Association of Prosthetists and Orthotists (BAPO) and the British Associations of Chartered Phys iotherapists in Amputation Rehabilitation (BACPAR), have issued encouragement and guidance on OMs in clinical services.16,17 However their use has yet to become embedded and there is currently no UK outcome data collection initiative capturing the inherent usefulness of this information following amputation.4 Despite the common view that it is due to ambivalence of c linicians18 and a resistan ce to change and innovation amongst service providers,19 a systematic review by Duncan and Murray exploring barriers and facilitators to OM use amongst a llied health professionals (AHPs) suggests this is not the case. Successful implementation ne eds multifactorial efforts, overcoming barriers such as lack of time, unfeasible OMs, perceived lack of value in measurement and insufficient organisational suppor t.4 This narrative review therefore seeks to explore ROM with in the field of prosthetic rehabilitation by critically synthesising the current evidence regarding outcome measurement, as applied in clinical practice. By unders tanding what is already known about this subject, in this context, we hope to highlight areas for future work aimed at making ROM a meaningful reality following lower limb amputation, that has the potential to evaluate and direct improvements in the care p rovided to patients. Methods Narrative approach A narrative approach was chosen for this r eview to allow for a broad exploration of the outcome literature within the prosthetic rehabilitation evidence base. This approach fits well with the narrative review methodology as it doesn’t stipulate formulation of a specific research question, as requi red for scoping or systematic reviews (SR).20 The narrative style also gives voice to the clinical narrative thread, which might be lost within the constraints of more systematic approaches.21 Search strategy A literature search was conducted using the CINAHL, Medline, Sci ence direct and PsychInfo bibliographic databases in July 2020, following the PRISMA principl es appropriate to narrative reviews, as set out by Ferrari.21 The key words outcome AND (measur* OR tool OR scale OR instrument) were combined with AND (prosthe* OR amput* OR “limb loss”) a nd searched in titles and abstracts. An English language filter was then applied. Search r esults 1116 papers published between 1981 and 2020 were identified and 777 citations were screened once duplicates were removed. 78 articles were selected for ful l text review alongside an additional 11 identified from manual searches of reference lists a nd grey literature sources. This review yielded a number of SRs (10) exploring the development and psychometric properties of OM tools for use following lower limb amputation. Studies of individual OMs were therefore excluded to avoid 4 duplication. In tota l 36 papers were included in the narrative review. The process is described in the flow diagram in fig 1. Figure 1. – Narrative review process flow chart 5 Results The findings from this search h ave been grouped into four main themes which have been posed as questions given the previousl y described issues regarding outcome measurement in the wider health care context. The four themes are 1) What outcomes should be measured? 2) How can these outcom es be measured? 3) What are the barriers to using OMs and 4) What examples of ROM currently e xist in prosthetic practice? 1) What outcomes should be measured? Deciding what is important to measure appears to be a significant challenge within the prostheti c rehabilitation literature. In 2014, a study by Heinemann et al.22 reported finding 43 unique measurement instruments designed to cap ture outcome following lower limb amputation. In light of a wide variety of tools measuring an extensive range of different outcome domains, a number of authors have raised the issue t hat there is no consensus around which OMs should be used.23,24 This is also evident in current UK Health Policy and professional guidance, where long lists of OMs are suggested in the Prosthetic National Service Specification,25 the NHS Microprocessor Knee Policy,26 the BACPAR OMs toolbox17 and the BAPO OM guidance.16 These recommendations offer too many options to feasibly use them all in clinical practice, and little consistency to aid consensus. This lack of consensus is a major barrier to system wide ROM and raises two important unanswered questions within the field of lower limb prosthetic rehabilitation: what outcomes should be measured and how should they be measured? What outcomes should be m easured will be discussed in this theme. How they should be measured will be addresse d in the theme exploring OMs. One approach identified in this review, is attempting to ascertain what to measure by exploring possible outcome constructs that capture the concept of health, as defined using a framework like the International Classificatio n of Functioning (ICF).27 This has been approached by Xu et al. who developed a core ICF set of domains which define health as it applies to people following amputation, with a view to exploring h ow these domains could be measured. Currently this work has yielded large numbers of constructs which may be impractical to capture in clinical practice. Importantly this approach does not give an indication of which constructs could be most meaningful to prosthetic users themselves, for whom the outcome of prosthetic rehab has the greates t impact. A more patient -centred approach to outcome measurement in prosthetics was called for in a review by Gallagher and Desmond in 2007 who suggested measuring qualit y of life may be an important first step in this process.28 Since then two studies have taken this patient centred approach fur ther by using qualitative methods to explore the outcomes that matter to patients. Mc Donald and colleagues29 used focus groups to explore meaningful outcomes when prescribing prosthetic feet, and Schaffalitzky et al30,31 explored user, clinician an d wider stakeholder views on the outcome of prosthetic prescription via interviews, focus groups and a Delphi consensus process. Bo th studies focused on the prescription of components rather than holistic rehabilitation post amputation, and McDonald et al. only included six participants, however they found that qualitative methodologies work well to explore successful prosthetic presc ription and highlighted outcome domains that were important to users such as balance and safety, independence, and importantl y the need to capture psychosocial outcomes.30 Despite this valuab le exploratory work consensus remains elusive in prosthetic rehabilitation and without understanding what to measure the next theme of how to measure it becomes even more challenging. 6 2) How can these outcomes be measured? Ten SRs were identified in the p rosthetic rehabilitation literature which collate and critique measurement tools designed for use following lower limb amputation ( Table 1). The many OMs included in these reviews vary widely. Measures focus on different outcome domains (i.e. function, fal ls risk, mobility, quality of life or prosthetic use), and vary in the methodological quality of their development and their psycho metric properties (i.e. the level of measurement, validity, reliability and responsiveness)32 The tools included also vary in length and format. Some are paper -based and others are available digitally via apps. Some measures are free to use whereas others have cost implications and this in itself can be a significant barrier in some healthcare settings. Many measure s have been developed in English and have now been translated and validated in other languages, which is important for use in today’s diverse society, and across the world. However, the challenges of using OMs in different languages is not just about trans lation. Ensuring the measure is meaningful in different social and cultural settings is often overlooked, i.e. measuring an i ndividual’s ability to stand from a chair may not be as meaningful in countries where much time is spent sitting or squatting at fl oor level. Considering these different approaches when attempting to make an informed choice of OM, as well as understanding and evaluating the many different psychometric properties, can make selecting tools almost unnavigable for clinicians. Despite the many measures available the quest for new measures continues and the future is focused on the digital arena. The PLUS -M OM, or item bank, consists of 44 different questions exploring the outcome construct of mobility. Each item is individually validated and calibrated on a single scale and the items can be used to develop specific short forms or be administered via Computer Ad aptive Testing (CAT).39 CAT uses algorithms to select items from the bank for individual patients based on their responses to previous questions.40 This tailored approac h has been able to achieve the same reliability with fewer questions therefore reducing the measurement burden on patients.39 Despite the use of different questions wit h each patient the results are comparable as all items in the bank are drawn from the same scale. The PLUS M item bank also a llows comparison of scores to a large development sample. Despite the advantages of this approach, as with all OMs consideration m ust be given to the population used to develop and select items for the bank, especially in limb loss research which can over - sample people with traumatic limb loss41 and those living in high income countries. This may raise questions about ho w transferrable OMs are to different populations and subgroups who were not well - represented by the development sample i.e. older d ysvascular patients, or those from different social and cultural backgrounds. However, whatever the measure the key concern highlighted in this review is responsiveness, or the ability to detect change when it has occurred22,33,36 which is a priority when using OMs to evaluate rehabilitation interventions in clinical settings.42 Many of the OMs identified in the SR lacked data regarding ceiling or floo ring effects33,35 and only three include a Mini mal Clinically Important Difference (MCID) value.43–45 The MCID is the smallest change in the score that indicates an important change for the patien t.46 Without these va lues, clinicians and patients do not know if the change they have recorded represents a meaningful improvement to patients’ lives, whether clinical interventions were effective and, importantly, whether the investment in rehabilitation was justified. Howev er MCID values may only be truly meaningful if the OM captures an outcome of importance to the patient in the first place. 7 3) What are the barriers to outcome measurement? Several studies (4) have explored OMs in prosthetic practice in an attempt to understand what factors may influence their use,47–50 however it should be noted that two of them focus on the same group of clinicians.47,48 The barriers identified in this review focus on the experiences o f prosthetists, of whom only 28% - 44% describe themselves as routine users of OMs.36,47,48,50 Interestingly a thesis st udy of physiotherapists working in amputation rehabilitation settings found that 100% used OMs regularly, but no information was available expl oring the drivers for this level of engagement.36 Therefore our understanding of the b arriers to outcome measurement in prosthetic rehabilitation is limited to the perceptions of prosthetists, which may not reflect the views of t he wider prosthetic rehabilitation multi - disciplinary team. In all of the studies included in this theme, insuf ficient time was identified as a key barrier as clinicians struggled to integrate outcome measurement into their usual clinical routines47,48,50 This wa s compounded by an often described lack of confidence and knowledge of the tools themselves. Challenges are commo nplace, including choosing a measure that is meaningful, psychometrically sound, and easy to use and interpret within a clinical session. These challenges are reportedly particularly difficult for many clinicians who lack knowledge and understanding in thi s highly technical field, which is not always covered at undergraduate level.47,48,50 An interesting finding from Hafner et al’s 2017 study exploring p erceptions of outcome measurement amongst 66 US prosthetists showed that a third of participants did not agree th at outcome measurement provides useful data.47 The is sue of the value of outcome measurement for clinicians was highlighted again in the same study where prosthetists reported they were more likel y to use the Amputee Mobility Predictor (AMP) measure than the quicker -to-administer Timed Up and Go (TUG). The a uthors discussed that this may be because the AMP was designed to guide the prescription of prosthetic componentry to insurance companies. This is supported by a study from Borrenpohl et al who found that regular OM use increased to 77% when prosthetists w ere specifically asked about using OMs to support payment claims.51 These findings demonstrate the impact of perceived value in engaging clinicians in the measurement of outcome. Solutions to address these barriers were also discussed in the same a rticles. The need for efficient measures, use of PROMs instead of more time consuming observed measures, and electronic data collection at the point of care, which is integrated with health records, were all suggested to help overcome time related barrier s47,48 An educational programme focused on improving prosthetist knowledge and skills concerning outcome measurement was tested by Ga unard et al and was found to improve confidence, which was maintained one year later.47 However these findings should be viewed with caution as they did not include a control group and participation in the studies may have been more attractive to those who were particularly interested in learning about outcom e measurement. 4) What examples of ROM currently exist in prosthetic practice? Despite the many barriers identif ied in this review three examples of system wide ROM programmes in prosthetic settings were identified. Two are in the style of registries, and as with the NJR they attempt to link surgical information and interventions with outcome data to understand the impact of lower limb amputation on patients, health care providers and society. The other is a study exploring the use of system wide outcome m easurement across a network of prosthetic clinics in the US, and how it could be used to inform quality improveme nt (QI) activities. The two registries identified in this review are the Scottish Physiotherapy Amputee Research Group (SPARG) data initiativ e which collects data on all patients undergoing amputation in Scotland,52 and SwedeAmp which i s the national lower limb amputation registry from Sweden.53 Both registries attempt to evaluate t he whole process concerning lower limb amputation, collecting demographic details, 8 surgical and rehabilitation interventions, prosthetic supply and outcome. For SwedeAmp, uptake across the country has been slow and after 9 years the regis try only captures 62% of amputations,53 illustrating the challenges related to multidisciplinary sy stem level data c ollection . In contrast, SPARG, which is a small group of 20 physiotherapists, currently captures 90% of amputations in Scotland and has done for over 20 years.52 The unprofessional nature of the group may account for its success in data c apture but also p laces a limitation on the scope of the data set. Despite these challenges huge amounts of data are now available for analysis and these projects demonstrate what is possible. Further publications regarding the implementation of these regis tries will be invaluable learning for the development of similar projects elsewhere, including how the outcomes included were selected and why, how the data are linked to clinical practice and improvement activities, how data security was addressed in the post GDPR era, an d how the barriers described in this review were overcome. The authors of the third paper, focusing on using routinely collected PROM data to direct QI activity, also found initiation of this work challenging with only two of the seven c linics involved a ctually collecting enough data to undertake QI projects.49 However, as the study was undertaken as research there was a higher administration burden, which was identified by the authors as the main factor for clinics dropping out.49. Clinics that were successful demonstrated high levels of organisation r elated to the project, integrated data collection with their electronic records and had well defined pathways of care. Clinicians in these centres reported being motivated by the chance to use PROMs to improve the care they provided to patients, however e xpert external fa cilitation was crucial to increase QI knowledge, interpret aggregated data and translate ROM findings into improvement work.49 Discussion This review has identified many barriers to outcome measurement in prosthetic rehabilitation, however the examples of system wide outcome measureme nt presented here also give an indication of what may facilitate ROM in prosthetic clinical settings, and suggests that barriers can be overcome but important lessons may need to be learnt to ensure success. Despite the fact that only the views of prosthe tists have been explored regarding outcome measurement in prosthetic clinical practice, their experiences are mirrored by those of other AHPs documented in the wider literature. Insufficient time for both patients and clinicians to comple te and score meas ures in time -pressured clinical consultations, difficulties in selecting a measure and interpreting results, combined with low confidence and limited knowledge of outcome measurement are all commonly reported.4,19,54 –56 This review identifi ed that establishing value in outcome measurement could be a potential facilitator to addressing barriers to implementation. This issue of value has been raised in previous ROM initiatives. A multi stakeholder consultation on the NHS P ROMS programme in 20 17 found that many clinicians and managers felt it was not worth continuing with the programme despite 8 years of data collection.57 The consultation reported that the data collection was not useful to clinical practice, could not be used during clinical consultations and that reports took too long to be published, so findings were out of date.57 This lack of perceived value was also described in mental health settings where clinicians felt that ROM using the Health of the Nation Outcome Scales (HoNOS) was overly bureaucratic, only concerned with performance management, l acked feedback of results and presented no relative meaning for their role.58 It seems clear that establishing the value of ROM to clinical stakeholders is key for success.59–62 Much of this value may lie in addressing some of the key themes identified in this review. Understanding what is important and meaningful to measure a nd how to measure it effectively following 9 lower limb amputation could help make outcome measurement more useful and address the issues highlighted here around lack of consensus in the field. A lack of consensus is not only seen in the prosthetic literatu re and attempts have been made to address the issue in several areas of health such as rheumatology63 and women’s health,64, especially when related to research. The reporting o f numerous outcomes in clinical trials can make the synthesi s and comparison of different studies near impossible.65 This problem has led to the development of initiatives such as Core Outcome Measurement in Effectiveness Trials (COMET) who seek to develop consensus around what to measure through core o utcome sets (COS) to be recorded in all clinical trials of a specific condition.66 They also highli ght that this approach can be useful to build consensus around outcome measurement in clinical practice.65 COMET adopt a multi -stakeholder approach to identifying outcomes of importance and seek to build consensus that can then be championed by the stakeholders involved. Importantly COMET advocate the in clusion of patients at the centre of this process, ensuring that a COS is measuring what matters most to the people affected by the outcome of an intervention.65 The studies identified in this review by Schaffalitzky et al. and McDonald et al. have made some progress towards understanding successful pr osthetic prescription from a user’s perspective, especially focusing on the need to measure psychosocial outcomes.29–31 Schaffalitzky et al. also highlight that clinicians and patients view important outcomes differently. This difference has been reported in other studies exploring UK orthotists perspectives of clinical outc omes,67 and in the development of a COS for rheumatoi d arthritis,68 and suggests that when seeking to build consensus around important outcomes for measurement that the patient’s voice is properly represented, especially in commonly used, but arguably less accessi ble, consensus building techniques such as Delphi.65 A rigorous foundation of understanding important outco mes of prosthetic rehabilitation could lead to consensus and would direct the recommendation, or development of, a set of user -centred OMs. This set would also need to play its part in overcoming some of the barriers to measurement described here, such as capturing meaningful changes over time, feasibility of use and ease of interpretation in busy clinics, as well as not overburdening patients themselves. This process is unlikely to be as simple, as highlighted by the UKROC project when developing a nation al set for neuro rehabilitation. They describe a tension between identifying measures that have robust psychometric properties that generate data that is useful for quantitative analysis, and meas ures that are feasible to use in clinical settings, and that clinicians themselves want to use as part of the clinical decision making process.14 Usefully COMET have also defined ‘Consensus -based standards for the selection of health Measurement Instruments’, which is a systematic approach to identifying, selecting and ass essing the quality of relevant tools, to capture the importa nt outcomes agreed upon in a COS69,70 thus defining a framework for how to measure outcomes. It appears the final piece in the value puzzle may go beyond what to measure and how to measure it and focus on how outcome measurement data can be used. This review identified that many prosthetis ts felt that outcome measurement was not useful47 whic h is supported by concerns described in the NHS PROMS programme consultation57, mental health examples58 and the wider AHP literature.56 All of these examples report that the ability to use OM data as part of usual care to inform treatment planning and monitor the progress of long term conditions in real time is key.47,57,58 The use of electronic records systems, as described by Heinemann a s a facilitator of success49, is increasing rapidly throughout health care organisations and presents opportunities to develop IT that supports OM collection. Aims should be to integrate results with clinical records and present findings instantly, enabling real time use, as well as for l ocally owned reporting and upload to national data sets which could be accessed for research.71 The UKROC programme attribu ted its consistent high quality data collection to the fact that OM data collated on their electronic system was available ‘live’ for clinicians. This access combined with careful integration of OMs into all aspects of clinical care, such as managing bed c apacity 10 and discharge planning, ensured OMs were useful to clinical teams and promoted delivery of the best care.14 Although the use of electronic platforms could allow for less burd ensome OM completion and real time feedback to clinicians, successful clinical uptake is likely to lie in the cost of the system as well as the accessibility of both the electronic version of the measure during data collection with patients, and the mode o f presenting information and reporting in an accessible way for interpretation. A qualitative study of UK orthotists perceptions of outcome mea surement identified technology as a potential enabler of OM use as long as it was usable, enabled interpretation of reports and met clinical need.67 Being able to interpret what OM data is telling us is another part of the pu zzle, especially when using aggregated OM data.3,72 Expert external facilitation was championed by Heinemann et al49 and has been described as part of other system level OM initiatives.14 Facilitation would be useful to support clinicians who report low levels of knowledge and confidence in using OMs, as well as working in busy clinical environments where there is often little time for anything other than treating patients.47,48 Developing partnerships with academic institutions or QI teams may help clinicians with the interpretation of findings and address educational needs t hrough joint working. Academics often have highly developed data analysis skills and increasingly need to demonstrate the real -world impact of their work, for example in the UK’s Research Excellence Framework and Knowledge Exchange Framework. Clinical acad emic roles, improvement fellowships or partnerships with universities could bridge the expertise gap between clinical practice, academia and QI.73 This is critically important when attempting to translate outcome data into improvements in care. Outcome data itself does not inform what the underlying cause of a poor outcome is, and only indicates where a problem may lie. Further work, as described by Heinemann et al., is then required to investigate causes, implement change and evaluate its effect iveness.49,73,74 Individuals with this valuable ‘knowhow’ may be critical to making outcome measurement really work in clinical practice. Limitations It should be considered that this review identified OMs in the form of scales, tools or questionnaires and does not include other outcomes that may be in use clinically, or of importance to prosthetic users, such as hours of limb use or limb abandonment. A second limitation of this review is its narrative approach, however this broad overview is a useful starting point which sets the scene for more systematic approaches in the future to explor e some of the themes identified here in more detail. Conclus ion This narrative review takes a broad look at outcome measurement in prosthetic rehabilitation from a clinical perspective and has demonstrated that successful implementation is complex and multifaceted. Understanding and embedding value at every step is key to success. For clinicians reading this review, the take home message is that measuring the outcome of our interventions is important to understand the impact on patients and the perform ance of our services. However, it is more than just selecting a n OM. Clinically we need to understand the ‘why’, ‘what’ and ‘how’ of outcome measurement. ‘Why’ measure, i.e. to inform at the individual or system level, ‘what’ to measure i.e. capturing out comes that are meaningful, and ‘how’ to measure it i.e. the best tools for the job used in a systematic way that adds value to our practice. Future work should consider all barriers to implementation and engage with patients and stakeholders throughout to overcome them. Consensus issues of what to measure and how to measure it need to be addressed, ensuring outcomes are meaningful to patients and measurement tools are accessible to use and interpret. Clinician led outcome measurement practice can then be developed in partnership 11 with HEIs or local quality improvement teams, focusing on making outcome measurement valuable to prosthetic rehabilitation services, to evidence and improve clinical practice. Acknowle dgments: The authors are grateful to funders the En gineering and Physical Sciences Research Council (EPSRC) / National Institute for Health Research (NIHR) Global Challenges Research Fund (grant EP/R014213/1 ), the Royal Academy of Engineering (R AEng grant RF/130) , and the University of Southampton’s Institute for Life Sciences / FortisNet interdisciplinary musculoskeletal research network . We have no conflicts of interest relevant to this study