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Speech–language pathology students: learning clinical reasoning

Chapter 37 Speech–language pathology students learning clinical reasoning Lindy McAllister, Miranda Rose CHAPTER CONTENTS Introduction 397 Seeking clinical reasoning in SLP 398 Emerging directions and challenges in SLP clinical reasoning 399 How do speech-language pathologists reason? 399 Other sources of knowledge about clinical reasoning in SLP 400 Teaching clinical reasoning in professional entry curricula 401 A problem-based learning approach to teaching clinical reasoning in SLP 402 Summary 403 INTRODUCTION In our chapter published in the previous edition of this book ( McAllister & Rose 2000 ) we wrote: Writing this chapter posed something of a dilemma because, in general, speech-language pathologists do not talk about clinical reasoning. … Firstly, speech-language pathologists (educators and clinicians) may well discuss or write about differential diagnosis, problem solving, decision making, critical thinking, professional judgment and diagnostic reasoning; they rarely discuss clinical reasoning. Secondly, the processes involved in clinical reasoning in our profession have been poorly researched and are little understood within the profession. (p. 205) Since we wrote that chapter, a paradox has emerged. Some discussions of applications of other professions’ models of clinical reasoning to speech-language pathology (SLP) models are appearing ( McAllister & Lincoln 2004 , Young 2001 ). However, there continues to be no substantial published research into the clinical reasoning practices of our profession. A 2005 search for references to research into clinical reasoning in SLP in academic databases and recent prominent texts on assessment and management of communication and swallowing disorders revealed minimal results. However, references to clinical reasoning are now quite common on university websites that describe their curricula, in professional association publications detailing professional competencies, and in texts describing clinically-related activities. Thus, while the profession appears to have become alerted to and interested in clinical reasoning as a necessary component of clinical practice, and is now using the term ‘clinical reasoning’ with greater frequency, it is used on the basis of a paucity of data about the actual clinical reasoning practices taking place in SLP. SEEKING CLINICAL REASONING IN SLP In this chapter we make a distinction between clinical decision making (a term more common in SLP) and clinical reasoning. We see clinical decision making as an end-product of clinical reasoning; that is, as the generation of tangible decisions about clinical management. In contrast we see clinical reasoning as the often intangible, rarely explicated thought processes that lead to the clinical decisions we make. We suggest that clinical reasoning utilizes metaprocesses , including an awareness or a becoming conscious of what we are thinking and what thought processes we are using. Reflection in and on action ( Schön 1987 ) has a major role to play in clinical reasoning. Based on our critical reading of the literature, we could describe the process of clinical reasoning in SLP as the ‘black box’ of information processing occurring between the input phase of data gathering and the output phase of producing decisions (concerning diagnosis and treatment) and taking action ( Fig. 37.1 ). The reasons for this ‘black box’ state of affairs lie in the history and operation of our profession wherein clinical reasoning, being (broadly) the thinking associated with clinical practice, was assumed to be a skill that could be absorbed without explication. Kamhi (1998 , p. 102), for instance, argued that ‘as clinicians become more experienced, they gradually internalise the framework of an assessment protocol and become proficient at analysing and interpreting test information and observational data’. The SLP profession seems to have adopted what Boshuizen & Schmidt (2000) referred to as a content-oriented approach to clinical reasoning. This approach assumes that knowledge and reasoning are interdependent. There is an expectation that with increasing knowledge and clinical experience, students and clinicians will be better able to reason and make clinical decisions. University curricula have concentrated more on knowledge acquisition and skills development while ‘issues specific to the decision-making process are relegated to the periphery of discussion’ ( Records et al 1994 , p. 74). Figure 37.1 Clinical reasoning in speech pathology – the ‘black box’ Another focus of our profession has been on outcomes and solving problems in clinical practice. Consider recent sources in the SLP literature: for example, Dodd’s 1995 text Differential Diagnosis and Treatment of Children with Speech Disorder contains a chapter on a problem-solving approach to clinical management. This problem-solving model begins at the stage of description of the current communication status (after diagnosis). Although it is an excellent model for problem solving in client management, it offers no clues to the clinical reasoning which lies behind the clinical problem solving. The Pocket Reference of Diagnosis and Management for the Speech-Language Pathologist ( White 2000 ) contains a wealth of useful information to assist in clinical problem solving or decision making. It does not consider the clinical reasoning thinking processes underpinning diagnosis and management. Another factor limiting understanding of clinical reasoning in SLP is that it has been seen as a linear or logical process, which obscures the ‘messiness’ and complexity of clinical reasoning in action. Duffy (1998 , p. 96) suggested that the processes of decision making ‘became obscured with training that views diagnosis as a linear, test-oriented, and mechanistic process, and that often “teaches” diagnosis by starting with the target disorder (the diagnosis) and then proceeding back to its defining symptoms and signs’. Yoder & Kent (1988) published an influential series of decision-making trees for the diagnosis and management of communication disorders. They stated that the trees were not to be seen as recipes, but rather as a series of guidelines and prompts for the clinician engaged in decision making. ‘Cookbooks cannot deal with the unknown or the uncertain, but clinical decision making frequently encounters them’ ( Yoder & Kent 1988 , p. xi). This approach has the advantage of providing guidance without rigidity and recognizing the need for professional judgement as part of decision making. However, the focus is again on the decision steps to be taken rather than on the nature of thinking in which clinicians engage and how they might respond to the prompts provided. The approach reinforces the view that clinical reasoning and decision making are basically linear and logical, whereas we argue that they are not. Further, the responsibility for learning how to think lies with the clinician. It is not made explicit. EMERGING DIRECTIONS AND CHALLENGES IN SLP CLINICAL REASONING In their edited text Differential Diagnosis in Speech-Language Pathology , Philips & Ruscello (1998) provided a broader picture of the process of diagnosis. Although they referred readers to decision-making trees they moved beyond a formulaic data collection approach to an acknowledgment that ‘the speech-language pathologist’s curiosity and inquisitiveness drive the process of differential diagnosis. The clinician who accepts diagnostic challenges, is curious about missing information and inconsistencies, constantly questions, and searches for possible answers is most likely to solve puzzles presented by difficult problems’ ( Philips & Ruscello 1998 , p. 3). It is argued here that clinicians need to be aware of missing information and inconsistencies and to be thinking about them, questioning self, the process and the data. In other words, clinicians need to be engaged in metacognition, or thinking about thinking, a key component in the Higgs & Jones (2000) model of clinical reasoning. Kamhi (1998) and Deputy & Weston (1998) have reminded readers of the importance of asking causal questions but cautioned them about assuming linear causality. Asking questions about factors that may or may not cause communication disorders and that contribute to the data obtained in evaluation is an important component of what we would call clinical reasoning. Records et al (1994) discussed clinical judgment. They emphasized not only the objective aspects of data collection, but also the subjective aspects of the decision-making process; the gut feelings, expertise and insights which are aspects of clinical reasoning. They considered clinical judgment to be a process poorly understood by speech-language pathologists. Scholten (2001) argued that both classroom and clinical experiences can be used to facilitate student clinical reasoning. She suggested that teachers should use authentic problems to develop students’ understanding of clinical problems and transfer of theoretical knowledge. However, again, such assertions were based on theory from medical education and student learning in general rather than specific evidence in speech language-pathology. HOW DO SPEECH-LANGUAGE PATHOLOGISTS REASON? In the relative absence of direct clinical reasoning research, writers in our discipline have resorted to supposition or analogy, drawing on research in other professions. Campbell (1998) outlined four approaches to diagnostic decision making found in clinical medicine that also apply to SLP: pattern recognition, decision-making trees, diagnosis by exhaustion (collecting all possible data), and hypothetical-deductive reasoning. Duffy (1998 , p. 97) stated that ‘most good diagnosticians reach conclusions through a hypothetical-deductive strategy, with frequent reliance on pattern recognition’. The paucity of research into decision making and clinical reasoning in SLP does not provide data to test Campbell’s or Duffy’s assumptions. However, in their reflection on comparisons with reasoning approaches in other disciplines, Campbell and Duffy began to question possible reasoning strategies in SLP. A promising discussion in our field comes from Hagstrom (2001) who presented a potential framework for using and building theory in clinical action in SLP. Hagstrom wrote about clinical action being guided by theory and proposed Bamberg’s (1997 ) six-element framework of theory analysis as a tool for reflection on practice. Table 37.1 illustrates the six aspects of Bamberg’s framework, with typical clinical questions that could be asked in SLP practice. Although Hagstrom did not directly discuss clinical reasoning and made no reference to research examining reasoning in other professions, it appears to us that there is a direct connection between her arguments and our discussion of clinical reasoning practices. Table 37.1 Bamberg’s aspects of theorizing in action and their potential applications to speech-language pathology Aspect Typical speech-language pathology clinical questions Domain of inquiry What knowledge base(s) could/should I be drawing on in working with this client/situation? Person Am I working with a client actively engaged in his/her care, or a passive client? Course of development Is change for this client/situation likely to happen step by step or can steps be merged or skipped? Telos What is the ideological endpoint for me and for my client in this situation? Mechanism What is likely to cause change to happen in this client/situation? Methodology What type of data should be collected? How will they be collected and documented?

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A dynamic model of ethical reasoning in speech pathology

Ten new graduate speech pathologists recounted their experiences in managing workplace ethical dilemmas in semi‐structured interviews. Their stories were analysed for elements that described the nature and management of the ethical dilemmas. Ethical reasoning themes were generated to reflect the participants' approaches to managing these dilemmas. Finally, a conceptual model, the Dynamic Model of Ethical Reasoning, was developed. This model incorporates the elements of awareness, independent problem solving, supported problem solving, and decision and outcome evaluation. Features of the model demonstrate the complexity of ethical reasoning and the challenges that new graduates encounter when managing ethical dilemmas. The results have implications for preparing new graduates to manage ethical dilemmas in the workplace.

New graduates require preparation to manage ethical issues and to facilitate the development of ethical reasoning skills. Buie's 1997 survey of speech pathologists and audiologists found that they experienced a range of ethical dilemmas that included managing unethical behaviour by colleagues, clients disagreeing with intervention approaches, difficulties in assigning treatment priorities, inadequate supervision and training, and external constraints on clinical decision making. 1 Effective preparation must equip graduates with the skills to resolve the current ethical dilemmas that confront the profession and the new ethical dilemmas that will emerge during their professional careers. This is important as the nature of ethical dilemmas experienced by professionals may change in response to political, economic and social environments. 2

There are many factors that influence how new graduates learn to manage ethical dilemmas. Graduates usually enter their professions equipped with a background of ethical theory and practice with bioethical case studies. Additionally they may make use of their personal and professional values. 3 They also have knowledge of their professional code of ethics and have opportunities to develop ethical reasoning skills as they observe the ethical practice of more experienced colleagues. 4 The cognitive, personal and perceptual characteristics of new graduates may also affect their skills and motivation to manage ethical issues at work. 5 , 6 It is important that professional preparation programmes, professional associations and allied health managers have knowledge of and enhance the ethical reasoning skills of new graduates to protect the wellbeing of clients, colleagues and the profession. However, it is difficult to propose strategies for supporting and improving ethical management as there is no research investigating how new graduate health professionals approach the task of solving ethical dilemmas. The aim of this study was to investigate the ethical reasoning approaches adopted by new graduates.

Participants

Ten participants were recruited from within an area health service in New South Wales. The participants were speech pathologists with 1–18 months' professional experience with diverse caseloads. A summary of their experience and workplaces is presented in table 1 ​ 1 .

Data collection

The primary investigator and a research assistant conducted a semi‐structured interview with each participant at work and recorded this on audio tape. The investigators adopted a narrative inquiry approach whereby participants were asked to describe ethical dilemmas experienced at work and their strategies for managing these. 6

Data analysis

The primary investigator was responsible for the analysis of the participants' reports. The interpretive process used required several stages of analysis: identification of meaning, configuration of data into a plot and synthesis to provide new insights into the ethical reasoning of new graduates. The participants' narratives were organised into four broad elements of ethical reasoning: identification, problem solving, decision, and outcome. 7

Firstly, the dilemma was identified. This included participants' descriptions of one or more ethical dilemmas that they had experienced at work. The problem‐solving element incorporated thoughts, feelings and actions that influenced participants' decision making. The decision element represented the results of ethical reasoning—that is, how the ethical dilemma was managed. The outcome element reflected participants' evaluation of the effect of ethical decisions. Secondly, an ethical reasoning story was configured for each participant by identifying themes under the four elements of identification, problem solving, decision and outcome, using the participant's words to reflect approaches to ethical reasoning. Transcripts and ethical stories were viewed and validated by the participants.

Thirdly, ethical reasoning themes were synthesised across participants. Participants experienced a range of ethical dilemmas and demonstrated individual differences in the way that they managed ethical dilemmas. Nevertheless there were patterns and similarities in their approaches to ethical reasoning. Group analysis resulted in the development of a theoretical framework to represent the ethical reasoning processes demonstrated by the participants.

RESULTS AND DISCUSSION

Analysis of the 10 participants' ethical stories revealed elements of ethical reasoning that were shared by them. A conceptual framework was developed to represent the major elements and features of ethical reasoning undertaken by the participants. This framework incorporated elements of awareness, heightened awareness, an initial response, independent and supported problem solving, decisions, and outcome evaluation from managing an ethical dilemma. Although participants shared these elements of ethical reasoning, there were differences in the nature of the ethical dilemmas they experienced in the workplace. Thus, each element incorporated a range of features that reflected the factors that influenced participants' ethical reasoning.

The major elements and features of ethical reasoning undertaken by the participants are presented in the Dynamic Model of Ethical Reasoning (table 2 ​ 2 ).

The model is dynamic in nature because participants did not manage their ethical dilemmas in a series of discrete steps but rather as a fluid, interactive reasoning process that incorporated cognitive and psychosocial elements. Table 3 ​ 3 demonstrates the complex process taken by participant 7 to resolve an ethical dilemma. Although the nature and order of the ethical process varied, analysis of the ethical stories revealed that participants consistently moved back and forth between the elements of ethical reasoning. Thus, they integrated knowledge, experience, insights and values as they resolved ethical dilemmas.

Elements and features of the Dynamic Model of Ethical Reasoning

Awareness element.

Participants discussed the process of identifying ethical issues at work as either gradual or abrupt. Five participants became progressively more concerned about the ethical implications of work practices. Six participants reported being suddenly confronted by an ethical dilemma during interactions with a client or colleague. Thus, features of the awareness element included early concerns, conflict in practice and critical incidents.

Early concerns

Over half the participants described feeling uncomfortable and unprepared to address ethical problems in the workplace. They attributed this uneasiness to a lack of insight into ethical issues. “You are trying to impress as a new grad and trying to deal with it because you basically think that the expectations that are being placed on you, they must be representative of what it is to work” (P8). Three reported feeling so overwhelmed by their new working environment that they had neither the confidence nor the energy to solve ethical dilemmas. “It's just very overwhelming when you come to a workplace like this and it's so shambolic” (P6).

Conflict in practice

As they gained experience, new graduates perceived discrepancies between policies and practice. Six participants noted that the policy of equal access to community services was not effectively implemented with clients, including those from culturally and linguistically diverse backgrounds. “In order to provide a fair and equitable service for all clients, we have an interpreter service available at the hospital. But I guess in terms of an ethical dilemma it can be quite difficult to book those interpreters” (P7).

Critical incidents

Nine participants reported increased sensitivity to ethical issues when clients' wellbeing and safety were at stake. Participants working in hospital settings were frequently involved in decisions to provide nasogastric feeding for adults with swallowing disorders who were unable to maintain adequate oral nutrition. “When I took over this lady she was still inappropriate for oral intake and still hadn't had any kind of nutrition for over a week” (P4). Participants perceived it as their responsibility to ensure that that the client's family and the healthcare team made an informed decision about whether to initiate tube feeding when the prognosis for recovery was poor.

Facilitating awareness

Although participants were able to describe the nature of an ethical dilemma, they were generally unable to define the specific ethical principles at stake. They questioned whether their dilemma was an ethical problem. “I don't know if it's a real ethical dilemma, though” (P3). Managers could support new graduates' awareness of ethical issues by discussing the ethical dilemmas they themselves have experienced, raising ethics during supervisory conferences and identifying ethical issues during case conferences.

Independent problem‐solving element

All participants had engaged in independent problem solving to manage ethical dilemmas. The independent problem‐solving element of the Dynamic Model of Ethical Reasoning has cognitive, emotional and psychosocial features, and incorporates participants' initial reactions, professional responsibility, self‐protection, clinical reasoning, rules, values and beliefs. Most participants sought advice from experienced colleagues to support independent problem solving. A perceived lack of support resulted in new graduates struggling to manage ethical dilemmas independently.

Initial reactions

The first response of seven participants to an ethical dilemma was to question whether it was their problem. “The main thing for me was knowing whether I should get involved or not” (P1). Eight participants described an initial response based on personal values and sympathy for the perceived “victims” of the ethical dilemma. “I just felt really sorry for them and really wanted to give them more services” (P5).

Accepting professional responsibility

All participants reflected upon their professional roles and responsibility to advocate for clients with communication and swallowing disorders. “I feel responsible for them because they are now… these are my children!” (P6). Once participants accepted responsibility for clients' welfare, they adopted an active role in resolving dilemmas or seeking support to improve the quality of client care.

Self‐protection

Seven participants raised concerns about the effects on relationships with colleagues and managers of raising ethical issues or challenging work practices. “You don't want negative feelings or thoughts directed back to you” (P10). Self‐protection influenced participants' motivation to resolve ethical dilemmas. Participants were reluctant to pursue ethical issues if they perceived risks of alienating more senior colleagues. However, they were motivated to resolve ethical dilemmas when they perceived that others held them responsible for the outcome. “You don't want to leave yourself open for litigation” (P2). These results are consistent with Braunack‐Mayer's findings that health professionals' concern for maintaining reputation and status influences their response to ethical dilemmas. 8

Clinical reasoning

All participants provided examples of client based dilemmas and demonstrated clinical reasoning skills. 9 Participants drew knowledge from previous clinical cases to support decision making “I'd just had another lady go home who was quite similar at the start but the team that this other lady was under had put an NG tube down straight away and she had improved quite markedly” (P4). Given their limited clinical experience, new graduate professionals may benefit from further application of evidence based practice to manage ethical dilemmas rather than relying on limited case experience.

For nine participants, it was important to establish who was to blame for an ethical problem. “I think the organisation fails its clinicians by not having some sort of protocol that is regularly evaluated” (P6). Although an understanding of cause–effect relationships may help new graduates avoid unethical behaviours, a culture of blame may prevent them from seeking support when ethical problems occur in the work place.

Seven participants reported using policy “rules” to guide ethical problem solving. Participant 10 perceived rules as the means for providing a fair service. “If you rang everyone on that waiting list and asked them for their story they would all have some sort of emotional heart‐wrenching story about why their child needs the service now So, I think that you need the rules” (P10). Following the rules reduced the anxiety of participant 10 about providing limited services to clients. However, a reluctance to challenge rules and policies may perpetuate unethical practices.

Beliefs and values

Eight participants considered attitudes, beliefs and values during problem solving. Participant 2 reflected upon her values when she addressed issues of safety and quality of life with a client. “I would want my mother to be comfortable and enjoy what life that she had left” (P2). They reported conflict between the cognitive and psychosocial features of ethical reasoning when strategies to resolve ethical dilemmas “didn't feel right”. “I obviously had to discharge her because that's the policy and the mother was aware of that…but I just didn't feel that it was right to be not offering that child anything” (P9). It may not be appropriate or possible to change the beliefs and values of new graduates, yet our results support the claims of Schneider and Snell that the opportunity to share and challenge attitudes about ethical issues may facilitate ethical reasoning skills. 2

Lack of support

Independent problem solving, according to four participants, resulted when senior colleagues failed to respond to ethical concerns. Participant 8 developed her own strategy for managing missing documentation because she perceived that senior colleagues were reluctant to address a staff member's incompetence. “I would mention to them that all those files were missing, they never really offered strategies in terms of how I can approach it” (P8).

Facilitating independent problem solving

Independent problem solving was a consistent feature of the ethical‐reasoning process and reflected participants' concern for the needs of clients, carers, managers and their professional status. During the dynamic process of ethical reasoning, participants frequently returned to independent problem solving to compare and contrast their thoughts and beliefs with recommendations provided by more experienced professionals. Results from this study suggest that the ethical reasoning of new graduates may be facilitated by reflecting upon the beliefs and values inherent in ethical dilemmas and by discussing roles, responsibilities and boundaries in managing these dilemmas. New graduates may require support to interpret and apply policies and procedures with the flexibility required to meet client and organisational goals.

Supported problem‐solving element

When managing ethical dilemmas, participants sought support from managers, colleagues and sometimes, other new graduates. However, the support they requested varied according to participant and the nature of the ethical dilemma and included features of checking, discussing and handing over problem solving to others.

Four participants independently resolved ethical dilemmas, then sought support to confirm their ethical decision. “Basically just a bit of reassurance that I was going in the right direction” (P1). Participants usually sought such support from senior professionals.

Seven participants reported collaborative problem solving with managers, colleagues or interdisciplinary team members. During these interactions, participants shared their perceptions and suggestions for managing ethical dilemmas. Managers provided a holistic perspective to ethical reasoning and suggested alternative practical strategies for managing ethical dilemmas based on their professional experience. “Talking to a senior and once talking about the different types of options and getting more perspective on the quality of life of this lady” (P2). Participant 2 was able to propose a range of intervention strategies to meet her client's needs. Colleagues and team members provided professional and emotional support by sharing the responsibility for resolving ethical dilemmas. “I actually find the whole team to be very supportive” (P4).

Handing over

Some ethical dilemmas were immediately “handed over” to a senior professional. Three participants reported strong psychosocial responses or feeling disempowered to manage ethical issues. “The safest option for me was to choose not to discuss it with anyone, to refer any comments to [manager]” (P7).

Facilitating supported problem solving

Consistent with the discussion by Handelsman et al of the acculturation process of ethical reasoning, senior clinicians were generally considered to be role models for ethical practice. 10 “She's a great model and she's actually quite a bit more senior to me” (P4). Consequently, new graduates need to observe positive role models to develop skills, confidence and independence in ethical reasoning. Similarly, role models who initiate debate of ethical issues and challenge inappropriate work practices and procedures 11 may facilitate ethical courage in new graduates.

Decision element

The decision element describes the features of ethical decisions as actions for a client, professional, team or organisation.

Action for client

Participants were motivated to provide the best outcomes for their clients. Nine participants reported that their ethical decision focused on client care. However, they felt restricted to making changes at the micro level of the clinic room rather than at the organisational level. Ethical decision making incorporated an acceptance of current work systems and a motivation to work within these systems to best meet the needs of clients. “Instead of giving her 6 weeks I'm going to see her once a month for 6 months and do a lot of collaboration with school and …give her a home programme as well”(P5).

Action for professional

Participants considered the effect of ethical decisions on their current levels of stress and job satisfaction and their future careers. Seven participants indicated that ethical decisions focused on their own needs. In community health settings, such decisions sometimes resulted from participants' tension between meeting the needs of a large caseload and addressing their own need for client‐free time to pursue professional development activities. “My decision with that has been more to preserve myself” (P10). In hospital settings, participants reported that avoiding litigation was of paramount importance when issues of quality of life versus safety arose in client care. These issues were most likely to occur when the participant was managing a client with swallowing difficulties and conflict arose between providing a “safe” diet and the carer's desire to provide more enjoyable or culturally appropriate meals. “You cannot care for your loved one for the reason that if something does happen one of us or all of us could be in a lot of strife” (P2). Participants empathised with carers' needs to support their family member in hospital and experienced discomfort when they perceived that dietary recommendations were enforced so that the speech pathologist would not be held accountable for any medical complications.

Action for team

Five participants expressed concerns about establishing a professional identity within their workplace teams. When they perceived that a team had disregarded their clinical recommendations, their ethical decisions focused on education and prevention rather than the individual client. “I think it's more of a prevention of it ever happening again. I mean for me the ethic has already been sort of broken” (P3).

Action for the organisation

There were three examples of participants withholding information to reassure clients about services yet save face for their organisations. “I'm always put in a position where I have to cover up all the time” (P6). Participants perceived it to be disloyal to discuss ethical concerns, a potential barrier to accessing support from professionals external to their workplace.

Facilitating ethical decisions

Ethical decisions frequently involved prioritising the needs and wishes of one stakeholder over another. Perhaps this is why six participants had unresolved ethical dilemmas at the time of their interview. New graduates may benefit from support to critically evaluate the needs of clients, colleagues and their employers and thus prioritise these needs when making an ethical decision.

Outcome element

Participants were requested to evaluate the outcomes of their ethical reasoning. In response, they judged the personal and professional outcomes of ethical decisions as positive, negative or adequate.

Positive outcomes

Positive outcomes of ethical reasoning were described by all the participants and included increased confidence in clinical skills and improved satisfaction with client care. “It was just amazing because as soon as she started to improve she had the best chance of happiness” (P4). Improved outcomes for clients included effective service delivery, clients having a “voice” in the healthcare system, or the implementation of quality‐improvement measures at work. “Lots of changes being made, really positive changes” (P8). Five participants experienced a stronger professional identity after defining their professional roles and responsibilities. Three participants developed a framework for managing ethical dilemmas and gained insight into workplace policies and procedures. Participant 9 explained that she had developed a stronger support network as a result of her ethical dilemma and her concerns that there were “no more places to go for help” were overcome.

Negative outcomes

Six participants expressed personal and professional frustration when limited resources prevented the application of evidence based practice. “I've been trained for it. And I can do it, but I can't with the limited services. I guess I feel really frustrated with that” (P5). They raised concerns about the effect of service delivery issues on client outcomes and clinicians' skills.

Adequate outcomes

Usually, participants reported an adequate outcome from managing an ethical dilemma. They recognised that services could be improved but were satisfied with their contribution to client care. “The only thing is knowing that within that length of time I've done everything I could for that child” (P9).

Facilitating outcomes

Participants were generally satisfied with the strategies used to manage ethical dilemmas. New graduates indicated that they fulfilled their professional responsibilities within the limitations imposed by workplace policies. However, seven participants expressed lingering concerns about the long‐term effect of ethical decisions on client care. The provision of a healthcare service in workplaces that are under‐resourced may result in poorer outcomes for clients and increased levels of stress and dissatisfaction for professionals. Providing new graduates with the opportunities and strategies to advocate effectively for clients may facilitate outcomes consistent with ethical practice.

The Dynamic Ethical Reasoning Model reflects the complexity of the ethical reasoning of new graduates. The elements and features of the model incorporate processes described in previous models of moral development. They are consistent with propositions that effective ethical reasoning requires sensitivity, problem solving skills, and the motivation and determination to act on decisions. 6 , 12 , 13 , 14 Our results indicate that new graduates may experience problems in various elements of the ethical reasoning process. Some struggled to define the nature of an ethical dilemma, others experienced difficulties with problem solving or reaching an ethical decision. By addressing the features of each element of the ethical reasoning process, educators, professional associations and managers may facilitate the development of new graduates' ethical reasoning skills.

The Dynamic Model of Ethical Reasoning is not a prescriptive tool that dictates how to manage ethical dilemmas. It provides scope for supporting new graduates to manage ethical dilemmas in the workforce as it reflects key elements and features of their ethical reasoning. Professional preparation programmes, professional associations and expert clinicians have an important role in equipping new graduates with the knowledge, experience and confidence to identify and manage ethical dilemmas effectively.

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Assessing and developing the written reflective practice skills of speech-language pathology students

Affiliations.

• 1 a Department of Communication Disorders , University of Canterbury , Christchurch , New Zealand and.
• 2 b Department of Mathematics and Statistics , University of Canterbury , Christchurch , New Zealand.
• PMID: 28925287
• DOI: 10.1080/17549507.2017.1374463

Purpose: Written reflective practice aims to support critical thinking and problem solving skills in speech-language pathology (SLP) clinical education programmes. Yet, there has been limited investigation of students' development of written reflective practice skills over time and during a real-time clinical experience. The purpose of this study was to investigate students' development of breadth and depth of written reflective practice across a six-week clinical experience.

Method: Participants were 59 undergraduate and 14 postgraduate SLP students. Participants wrote critical reflections describing an interaction with a client/s at the conclusion of weeks two, four and six of their clinical experience. Formative feedback was provided after each submission. Breadth and depth of reflection were coded using a modification of Plack et al.'s coding schema.

Result: There was a statistically significant association between time and likelihood of development of breadth of reflection for the elements process and content. Depth of reflection improved significantly across time. The majority of participants were classified as "reflectors" or critical reflector at the conclusion of the study.

Conclusion: SLP students can make significant improvements in both breadth and depth of written reflective practice over a six-week period. Implications for clinical teaching are discussed.

Keywords: clinical education; reflection; reflection coding; reflective practice development; speech-language pathology students.

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• The exploration of speech-language pathology students' perceptions and attitudes towards written clinical reflection. Nichols K, Ttofari Eecen K, Crosbie S. Nichols K, et al. Int J Speech Lang Pathol. 2023 Oct;25(5):697-709. doi: 10.1080/17549507.2022.2100930. Epub 2022 Aug 4. Int J Speech Lang Pathol. 2023. PMID: 35924686
• Reflective practice in speech-language pathology: a scoping review. Caty MÈ, Kinsella EA, Doyle PC. Caty MÈ, et al. Int J Speech Lang Pathol. 2015;17(4):411-20. doi: 10.3109/17549507.2014.979870. Epub 2014 Dec 30. Int J Speech Lang Pathol. 2015. PMID: 25548856 Review.
• Does a simulation-based learning programme assist with the development of speech-language pathology students' clinical skills in stuttering management? Penman A, Hill AE, Hewat S, Scarinci N. Penman A, et al. Int J Lang Commun Disord. 2021 Nov;56(6):1334-1346. doi: 10.1111/1460-6984.12670. Epub 2021 Sep 14. Int J Lang Commun Disord. 2021. PMID: 34519389 Review.
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• Published: 09 August 2024

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology

• Visar Berisha   ORCID: orcid.org/0000-0001-8804-8874 1 &
• Julie M. Liss 2  

npj Digital Medicine volume  7 , Article number:  208 ( 2024 ) Cite this article

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This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.

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Introduction.

Recently, there has been a surge in interest in leveraging the acoustic properties (how it sounds) and linguistic content (what is said) of human speech as biomarkers for various health conditions. The underlying premise is that disturbances in neurological, mental, or physical health, which affect the speech production mechanism, can be discerned through alterations in speech patterns. As a result, there is a growing emphasis on developing AI models that use speech for the diagnosis, prognosis, and monitoring of conditions such as mental health 1 , 2 , 3 , 4 , 5 , cognitive disorders 6 , 7 , 8 , 9 , 10 , and motor diseases 11 , 12 , 13 , 14 , 15 , among others.

The development of clinical speech AI has predominantly followed a supervised learning paradigm, building on the success of data-driven approaches for consumer speech applications 16 , 17 . For instance, analysis of published speech-based models for dementia reveals that most models rely on high-dimensional speech and language representations 18 , either explicitly extracted or obtained from acoustic foundation models 19 , 20 and language foundation models 21 , 22 , to predict diagnostic labels 9 , 23 , 24 , 25 ; a similar trend is observed for depression 5 , 26 . The foundational models, initially pre-trained on data from general populations, are subsequently fine-tuned using clinical data to improve predictive accuracy for specific conditions. While data-driven classification models based on deep learning have worked well for data-rich applications like automatic speech recognition (ASR), the challenges in high-stakes clinical speech technology are distinctly different due to a lack of data availability at scale. For example, in the ASR literature, speech corpora can amount to hundreds of thousands of hours of speech samples and corresponding transcripts upon which models can be robustly trained in supervised fashion 16 , 17 . In contrast, currently available clinical datasets are much smaller, with the largest samples in the meta-analysis 9 , 24 , 25 consisting of only tens to hundreds of minutes of speech or a few thousand words. This is because clinical data collection is inherently more challenging than in other speech-based applications. Clinical populations are more diverse and present with variable symptoms that must be simultaneously collected with the speech samples, ensuring proper sampling from relevant strata.

Compounding the data problem is the fact that the ground truth accuracy of diagnostic labels for different conditions where speech is impacted varies from 100% certainty to less than 50% certainty, particularly in the early stages of disease when mild symptoms are nonspecific and present similarly across many different diseases 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 . Retrospective data often used to train published models does not always report diagnostic label accuracy or the criteria used to arrive at a diagnosis. Collecting representative, longitudinal speech corpora with paired consensus diagnoses is time-intensive and further impedes the development of large-scale corpora, which are required for developing diagnostic models based on supervised learning. Unfortunately, supervised models built on smaller-scale corpora often exhibit over-optimistic performance in controlled environments 35 and fail to generalize in out-of-sample deployments 36 , 37 . This begs the question of how we can successfully harness the power of AI to advance clinical practice and population health in the context of data availability constraints.

Here we propose that the clinical data constraints provide an opportunity for co-design of new analytics pipelines with lower sample complexity in collaboration with the clinical speech science community. The clinical speech science community has long studied the correlational and causal links between various health conditions and speech characteristics 38 , 39 , 40 , 41 , 42 . This research has focused on the physiological, neurological, and psychological aspects of speech production and perception, primarily through acoustic analysis of the speech signal, and linguistic analysis of spoken language. They involve interpretable and conceptually meaningful attributes of speech, often measured perceptually 43 , via functional rating scales 15 , or self-reported questionnaires 44 . Contributions from speech scientists, neuroscientists, and clinical researchers have deepened our understanding of human speech production mechanisms and their neural underpinnings, and particularly how neurodegeneration manifests as characteristic patterns of speech decline across clinical conditions 43 , 45 .

A co-design of a new explainable analytics pipeline can intentionally integrate scientific insights from speech science and clinical research into existing supervised models. We hypothesize that this will reduce timelines to translation, therefore providing an opportunity to grow clinical data scale through in-clinic use. As data size grows, data-driven methods with greater analytic flexibility can be used to discover new relations between speech and different clinical conditions and to develop more nuanced analytical models that can be confidently deployed for high-stakes clinical applications.

Bridging the gap between speech AI and clinical speech research leads to new opportunities in both fields. There is a clear benefit to the development of more sensitive tools for the assessment of speech for the clinical speech community. Existing instruments for assessment of speech exhibit variable within-rater and between-rater variability 46 . Developing objective proxies for these clinically-relevant constructs has the potential for increased sensitivity and reduced variability. More sensitive objective measures can also catalyze scientific discovery, enabling the identification of yet-to-be-discovered speech patterns across different clinical conditions. Conversely, effectively connecting speech AI research with clinical research enables AI developers to prioritize challenges directly aligned with clinical needs and streamline model building by leveraging domain-specific knowledge to mitigate the need for large datasets. To date, model developers have often overlooked feasibility constraints imposed by the inherent complexity of the relationship between speech production and the condition of interest. For example, recent efforts in clinical speech AI have focused on the cross-sectional classification of depression from short speech samples 5 , 26 . Given the well-documented variability in speech production 47 , the limitations of existing instruments for detecting depression 40 , and the heterogeneity in the manifestation of depression symptoms 48 , it is unlikely that stand-alone speech-based models will yield high-accuracy diagnostic models. Other studies have proposed using speech to predict conditions like coronary artery disease 49 or diabetes 50 . However, to the best of our knowledge, there is no substantial literature supporting the hypothesis that speech changes are specific enough to these conditions to serve as stand-alone indicators. In working with small data sets, understanding the approximate limits of prediction is critical for resource allocation and avoiding unwarranted conclusions that could lead to premature model deployment.

This perspective article advocates for a stronger link between the speech AI community and clinical speech community for the development of scientifically-grounded explainable models in clinical speech analytics. We begin by presenting a new framework for organizing clinical conditions based on their impact on the speech production mechanism (see Fig. 1 ). We believe such a framework is important to facilitate a shared understanding of the impact of clinical conditions on speech and stimulate interdisciplinary thought and discussion. It is useful in categorizing health conditions by the complexity and uncertainty they present for speech-based clinical AI models and provides a mental model for considering the inherent limitations of speech-based classification across different conditions. It orients researchers to consider the challenges posed by limited clinical datasets during model development, and helps prevent frequent methodological errors. This has the potential to expedite progress and further foster collaboration between the speech AI community and the clinical speech community. We then explore various contexts of use for speech analytics beyond cross-sectional classification, highlighting their clinical value and the value they provide to the clinical speech research community (see Fig. 2 ). The discussion further examines how the selected context of use influences model development and validation, advocating for the use of lower-dimensional, individually-validated and explainable measures with potential to reduce sample size requirements (see Fig. 3 ). The paper concludes with a discussion on ethical, privacy, and security considerations, emphasizing the importance of rigorous validation frameworks and responsible deployment (see Fig. 4 ).

The clinically-relevant information in speech

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. Here we use the term ‘speech production’ to refer broadly to the culmination of these spoken language processes. There are several extant speech production models, each developed to accomplish different goals (see, for example 51 , 52 , 53 , 54 , 55 ). Common to these models is that speech begins with a person conceptualizing an idea to be communicated, formulating the language that will convey that idea, specifying the sensorimotor patterns that will actualize the language, and then speaking 56 :

Conceptualization: the speaker forms an abstract idea that they want to verbalize (Abstract idea formulation) and the intention to share through speech (Intent to speak).

Formulation: the speaker selects the words that best convey their idea and sequences them in an order allowed by the language (Linguistic formulation). Then they plan the sequence of phonemes and the prosodic pattern of the speech to be produced (Morphological encoding). Next, they program a sequence of neuromuscular commands to move speech structures (Phonetic encoding).

Articulation: the speaker produces words via synergistic movement of the speech production system. Respiratory muscles produce a column of air that drives the vocal folds (Phonation) to produce sound. This sound is shaped by the Articulator movements to produce speech. Two feedback loops (Acoustic feedback and Proprioceptive feedback) refine the neuromuscular commands produced during the Phonetic encoding stage over time.

Figure 1 introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism. This hierarchy, motivated by initial work on speech and stress 57 , roughly aligns with the three stages of speech production and has direct consequences for building robust clinical speech models based on supervised learning.

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. The three stages are Conceptualization, Formulation, and Articulation. This figure introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism.

This hierarchy compels researchers to ask and answer three critical questions prior to engaging in AI model development for a particular health condition. First, how directly and specifically does the health condition impact speech and/or language? In general, the further upstream the impact of a health condition on speech, the more indeterminate and nuanced the manifestations become, making it challenging to build supervised classification models on diagnostic labels. As we move from lower to higher-order health conditions, there are more mediating variables between the health condition and the observed speech changes, making the relationship between the two more variable and complex.

The second question the model compels researchers to ask and answer is what are the sensitivity and specificity of ground truth labels for the health condition? In general (but with notable exceptions), the objective accuracy of ground truth labels for the presence or absence of a health condition generally becomes less certain from lower to higher-order conditions, adding noise and uncertainty to any supervised classification models built upon the labels. High specificity of ground truth labels is critical for the development of models that distinguish between health conditions with overlapping speech and language symptoms. The answers to these two questions provide a critical context for predicting the utility of an eventual model prior to model building.

Finally, the hierarchy asks model developers to consider the relevant clinical speech symptoms to be considered in the model. In Table 1 , we provide a more complete definition of each level in the hierarchy, a list of example conditions associated with the hierarchy, and primary speech symptoms associated with the condition. The list is not exhaustive and does not consider second and third-order impacts on speech. For example, Huntington’s disease (HD) has a first-order impact on speech causing hyperkinetic dysarthria (e.g. see Table 1 ). But it also has a second- and third-order impact to the extent one experiences cognitive issues and personality changes with the disease. Nevertheless, the table serves as a starting point for developing theoretically-grounded models. Directly modeling the subset of primary speech symptoms known to be impacted by the condition of interest may help reduce sample size requirements and result in smaller models that are more likely to generalize.

Ordering of health conditions based on speech impact

Zeroth-order conditions have direct, tangible effects on the speech production mechanism (including the structures of respiration, phonation, articulation, and resonance) that manifest in the acoustic signal, impacting the Articulation stage in our model in Fig. 1 . This impact of the physical condition on the acoustic signal can be understood using physical models of the vocal tract and vocal folds 58 that allow for precise characterization of the relationship between the health condition and the acoustics. As an example, benign vocal fold masses increase the mass of the epithelial cover of the vocal folds, thereby altering the stiffness ratio between the epithelial cover and the muscular body. The impact on vocal fold vibration and the resulting acoustic signal are amenable to modeling. These types of conditions are physically verifiable upon laryngoscopy, providing consistent ground truth labeling of the condition; and the direct relationship between the condition, its impact on the physical apparatus, and the voice acoustics is direct and quantifiable (although, note that differential diagnosis of vocal fold mass subtype is more difficult, see refs. 59 , 60 ). Thus, zeroth-order health conditions directly impact the speech apparatus anatomy and often have verifiable ground-truth labels.

First-order conditions interfere with the transduction of neuromuscular commands into movement of the articulators (e.g. dysarthria secondary to motor disorder). As with zeroth-order conditions, first-order conditions also disturb the physical speech apparatus and the Articulation stage in our model, however the cause is indirect. Injury or damage to the cortical and subcortical neural circuits and nerves impacts sensorimotor control of the speech structures by causing weakness, improper muscle tone and/or mis-scaling and incoordination of speech movements 61 . The sensorimotor control of speech movements is mediated through five neural pathways and circuits, each associated with a set of cardinal and overlapping speech symptoms: Upper and lower motor neuron pathways; the direct and indirect basal ganglia circuits; and the cerebellar circuit . Damage to these areas causes distinct changes in speech:

The lower motor neurons (cranial and spinal nerves, originating in brainstem and spinal cord, respectively) directly innervate speech musculature. Damage to lower motor neurons results in flaccid paralysis and reduced or absent reflexes in the muscles innervated by the damaged nerves, and a flaccid dysarthria when cranial nerves are involved.

The upper motor neurons originate in the motor cortex and are responsible for initiating and inhibiting activation of the lower motor neurons. Damage to upper motor neurons supplying speech musculature results in spastic paralysis and hyperreflexia, and a spastic dysarthria.

The basal ganglia circuit is responsible for facilitating and scaling motor programs and for inhibiting involuntary movements. Damage to the direct basal ganglia circuit causes too little movement (hypokinesia, as in Parkinson’s disease), resulting in a hypokinetic dysarthria; while damage to the indirect basal ganglia circuit causes too much movement (hyperkinesia, as in Huntington’s disease), resulting in a hyperkinetic dysarthria.

The cerebellar circuit is responsible for fine-tuning movements during execution. Damage to the cerebellar circuits result in incoordination, resulting in an ataxic dysarthria.

Speech symptoms are characteristic when damage occurs to any of these (or multiple) neural pathways, although there is symptom overlap and symptoms evolve in presence and severity as the disease progresses 61 . The diagnostic accuracy and test-retest reliability (within and between raters) of dysarthria speech labels from the speech signal alone (i.e., without knowledge of the underlying health condition) is known to be modest, except for expert speech-language pathologists with large and varied neurology caseloads 62 . Diagnosis of the corresponding health conditions relies on a physician’s clinical assessment and consideration of other confirmatory information beyond speech. Diagnostic accuracy is impacted by the physician’s experience and expertise, whether the symptoms presenting in the condition are textbook or unusual, and whether genetic, imaging, or other laboratory tests provide supporting or confirmatory evidence is available. For example, unilateral vocal fold paralysis is a first-order health condition with direct impact on the speech apparatus (impaired vocal fold vibration) and high-ground truth accuracy and specificity (can be visualized by laryngoscopy). In contrast, Parkinson’s disease (PD) has a diffuse impact on the speech apparatus (affecting phonation, articulation, and prosody) which is hard to distinguish from healthy speech or other similar health conditions (e.g., progressive supranuclear palsy) in early disease. The reported ground-truth accuracy of the initial clinical diagnosis ranges from 58% to 80%, calling into question clinical labels in early stage PD 28 .

Second-order conditions move away from the speech production mechanism’s structure and function and into the cognitive (i.e., memory and language) and perceptual processing domains. These conditions impact the Formulation stage of speaking and manifest as problems finding and sequencing the words to convey one’s intended message and may include deficits in speech comprehension. Alzheimer’s disease (AD) is a second-order condition that deserves particular attention because of the burgeoning efforts in the literature to develop robust supervised classification models 63 . AD disrupts the Formulation stage of speaking with word-finding problems, and the tendency to use simpler and more general semantic and syntactic structures. Natural language processing (NLP) techniques have been used to characterize these patterns and acoustic analysis has identified speech slowing with greater pausing while speaking, presumably because of decreased efficiency of cognitive processing and early sensorimotor changes 9 , 24 , 25 .

While the clinical study of speech and language in AD has consistently found evidence of such pattern changes in individuals diagnosed with probable AD, progress toward developing generalizable speech-based supervised learning clinical models for mild cognitive impairment (MCI) and AD has been relatively slow despite optimistic performance results reported in the literature 35 , 63 . We posit that this can be explained by answers to the first two questions that model in Fig. 1 compels researchers to consider. First, there is a lack of specificity of early speech and language symptoms to MCI and AD, given that the output is mediated by several intermediate stages and the variability associated with speech production. Mild and nonspecific speech and language symptoms will always pose a challenge for the development of clinical early detection/diagnostic speech tools until sufficient training data can result in the identification of distinct signatures (if they exist). Furthermore, given the current difficulty in accurately diagnosing MCI and AD, models based on supervised learning may be unwittingly using mislabeled training data and testing samples in their models. At present, AD is a clinical diagnosis, often preceded by a period of another clinical diagnosis of MCI. MCI is extremely difficult to diagnose with certainty, owing to variability in symptoms and their presentation over time, the overlap of speech and language symptoms with other etiologies, and the diagnostic reliance on self-report 33 . With the current absence of a definitive ground truth label for MCI or early Alzheimer’s disease, and the lack of specificity in speech changes, supervised learning models trained on small, questionably labeled data likely will continue to struggle to generalize to new data.

Third-order conditions impact the Conceptualization stage of speech production and include mental health conditions affecting mood and thought. These conditions can manifest in significant deficits and differences in speech and language, and this has been well-characterized in the literature 4 . For example, acoustic analysis can reveal rapid, pressed speech associated with mania, as well as slowed speech without prosodic variation that might accompany depression. Natural language processing can reveal and quantify disjointed and incoherent thought in the context of psychiatric disorders 64 . Despite this, the impact of these mood and thought conditions on the speech apparatus and language centers in the brain may be indirect and nonspecific relative to low-order conditions. Mental health conditions frequently cause a mixture or fluctuation of positive symptoms (e.g., hallucinations, mania) and negative symptoms (e.g., despondence, depression), which can present chronically, acutely, or intermittently. The associated speech and language patterns can be attributed to any number of other reasons (fatigue, anxiety, etc.) With regard to ground-truth accuracy and specificity, studies have shown that around half of schizophrenia diagnoses are inaccurate 65 . This problem has resulted in a push to identify objective biomarkers to distinguish schizophrenia from anxiety and other mood disorders 66 , 67 . This complicates the development of models for health condition detection and diagnosis; however, machine-learning models may be developed to objectively measure speech and language symptoms associated with specific symptomatology. For example, distinguishing between negative versus positive disease symptoms may be achievable with careful construction of speech elicitation tasks and normative reference data, given the central role that language plays in the definition of these symptoms 68 , 69 .

Across all health conditions, extraneous and comorbid factors can exert meaningful influence on speech production. For example, anxiety, depression, and fatigue, perhaps even as a consequence of an underlying illness, are known to impact the speech signal. It would not be straightforward to distinguish their influence from those of primary interest, adding complexity and uncertainty for models based on supervised learning, regardless of the health condition’s order. However, the increased variability in both data and diagnostic accuracy for many higher-order conditions makes speech-based models trained using supervised learning on small datasets vulnerable to reduced sensitivity and specificity. This is not merely a matter of augmenting the dimensionality of speech features or enlarging the dataset; it reflects the intrinsic variability in how humans generate speech. Finally, the accuracy and specificity of ground truth labels for health conditions are critical to consider in assessing the feasibility of interpretable model development. Unlike the static link between speech and the health condition, as diagnostic technologies advance and criteria evolve, the accuracy of these labels is expected to improve over time, thereby potentially enabling more robust model development.

Defining an appropriate context of use

As mentioned before, most published clinical speech AI development studies are based on supervised learning where developers build AI models to distinguish between two classes or to predict disease severity. This approach generally presumes the same context of use for clinical speech analytics across different applications: namely, the cross-sectional detection of a specific condition or a prediction of clinical severity based on a speech sample. As we established in the foregoing discussion, this approach, when combined with limited training data, is less likely to generalize.

Nevertheless, there are a number of use cases, in which speech analytics and AI can provide more immediate value and expedite model translation. These are outlined in Fig. 2 , where we explore these applications in greater depth. Focusing on these use cases will reduce timelines to translation, providing an opportunity to grow clinical data scale through in-clinic collection. With increased data size and diversity, researchers will better characterize currently-unknown fundamental limits of prediction for speech-based classification models for higher-order conditions (e.g. how well can we classify between depressed and non-depressed speech); and can bring to bear more advanced data-driven methods to problems that provide clinical value.

A listing of different contexts of use for the development and validation of clinical tools based on speech AI.

Diagnostic assistance

Despite rapid advancements in biomedical diagnostics, the majority of neurodegenerative diseases are diagnosed by the presence of cardinal symptoms on clinical exams. As discussed previously and as shown in Table 1 , many health conditions include changes in speech as a core symptom. For example, diagnosis of psychiatric conditions involves analysis of speech and language attributes, such as coherence, fluency, and tangentiality 70 . Likewise, many neurodegenerative diseases lead to dysarthria, and a confirmatory speech deficit pattern can be used to support their diagnoses 61 . Tools for the assessment of these speech deficit patterns in the clinical setting typically depend on the clinical judgment or on scales reported by patients themselves. There is a large body of evidence indicating that these methods exhibit variable reliability, both between different raters and within the same rater over time 46 , 62 . Clinical speech analytics has the potential to enhance diagnostic accuracy by providing objective measures of clinical speech characteristics that contribute to diagnosis, such as hypernasality, impaired vocal quality, and articulation issues in dysarthria; or measures of coherence and tangentiality in psychosis. These objective measures can provide utility for manual diagnosis in clinic or can be used as input into multi-modal diagnostic systems based on machine learning.

Non-specific risk assessment tools

While differential diagnosis based on speech alone is likely not possible for many conditions, progressive and unremitting changes in certain aspects of speech within an individual can be a sign of an underlying illness or disorder 61 . Clinical speech analytics can be used to develop tools that track changes in speech along specific dimensions known to be vulnerable to degradation in different conditions. This could provide value as an early-warning indicator, particularly as the US health system moves toward home-based care and remote patient monitoring. Such a tool could be used as a non-specific risk assessment tool triggering additional tests when key speech changes reach some threshold or is supported by changes in other monitored modalities.

Longitudinal tracking post-diagnosis

In many conditions, important symptoms can be tracked via speech post-diagnosis. For example, tracking bulbar symptom severity in ALS, as a proxy for general disease progression, can provide insights on when AAC devices should be considered or to inform end-of-life planning 71 . In Parkinson’s disease, longitudinal tracking of speech symptoms would be beneficial for drug titration 72 , 73 . In dementia, longitudinal tracking of symptoms measurable via speech (e.g. memory, cognitive-linguistic function) can provide valuable information regarding appropriate care and when changes need to be made.

Speech as a clinically meaningful endpoint

Speech is our principal means of communication and social interaction. Conditions that impair speech can severely hinder a patient’s communicative abilities, thereby diminishing their overall quality of life. Current methods for assessing communication outcomes include perceptual evaluations, such as listening and rating, or self-reported questionnaires 61 , 69 . In contrast to the use case as a solitary diagnostic tool, employing clinical speech analytics to objectively assess communicative abilities is inherently viable across many conditions. This is due to the direct correlation between the construct (communicative ability) and the input (speech). For instance, in dysarthria, clinical speech analytics may be utilized to estimate intelligibility, the percentage of words understood by listeners, which significantly affects communicative participation 74 . In psychosis, speech analytics can facilitate the creation of objective tools for assessing social competencies; these competencies are closely tied to quality of life indicators 69 . Similarly, in dementia, a decline in social interaction can lead to isolation and depression, perhaps hastening cognitive decline 75 . A related emerging use case in Alzheimer’s disease is providing context for blood-based diagnostics. As new biomarkers with confirmatory evidence of pathophysiology emerge, there will likely be an increase in Alzheimer’s diagnoses without co-occurring clinical-behavioral features. The group of patients with AD diagnoses, but without symptoms, will require context around this diagnosis. Speech analytics will be important as measures of behavioral change that are related to quality of life.

Improving clinical trial design

The Food and Drug Administration (FDA) prioritizes patient-relevant measures as endpoints in clinical trials. They have also identified speech and communication metrics as particularly underdeveloped for orphan diseases 76 . Objective and clinically-meaningful measures based on speech analytics that are collected more frequently can result in an improved sensitivity for detecting intervention effects. Such measures have the potential to decrease the required sample sizes for drug trials, enable more efficient enrollment, or to ascertain efficacy with greater efficiency 77 .

Facilitating development of digital therapeutics

There has been significant recent interest in development of digital therapeutics for various neurological and mental health conditions. Several of these devices target improving the patients’ social skills or communication abilities 78 . In this evolving space, introducing concrete digital markers of social competence allows for more efficient evaluation of efficacy and precision approaches for customizing therapeutics for the patient.

Development and validation of robust models

The context of use profoundly influences the development of clinical speech AI models, shaping their design, validation, and implementation strategies. For example, for contexts of use involving home monitoring, robustness to background noise, variability in recording conditions and usability are essential. For longitudinal monitoring, developed tools must be sensitive to subtle changes in speech characteristics relevant to the progression of the condition being monitored. This necessitates longitudinal data collection for development and validation to ensure stability and sensitivity over time. Screening tools in diverse populations require a training dataset that captures demographic variability to avoid bias. Solutions based on noisy diagnostic labels may require uncertainty modeling through Bayesian machine learning or ensemble methods that quantify prediction confidence 79 . Concurrently, techniques like label smoothing 80 and robust loss functions 81 can enhance model resilience under label noise.

Each context of use presents a custom development path to address the unique challenges and a parallel validation strategy that spans hardware, analytical validation, and clinical validation - see Fig. 3 . The current approach focused on data-driven supervised learning on diagnostic labels limits the development and understanding of new models and makes model validation challenging. While there are many validation metrics for evaluating AI model performance, the prevalent metrics in published speech-based models primarily focus on estimating “model accuracy” (e.g. what percent of the time does the model correctly classify between Healthy and Dementia labels based on speech) using a number of methods (e.g. cross-validation, held-out test accuracy). However, accurately estimating the model accuracy of high-dimensional supervised learning models is challenging, and current methods are prone to overoptimism 35 . In addition, many supervised machine learning models are sensitive to input perturbations, which is a significant concern for speech features known for their day-to-day variability 82 . Consequently, model performance diminishes with any temporal variation in the data.

The development of clinical speech AI models begins with a context of use. The context of use informs downstream development and validation of resulting models. The Verification, Analytical Validation, and Clinical Validation (V3) framework has been proposed as a conceptual framework for the initial validation of biometric monitoring technologies.

A starting point for clinical model validation is the Verification/Analytical Validation/Clinical Validation (V3) framework, a framework for validating digital biometric monitoring technologies. The original version of the framework proposes a structured approach with three evaluation levels: Verification of hardware, Analytical Validation, and Clinical Validation 83 . This framework has roots in principles of Verification and Validation for software quality product management and deployment 84 . While these existing validation systems are designed to confirm that the end system accurately measures what it purports to measure, the V3 framework adds the additional step of confirming that the clinical tools are meaningful to a defined clinical population. To that end, Verification ascertains the sensor data’s fidelity within its intended environment. Analytical validation examines the accuracy of algorithms processing sensor data to yield behavioral or physiological metrics, and clinical validation evaluates clinical model outputs with clinic ground truths or established measures known to be meaningful to patients. This includes existing clinical scales like the PHQ-9 (depression) or the UPDRS (Parkinson’s disease). In Fig. 3 we provide a high-level overview of the end-to-end development and validation process for clinical speech AI. It is important to note that the V3 is a conceptual framework that must be specifically instantiated for the validation of different clinical speech applications. While it can help guide the development of a validation plan, it does not provide one out of the box. Furthermore, this level of validation is only a starting point as the FDA suggests constant model monitoring post-deployment to ensure continued generalization 85 .

Supervised learning approaches based on uninterpretable input features and clinical diagnostic labels make adoption of the complete V3 framework challenging. Analytical validation is especially challenging as it’s difficult to ensure that learned speech representations are measuring or detecting physiological behaviors of interest. For example, in Parkinson’s disease, both the speaking rate and the rate of opening and closing of vocal folds is impacted. Uninterpretable features have unknown relationships with these behavioral and physiological parameters. As an alternative, model developers can use representations that are analytically validated relative to these constructs. This would lead to more interpretable clinical models. Validation should be approached end-to-end during the development process, with different stages (and purposes of analysis) employing different validation methods. Small-scale pilot tests may focus on parts of this framework. However, for work with deployment as a goal, ensuring generalizability and clinical utility requires validating the hardware on which the speech was collected, ensuring that intermediate representations are valid indicators of behavioral and physiological measures (e.g speaking rate, articulatory precision, language coherence), and clinical models developed using these speech measures are associated with existing clinical ground truths or scales that are meaningful to patients 86 .

Interpretable, clinically-important measures based on speech are currently missing from the literature. Clinically-relevant feature discovery and model performance evaluation in speech analytics are challenged by the high-dimensionality of speech, complex patterns, and limited datasets. Table 1 highlights several speech constructs that have been studied relative to various conditions; however, most of these constructs do not have standardized operational definitions in the clinical speech analytics literature. Instead, model developers rely on high-dimensional representations that have been developed for other purposes. For example, adopted from the ASR literature, many clinical models use representations based on mel-frequency cepstral coefficients or mel-spectra 18 ; or representations learned by pre-trained foundation models 19 , 20 . However, these features are not interpretable, making analytical and clinical validation challenging.

Development of a clinically-tailored speech representation could significantly refine the development process, favoring smaller, individually validated, and clinically-grounded features that allow scientists to make contact with the existing literature and mitigate model overfitting and variability. This field would benefit from a concerted and synergistic effort in the speech AI community and the speech science community to operationalize and validate a measurement model for the intermediate constructs like those listed in Table 1 87 . For example, in our previous work, we made progress in this direction by developing measurement models for the assessment of hypernasality and consontant-vowel transitions and used it to evaluate cleft lip and palate and dysarthria 88 , 89 ; several measures of volition and coherence for schizophrenia 69 ; and measures of semantic relevance for dementia 10 . Individually-validated interpretable measures allow for easier alignment to different contexts of use, integration within larger multi-modal systems, and establish a more direct link to the existing clinical literature. Furthermore, they can be used as a way of explaining the operation of larger, more complex models via bottleneck constraints 90 or they can be combined with new methods in causal machine learning for development of explainable models 91 .

Finally, clinically-interpretable representations can also play a pivotal role in integrating the patient’s perspective into the design of algorithms. The idea is that by aligning closely with the lived experiences and symptoms important to patients, these representations ensure that algorithmic outcomes resonate with the quality of life impact of health conditions. The hypothesis is that this patient-centric approach could have the added benefit of reinforcing patient trust and engagement in digital health.

Ethical, privacy, and security considerations

The deployment and regulation of clinical speech models in healthcare present multiple challenges and risks. Prematurely launched models (without robust validation) risk delivering clinically inaccurate results and potentially causing patient harm, while biases in model training can lead to skewed performance across diverse populations. Moreover, the use of speech data for health analytics raises significant privacy and security concerns. We outline these considerations in Fig. 4 and expand on them below.

An overview of key risks and corresponding mitigation strategies for the development of clinical speech AI models.

Premature deployment of inaccurate models

A primary risk of prematurely-deployed models is that they will provide clinically inaccurate output. As discussed in previous work 35 , current strategies to validate AI models are insufficient and produce overoptimistic estimates of accuracy. Several studies have highlighted this as a more general problem in AI-based science 92 , 93 . However, reported accuracy metrics carry much weight when presented to the public and can lead to premature deployment. There is considerable risk that these models will fail if deployed and potentially harm patients 94 . For example, consider the Cigna StressWaves Test model, deployed after only internal evaluation and no public efficacy data. This model analyzes a user’s voices to predict their stress level and is publicly available on the Cigna Website. Independent testing of the model reveals that it has poor test-retest reliability (measured via intraclass correlation) and poor agreement with existing instruments for measuring stress 37 .

Biased models

An additional risk of clinical speech-based models stems from the homogeneity of the data often used to train these models. Biological and socio-cultural differences contribute significantly to the variation in both the speech signal and the clinical conditions (impacting aspects from risk factors to treatment efficacy). Careful consideration of these differences in model building necessitates robust experiment design and representative stratification of data. However, a recent study demonstrates that published clinical AI models are heavily biased demographically, with 71% of the training data coming from only three states: California, Massachusetts, and New York, with 34 of the states not represented at all 95 . Similarly, analysis of clinical speech datasets indicates a significant skew towards the English language, overlooking the linguistic diversity of global populations. To accurately capture health-related speech variations, it’s essential to broaden data collection efforts to include a more representative range of the world’s native languages as health-related changes in speech can be native language-specific 96 . It becomes challenging to determine how models trained on unrepresentative data would perform when deployed for demographic groups for which they were not trained.

Privacy and security considerations

Speech and language data is widely available and, as we continue to interact with our mobile devices, we generate an ever-growing personal footprint of our health status. Previous studies have shown that this data (speeches, social media posts, interviews) can be analyzed for health analytics 97 , 98 , 99 . There is a risk that similar data on an even larger scale and over longer periods of time can be accessed by technology companies to make claims about the health or emotional state of their users without their permission or by national or international adversaries to advance a potentially false narrative on the health of key figures. The risks to the privacy of this type of analysis, if used outside of academic research, is considerable, with national and international political ramifications. Internally, political adversaries can advance a potentially false narrative on the health of candidates. Internationally, geopolitical adversaries could explore this as an additional dimension of influence in elections.

There is no silver bullet to reduce these risks, however, there are several steps that can be taken as mitigation strategies. With the public availability of speech technology, building AI models has become commoditized; however, the bottleneck remains prospective validation. Thorough validation of the model based on well-accepted frames such as the V3 framework is crucial prior to deployment 83 . This validation must extend beyond initial data sets and include diverse demographic groups to mitigate biases. Moreover, developers should engage in continuous post-deployment monitoring to identify and rectify any deviations in model performance or emergent biases. Transparency in methodology and results, coupled with responsible communication to the public, can reduce the risks of misperceived model accuracy.

On the privacy front, there are emerging technical solutions to parts of this problem based on differential privacy and federated learning 100 , 101 , 102 ; however, a complete socio-technical solution will require stringent data protection regulations and ethical guidelines to safeguard personal health information. First, it is wise to reconsider IRB review protocols in light of new technologies and publicly available data; in industry, proactive collaboration with regulatory bodies (e.g. FDA) can help establish clear guidelines. This is clear for companies focused on clinical solutions, however, the regulation of AI-based devices for technology companies, particularly those focused on wellness, is less well-defined. Recent guidance from the Federal Trade Commission (FTC) advising companies to only make evidence-backed claims about AI-driven products is a step in the right direction 103 .

Data availability

There is no data associated with this manuscript as it is a perspectives article centered around a theoretical framework.

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Acknowledgements

This work is funded in part by the John and Tami Marick Family Foundation, NIH NIA grant 1R01AG082052-01, NIH NIDCD grants R01DC006859-11 and R21DC019475, and NIH NIDCR grant R21DE026252-01A.

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College of Health Solutions, Arizona State University, Tempe, AZ, USA

Julie M. Liss

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V.B. and J.M.L. both made substantial contributions to the conception or design of this work and they helped draft and revise the manuscript. Berisha is the corresponding author: [email protected].

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Correspondence to Visar Berisha .

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The Authors declare no Competing Non-Financial Interests but the following Competing Financial Interests: Berisha and Liss are founders of and previously held equity in Aural Analytics, a clinical speech analytics company.

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Berisha, V., Liss, J.M. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. npj Digit. Med. 7 , 208 (2024). https://doi.org/10.1038/s41746-024-01199-1

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Received : 23 November 2023

Accepted : 19 July 2024

Published : 09 August 2024

DOI : https://doi.org/10.1038/s41746-024-01199-1

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