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Medical problem solving.
Medical problem-solving skills are essential to learning how to develop an effective differential diagnosis in an efficient manner, as well as how to engage in the reflective practice of medicine. Students' experience in CBI complements the clinical reasoning skills they learn through the UA COM Doctor and Patient course and through their Societies mentors.
The UA COM medical problem-solving structure applies the B-D-A ( Before-During-After) framework as an educational strategy. Thus, CBI requires students to engage in reflection before, during and following facilitated sessions. Reflection contributes to improvement in problem-solving skills and helps medical students cultivate a habit of reflection that will serve them well as they become lifelong professional learners.
As with medical-problem solving, practice-based learning (learning through experience) requires students to engage in reflection before, during and following each learning experiences. Reflection contributes to improvement in problem-solving skills and cultivating a habit of reflection will serve medical students well as they become lifelong professional learners.
B-D-A Framework Reflective Learning Guide Cognitive Error Quick Guide
Department of Internal Medicine
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- Clinical Problem Solving/Diagnostic Reasoning Session
Background: Clinical reasoning remains a central skill of the successful clinician. To improve or master these skills, it is essential to outline the cognitive steps that lead to success in eliciting, framing and then solving medical problems. In this era of increasingly hectic time schedules and duty hour restrictions, it is less common for the learner to hear a master clinician’s distinct steps used when solving problems. Moreover, since reasoning transpires largely at the subconscious level, it can be difficult for busy clinicians to articulate the steps in their thought process. Thus, more inexperienced learners are left to pick up reasoning in bits and pieces and to figure out their own way to “connect the dots” when faced with a patient problem. The clinical problem solving/diagnostic reasoning session is designed to provide a window into the master clinician’s thought process and reasoning strategies. The session serves as a valuable tool to learn and teach the process of hypothesis generation (eliciting the right question), problem representation (problem list), prioritized differential diagnosis and finally problem-solving strategies (pattern recognition and analytical reasoning).
Clinical Problem Solving Session Goal: to provide a mechanism for residents to learn the skills and to become more adept at clinical reasoning by listening to expert clinicians talk through process of making decisions and actively participating in discussion around a clinical problem.
Objectives: By making the thought process of a master clinician more transparent, the clinical reasoning session will:
- highlight the intricacies of the diagnostic process.
- help learners better understand how an experienced clinical formulates complex clinical decisions by combining prior experiences with evidence-based knowledge.
- demonstrate history-taking, examination and diagnostic skills as a foundation for clinical reasoning.
- teach the process of progressive problem solving.
- outline the cognitive steps that lead to success in eliciting, framing and then solving medical problems.
- engage learners in the diagnostic process.
- allow master clinicians to serve as role models for less experienced learners.
- help develop a collective espirit de corps while developing skills and confidence with the diagnostic reasoning process.
Clinical Problem Solving Session Format:
- The 55- minute session uses the “Stump the Professor” format.
- Those chosen to serve as master clinician will not have heard the case.
Introduction to session by the facilitator (5 minutes)
- articulate goals and objectives of the session
- remind all participants that:< >diagnostic reasoning is not “magic” but rather a skill that is developed over timean individual does not need to know everything to participatethe session in about the process; getting the diagnosis right is relatively unimportantoutline session format
Presentation of case by the facilitator with responses/discussion by master clinicians (30 minutes)
- The clinical case will be presented in short segments rather than all at once. This allows for an infusion of information which allows for discussion breaks and gives the master clinician an opportunity to verbalize his/her thought process as well as to teach the process of progressive problem solving to the learners throughout the session.
- The clinician is expected to “think out loud” allowing the learners insight into the thought process of a master clinician.
- The master clinician is also expected to keep the audience engaged in solving the case throughout the presentation.
- Session participants are expected to be actively engaged in asking questions and sharing their reasoning.
Discussion led by facilitator (15 minutes)
- comments on the process after the diagnosis is revealed.
- comments on the diagnosis from the audience.
- brief presentation of any new relevant knowledge on specific topics raised by the case.
Teaching points and wrap-up led by facilitator as well as completion of survey (5 minutes)
- teaching points will focus on physical diagnosis and diagnostic reasoning
- short survey to participants
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Developing Physician Leaders’ Complex Problem-Solving Skills
by Mitchell Kusy, PhD | Steve Wiesner, MD
March 8, 2022
Real-time, real-work simulations provide opportunities for organizations to jump-start physician leaders’ complex problem-solving skills.
Complex problem-solving is difficult for any leader. Fortunately, physicians have an edge, as they solve problems hundreds of times every day in their clinical practice and research endeavors. That’s the good news. The bad news is that problem-solving does not necessarily come as easily when physicians enter formal leadership roles.
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Resources & publications, membership & community, for over 45 years..
The American Association for Physician Leadership has helped physicians develop their leadership skills through education, career development, thought leadership and community building.
The American Association for Physician Leadership (AAPL) changed its name from the American College of Physician Executives (ACPE) in 2014. We may have changed our name, but we are the same organization that has been serving physician leaders since 1975.
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The Lean Post / Articles / Engaging Physicians to Solve Real Problems in Healthcare
Engaging Physicians to Solve Real Problems in Healthcare
By Jack Billi
June 24, 2013
Trained in the scientific method, practical to the core, focused on the patient--one would think physicians would be the first adopters of lean thinking and practice, but many healthcare professionals resist learning more about what lean can do for them. Dr. Jack Billi explains why this may be and makes the case for lean in healthcare.
Healthcare grew up as a cottage industry, with notoriously weak process management and unclear responsibilities for costs. As healthcare organizations now strive to fix their broken processes and provide greater value (high quality care at a reasonable cost), one of the barriers often mentioned is ‘difficulty engaging physicians’ in lean improvement.
I just returned from the Lean Healthcare Transformation Summit in Orlando. Lean thinking is spreading rapidly across healthcare organizations in the US and across the globe. Early adopters such as Virginia Mason and ThedaCare have powerfully demonstrated lean’s potential to transform healthcare. Over 200 organizations participated in the 2013 Summit, and we have a long way to go.
Physicians are natural “fixers” who love to solve problems and puzzles. Medical students are selected for this attribute, among others. Future physicians are trained to use the scientific method to diagnose and treat patients’ medical problems. They learn how to make direct observations of the patient, asking questions in a systematic manner, as part of the history and physical exam. Like lean practitioners, physicians are trained to “Grasp the Situation” by systematically observing the work and identifying problems in the gemba .
As physicians, we use scientific problem solving daily when we compare our patient’s findings with known syndromes and diseases, to create a hypothesis about the patient’s tentative diagnosis. We use root cause analysis in our “Impression”, including alternative explanations (“The jaundice might be caused by biliary obstruction or a reaction to a nausea drug”), and in developing a Plan of care (countermeasures). The hypothesis is tested ( Do ) and revised by further diagnostic testing or by response to treatment, a form of Check and Adjust . No physician I know would consider treating or performing surgery on a patient he or she had not personally examined. We must go to the gemba, so to speak.
So if lean thinking is just another version of what physicians do every day in taking care of patients, why don’t all doctors naturally gravitate to lean? Here are some common themes:
- Like nurses, as physicians many of us have had to become “ workaround artists” to get through our day. Doctors perform daily heroics to get their patients the care they need, despite being frequently frustrated by fragmented systems of care and broken processes. Doctors know that the ‘current state’ is deeply flawed, and some have lost hope that they can improve the work.
- Some physicians have developed a deep-seated wariness of corporate improvement programs, having experienced flavor of the month cost efficiency and re-engineering programs. They may cynically believe that lean is just the latest cost cutting program imported from another industry, rather than a path to value creation.
- Lean vocabulary is obscure to newcomers, and the term “ standard work ,” if not properly explained, may be off-putting for physicians. Doctors value using critical thinking skills in service to their patients. They don’t want to practice cookbook medicine, or have someone outside of the profession (e.g., the government or an insurance company…) tell them how best to take care of their patients.
So what’s the prescription for engaging physicians?
Lean is practical to its core. Helping physicians “learn it by doing it” can help overcome resistance. When physicians can see for themselves that scientific problem-solving improves patients’ experience while making it easier for them to do their work, most become converts. For this reason I suggest always scoping a problem or project to ensure it includes some representation or telling of the physician’s pain with the current process.
The bad rap on standard work, I believe, reflects a misunderstanding of what it really is. If standard work is explained to physicians as the best way we know now to practice so as to reliably produce desired results, resistance will melt away. Standard work should be viewed as how we’ve designed our work to consistently deliver safe, effective care. Standard work makes it possible for physicians to apply their creativity to improving work methods. Without standard work, how would anyone know if a change is actually an improvement?
Since lean thinking is essentially the scientific method, practiced through iterative cycles of PDCA , physicians already have the mindset to be lean thinkers. We pride ourselves on practicing evidence-based medicine. Physicians are natural allies in a lean transformation. What’s not to like about a method that makes it easier for the doctor to do his or her job, and do it better? The challenge is to apply the same rigorous thinking we use to work up patient problems to solve the ongoing problems we experience in our organizations.
I wonder how many of my fellow physicians see it the same way?
About Jack Billi
Dr. Jack Billi serves as Professor of Internal Medicine and Medical Education at the University of Michigan Medical School, and as Associate Vice President for Medical Affairs of the University of Michigan. He leads the Michigan Quality System, the University of Michigan Health System’s lean transformation strategy. Dr. Billi’s research and management interests include the use of lean thinking to improve quality, safety and efficiency in health care, evidence-based guidelines, population health, clinical practice transformation tied to performance-based differential reimbursement, and conflict of interest management. Billi is active in organized medicine and collaborative quality improvement initiatives in Michigan, and is involved nationally and internationally in developing guidelines and educational programs for cardiac resuscitation.
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Learning to Solve Problems By… Wait for It… Solving Problems
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Video by James (Jim) Womack, PhD
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[Methods for teaching problem-solving in medical schools]
- PMID: 6714143
The need to include in the medical curriculum instructional activities to promote the development of problem-solving abilities has been asserted at the national and international levels. In research on the mental process involved in the solution of problems in medicine, problem-solving has been defined as a hypothetical-deductive activity engaged in by experienced physicians, in which the early generation of hypotheses influences the subsequent gathering of information. This article comments briefly on research on the mental process by which medical problems are solved. It describes the methods that research has shown to be most applicable in instruction to develop problem-solving abilities, and presents some educational principles that justify their application. The "trail-following" approach is the method that has been most commonly used to study the physician's problem-solving behavior. The salient conclusions from this research are that in the problem-solving process the diagnostic hypothesis is generated very early on and with limited data; the number of hypotheses is small; the problem-solving approach is specific to the type of medical problem and case in hand; and the accumulation of medical knowledge and experience forms the basis of clinical competence. Four methods for teaching the solution of problems are described: case presentation, the rain of ideas, the nominal groups technique and decision-making consensus, the census and analysis of forces in the field, and the analysis of clinical decisions. These methods are carried out in small groups. The advantages of the small groups are that the students are active participants in the learning process, they receive formative evaluation of their performance in a setting conductive to learning, and are able to interact with their instructor if he makes proper use of the right questioning techniques. While no single problem-solving method can be useful to all students or in all the problems they encounter, teachers of medicine can improve their students' performance by adjusting these available methods to their particular needs and to those of their schools. The problem-solving methods described can help teachers shape the learning environment so as to develop in their students the most coherent, logical, concrete and complete set of skills possible. These methods can so be of value in improving the training of future doctors and the quality of their decisions to the benefit of their patients.
- Impact of undergraduate medical training on housestaff problem-solving performance: implications for problem-based curricula. Patel VL, Arocha JF, Leccisi MS. Patel VL, et al. J Dent Educ. 2001 Nov;65(11):1199-218. J Dent Educ. 2001. PMID: 11765866
- [Introduction of a brief Problem-Based-Learning (PBL) experience in traditional medical faculty curriculum]. Heyman SN, Reches A, Safadi R, Cohen R. Heyman SN, et al. Harefuah. 2007 Jun;146(6):435-8, 502, 501. Harefuah. 2007. PMID: 17760396 Hebrew.
- Solving the problem of how medical students solve problems. Stevens RH, McCoy JM, Kwak AR. Stevens RH, et al. MD Comput. 1991 Jan-Feb;8(1):13-20. MD Comput. 1991. PMID: 2011052
- Critical thinking a new approach to patient care. Sullivan DL, Chumbley C. Sullivan DL, et al. JEMS. 2010 Apr;35(4):48-53. doi: 10.1016/S0197-2510(10)70094-2. JEMS. 2010. PMID: 20399376 Review.
- Current trends in developing medical students' critical thinking abilities. Harasym PH, Tsai TC, Hemmati P. Harasym PH, et al. Kaohsiung J Med Sci. 2008 Jul;24(7):341-55. doi: 10.1016/S1607-551X(08)70131-1. Kaohsiung J Med Sci. 2008. PMID: 18805749 Review.
- Performance of a core of transversal skills: self-perceptions of undergraduate medical students. Ribeiro L, Severo M, Ferreira MA. Ribeiro L, et al. BMC Med Educ. 2016 Jan 15;16:18. doi: 10.1186/s12909-016-0527-2. BMC Med Educ. 2016. PMID: 26772744 Free PMC article.
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Cognitive Problem Solving Patterns of Medical Students Correlate with Success in Diagnostic Case Solutions
1 Lehrstuhl für Didaktik und Ausbildungsforschung in der Medizin am Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany
Anja görlitz, matthias holzer, martin r. fischer, ralf schmidmaier.
2 Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany
Conceived and designed the experiments: RE AG MH MRF RS. Performed the experiments: RE. Analyzed the data: JK RE MH RS. Contributed reagents/materials/analysis tools: JK RE AG MH RS MRF. Wrote the paper: JK RE AG MH RS MRF.
Problem-solving in terms of clinical reasoning is regarded as a key competence of medical doctors. Little is known about the general cognitive actions underlying the strategies of problem-solving among medical students. In this study, a theory-based model was used and adapted in order to investigate the cognitive actions in which medical students are engaged when dealing with a case and how patterns of these actions are related to the correct solution.
Twenty-three medical students worked on three cases on clinical nephrology using the think-aloud method. The transcribed recordings were coded using a theory-based model consisting of eight different cognitive actions. The coded data was analysed using time sequences in a graphical representation software. Furthermore the relationship between the coded data and accuracy of diagnosis was investigated with inferential statistical methods.
The observation of all main actions in a case elaboration, including evaluation, representation and integration, was considered a complete model and was found in the majority of cases (56%). This pattern significantly related to the accuracy of the case solution (φ = 0.55; p<.001). Extent of prior knowledge was neither related to the complete model nor to the correct solution.
The proposed model is suitable to empirically verify the cognitive actions of problem-solving of medical students. The cognitive actions evaluation, representation and integration are crucial for the complete model and therefore for the accuracy of the solution. The educational implication which may be drawn from this study is to foster students reasoning by focusing on higher level reasoning.
The physician's profession demands a number of competencies. One of these is the ability to reason clinically. Clinical reasoning focuses on the signs and symptoms of a patient and the subsequent identification of relevant questions on the patient´s history, further the physical examination, the correct interpretation of those results and information, as well as procedures required to reach the correct diagnosis in an efficient manner  . The actual reasoning process involves medical decision-making on the one hand and problem-solving on the other hand  . This study focuses on medical problem-solving. There is a broad base of knowledge on expertise of physicians and their decision-making (cf.  ), but only little is known about cognitive actions of medical students. This lack of knowledge exacerbates attempts of medical educators to foster problem-solving adapted to their students’ needs. This study focuses therefore only on medical students. Prior knowledge is essential for successful problem-solving as shown by various studies regarding “content specificity”  ,  . Previous research has identified a spectrum of four consecutive strategies for problem-solving in medicine: guessing, hypothetical-deductive reasoning, scheme induction and pattern recognition  . With increasing knowledge and experience, medical students derive hypotheses from the patient’s information and try to verify them purposefully. These strategies of generating and testing of hypotheses have successfully been observed empirically  –  and described in detail  ,  –  . In the last decade there has been a tendency towards case-based learning as an instructional approach for students to learn medical problem-solving  ,  . To foster the development of expertise early in medical careers learning from authentic patient cases has been stipulated  . The key to successful learning of medical students seems to lie in the consequent process character of the cases  . Despite this empirical basis it remains hard to assess the verification if, when and how to foster medical students’ problem-solving skills. Even more, there is currently no established model in medical education to accurately describe the cognitive process of clinical problem solving. In order to educate with a resource-oriented instructional approach it is a prerequisite to first investigate the actual process of medical student's problem-solving.
When confronted with a problem, humans tend to take the same cognitive actions regardless of the content of the problem  . Cognitive actions could be defined as follows: the retrieval of the problem, the processing of the information, a formulation of the plan to tackle the task, carrying out the plan and an evaluation of the results. These cognitive actions have been thoroughly researched and are found in abundance known as action theoretic approaches in cognitive psychology  ,  , mathematics  , pedagogy  , in medicine  and many other fields  . A medical problem-solving process including the underlying cognitive actions could be exemplified as follows: When a patient sees a doctor, the doctor recognizes or finds out about the symptoms of the patient (i.e. she complains about red urine), analyses these symptoms and generates differential diagnostic ideas (i.e. urinary tract infection). In order to get more information the physician asks further questions and performs further investigations (i.e. by examining the patient and carrying out a urine sample and a blood test). When presenting the patient to another physician, the doctor would summarize what he or she has learned so far from an inner representation of the patient (i.e. 57 year old female patient, hematuria since three days, no signs of an infection). This inner representation includes positive and negative findings and might as well contain differential diagnostic ideas (i.e. malignant tumour or glomerulonephritis). After an evaluation of the differential diagnoses, decisions about further steps would be reached and communicated to the patient. All models include the above mentioned cognitive actions with varying emphasis  . These cognitive actions serve as the foundation of the strategies of problem-solving within a field including medicine. A more adaptable and faster learning of clinical reasoning founding on the empirical verification of cognitive actions has been stipulated very recently  ,  . The model using typified objects (MOTmodel) comprehensively describes cognitive clinical reasoning process as suggested by experts. On the top-level of this hierarchically built model the experts agreed on the following processes: Identify early cues, determine the objectives of the encounter, categorize for the purpose of action, implement purposeful action and evaluate the results. All processes are interlinked and receive specific inputs and produce certain outputs thus representing the dynamic nature of the problem-solving process of experts. However, cognitive actions were not examined empirically among medical students. This is especially surprising as the development of medical students’ problem-solving skills could be fostered using knowledge about an optimum relation of cognitive actions. Furthermore, so far there is no evidence available that using certain cognitive action models predict successful case solutions.
The aim of this study was to empirically examine how medical students think clinically with the following objectives: (1) can the process of clinical problem-solving be described using the proposed cognitive actions; (2) can a specific pattern in case-based problem-solving be extracted using the relation of the proposed cognitive actions to each other; (3) is this pattern correlated with the diagnostic accuracy?
Operationalization of the Research Questions
The stated research questions were investigated in a laboratory setting with a controlled set of clinical content. A think-aloud method was used to be able to identify patterns and certain subcomponents of thinking. Paper-based cases with basic patient information and further on test-results were given to the subjects.
Twenty-three medical students in their 4 th or 5 th year (female = 11) of two medical faculties volunteered (M = 23.9 years; range 20–34) to take part in the study. These years of the medical curriculum were chosen because the participants should have enough prior knowledge to solve clinical problems, but should not have experienced their final 6 th clinical year of full time electives to focus on the the problem-solving of the student. Furthermore these participants had finished their internal medicine curriculum. Written informed consent was obtained from all participants. This study was approved by the Ethical Committee of the Medical Faculty of LMU Munich. Participants received a small monetary compensation for their expenses.
Operationalization of the Model
It has been criticized that action theoretic models might be useful for instructional purposes, but are not suitable to describe the real-life problem-solving processes  . To conduct empirical research, an analysis model was needed to concretize the task, most likely applicable to medical students and detailed enough not to miss fundamental cognitive actions. After a thorough literature review and comprehensive expert discussions the empirically tested model from Schoenfeld  was chosen as a starting point as it represents the widely used action theoretic models, with the following cognitive actions: read , analyse , explore , plan , implement and verify . Schoenfeld’s model was especially formulated for simple problem-solving dealing with a single problem, but not for complex problems  . Problems can be considered as complex where diverse and volatile goals have to be considered  . Medical problem-solving is complex problem-solving  . Thus, more cognitive actions needed to be defined to gain a comprehensive view. Therefore, the original Schoenfeld model was modified in the following way. The doctor needs an inner representation to cope with the complexity of the problems, the development of which is another cognitive action within the analysis model. With this inner representation of the problems, the doctor evaluates the different actions taken and integrates the results to finally come to a solution. This decision for a working diagnosis or for the final solution is another cognitive action in the analysis model. The here presented “modified Schoenfeld model for complex problem-solving” (further referred to as “modified Schoenfeld model”) consists of eight selective cognitive actions, dealing with the problems given: Denomination , Analysis , Exploration , Plan , Implementation , Evaluation , Representation , Integration (see table 1 ). This “modified Schoenfeld model” was used for the case sessions of a pilot study. The detailed subactions and contents of each cognitive action were observed, summarized and defined using qualitative research methods (qualitative content analysis, inductive category development, open coding process  ). After several test codings, a fixed coding scheme was defined and applied to the whole sample of cases.
Course of the Study
Figure 1 shows that the study consisted of a controlled knowledge training, a subsequent knowledge test, and the paper-based clinical case-scenarios. Participants solved three cases in clinical nephrology with the think-aloud-method after three hours practising a standardized learning unit in the field of clinical nephrology. Recordings were transcribed and coded according to the “modified Schoenfeld model”. Codings were analysed for accuracy of the diagnosis. Learner characteristics were obtained by questionnaires.
All participants filled out a questionnaire containing items about their socio-demographic data, gender and age as possible confounders. The reliability of this multiple-choice exam is very high (Cronbachs α = .957)  . The performance of participants in this exam was used as an indicator for general prior knowledge in medicine. The results of the questionnaire and all other obtained data were anonymized.
Knowledge training and test
Although all participants were in the advanced part of medical school and had all passed the internal medicine curriculum a pre-learning phase was established. The pre-learning phase involved an extensive 3-hour computer-based tutorial on clinical nephrology to account for content specificity  . This was to help ensure that all students were able to show their problem-solving strategy and ability because they had the knowledge needed for application of strategies. Upon completion, the students' retention of content specific medical knowledge was tested  ,  ,  .
Clinical case scenarios
The three paper-based case scenarios with diagnoses within the field of clinical nephrology were real cases of the department of internal medicine adapted from experts with anonymized real supplemental material (i.e. lab values). After the transformation into paper-based scenarios, authenticity was additionally ensured through review by two content experts and one didactic expert. All cases were structured the same way, containing two or three pages describing the patient´s complaints and medical history. The results of the physical examination, blood tests, urine sample, ECG and ultrasound scan were each described on separate pages. The first case described a patient with hematuria due to glomerulonephritis. The second case concerned a patient with both the symptoms of acute renal failure as well as depression. The third case was on a patient with hypertensive crisis due to renal arterial stenosis. Students were not allowed to use secondary aids such as books or computers.
In a short practice exercise participants were instructed on the think-aloud method  . The students' task was to work on each case to show their problem-solving abilities with no other instructions being given than “please work on this case”. They were not explicitly asked to state a diagnosis. Only one single student and the test instructor were present in the room during the case elaboration. The test instructor sat behind the participant to avoid any diversion of thought  . The only interaction between the participant and instructor was when the instructor provided the next page of a case. Every case was interrupted after ten minutes, independent of whether the case was solved or not. While participants were working on the cases using the think-aloud method, they were audiorecorded.
All audio recordings (total time of 13∶05 hours) were transcribed and coded using the model described above. For technical reasons, three tapes were not completely evaluable and 66 of 69 cases were analyzed. The standard qualitative content analysis by Mayring  was used as method to assess, code and analyse the process of thought, as it also yields very detailed quantitative data in consecutive analysis. It uses models with several categories for the coding of a text. In this study, the cognitive actions were used as categories. A section of text matching a particular cognitive action was determined as an episode. One text section could be coded as more than one episode, when different cognitive actions took place at the same time. Subsequently, the codings were marked as time-sections in the transcription software “f4” (f4 2011, Dr. T. Dresing, http://www.audiotranskription.de ) and then exported to Microsoft Excel 2010 (Microsoft, 2010). For further analysis the statistical environment “R” was used ( http://www.r-project.org/ ). A predefined alpha level set at p<0.05 was used for all tests of significance. Graphical illustrations were processed as the percentage of time spent on one action relative to the overall time. Although the cognitive actions of the model were described qualitatively, this was the basis for a quantitative analysis and graphical illustration of the results.
As quantitative dependent variables the frequencies of cognitive actions were analysed, as well as the length of the episodes.
The accuracy of diagnosis was established in a binary form (correct or not correct) as a dependent variable. Chi-squared tests were used to verify the relationship of dependent variables to all dichotomous participant variables, while Pearson correlation was used for all continuous dependent variables to correlate them to previously obtained participant data. Chi-squared tests were processed in SPSS 20.0 with a predefined alpha level set at p<0.05.
One investigator (R. E.) coded all transcripts. A second rater coded more than 10% of the transcripts. Based on the coded time, the interrater coefficient analysed with Cohens kappa was κ = .935. Based on the coded text, the interrater coefficient was κ = .884.
The “modified Schoenfeld model for complex problem-solving” in medicine enables us to describe the cognitive actions of medical students. The times-on-task participants spent overall on each of the eight cognitive actions are shown in table 1 . Most time was spent on the cognitive actions Denomination and Analysis. The frequencies of the episodes overall showed a similar distribution with minor distinctions. Action Denomination and Analysis have mainly long episodes (M Denomination = 45sec ±1.74, M Analysis = 51sec ±2.34). Action Implementation often consists of short episodes (M Implementation = 19.10sec ±1.11), so the percentage in terms of frequencies is higher than the percentage in terms of session-time (as illustrated in table 2 ).
Figure 2 shows how the cognitive actions were distributed over time. All elaborations are presented separately for each of the three cases ( Fig. 2a–c ) and aggregated for all three cases ( Fig. 2d ). The case elaborations of all participants were mapped onto each other. As the figure shows, Denomination and Analysis were spread over the entire case elaboration, equally Plan and Implementation . The cognitive actions Evaluation , Representation and Integration were not present at the beginning and emerged during the case elaboration in this order. This pattern evolved for each of the three cases in a similar way (compare Fig. 2a–c ).
It shows the distribution of cognitive actions over time. The darker the blue is presented, the more case elaborations are containing this action at this part of the process.
Elucidation of a “ Complete Model Pattern”
In most individual case elaborations, two or three cognitive actions took place at the same time. Mostly this was Analysing or Evaluating while Denominating (44% of coded categories). To identify patterns in the case elaborations, the time-line graphs of the single cases were analysed. The analysis revealed a typical reproduced sequence how the participants traversed through the cognitive actions: they mostly started with Denomination , progressed through Analysis (or sometimes Exploration ) to Implementation (or more rarely Plan ). The obtained new information, due to the requests of the cognitive action Implementation, are then read and denominated , and another loop starts from the beginning of this sequence again. We keyed this sequence, which was found in every case elaboration, a “lower loop” (M loop = 3.18 loops/case ±1.46). The most widely used sequence of cognitive actions in the lower loops was Denomination , Analysis , Implementation , Denomination (116 of 210 loops; 55%). The actions Evaluation , Representation and Integration did also show a typical sequence in more than half of the case elaborations (37/66; 56%). This sequence was called “higher loop”. The sequence began with Evaluation and optionally Representation , followed or closed by Integration . As only explicitly stated representations were coded, Representation was considered to be optional. When the case elaboration included both, the lower loops as well as higher loops of the actions Evaluation, Representation and Integration these case elaborations were labelled a “ complete model ” (37/66; 56%). If the actions Evaluation , Representation and Integration were in another order or only single actions were coded, the case elaboration was labelled “incomplete” (29/66; 44%). The complete model was equally distributed over the three given paper-based cases, with a lower frequency in the third case (first case: 14/23; 61%, second case: 13/22; 59%, third case: 10/21; 48%). Figure 3 shows representative case examples each for a complete and an incomplete model .
When the case elaboration included also the higher loops of the actions Evaluation, Representation and Integration these case elaborations were labelled a “complete model”. If the actions Evaluation, Representation and Integration were in another order or only single actions were coded, the case elaboration was labelled “incomplete”.
The Complete Model Pattern is Significantly Correlated with the Correct Diagnostic Case Solution
Neither socio-demographic data of the participants (age, year of studies), nor prior knowledge (grades of PME as general prior knowledge, assessment of the learning phase in the field of clinical nephrology) were related to the completion of the model, analysed with Pearson correlation. As well, the dichotomous variables of sex and practical experience were not related to the completion of the model, analysed with Chi square test. Previous knowledge is not correlated with the complete model or for the correct solution in this setting with this level of knowledge in clinical nephrology.
The correct solution was obtained in 27 of all cases (27/66; 41%), the incorrect solution or no solution in the majority of the case elaborations (39/66; 59%), respectively. Out of the 37 cases with the complete model , the correct solution was reached in 24 cases (24/37; 64%). In contrast, out of the 29 cases with the incomplete model , the correct solution was reached in 3 cases only (3/29; 10%) (see table 3 ). The complete model was a strongly correlated with the correct solution. (Chi-squared test, p<.0001; phi coefficient [mean square contingency coefficient] φ = 0.55).
The aim of the study was to empirically verify the process of complex problem-solving among medical students. The first objective was to determine whether the process of problem-solving can be described using the cognitive actions in the proposed “modified Schoenfeld model”. The results indicate that it is possible to describe the process of problem-solving using this model. More specifically, it was found that all medical students used the following cognitive actions: Denomination , Analysis , and Implementation . When dealing with the cases, the medical student participants spent 73% of the session time with these relatively basic cognitive actions. Further, the results yield that the students spend less time on the actions Exploration and Plan . Furthermore, the cognitive actions of Evaluation , Representation and Integration were found only in a subset of the students. On average, students spent only 17% of the total session time on these higher cognitive actions.
The second objective of this study was to assess whether certain patterns can be extracted in the distribution of the actions over the duration of the case sessions. In our analysis, certain repeating patterns were found. Among all students the pattern of Denomination to Analysis and to Implementation could be found and was called a lower loop. This finding is consistent with the loops in the problem-solving process of medical doctors as described by Barrows and Tamblyn  . The higher cognitive actions (higher loops) could be coded in 56% of all cases. Solving a case with both, the lower loops and the higher loops was defined as the complete model pattern . The overall process of the case elaboration revealed a dynamic and complex sequence of actions with various lengths and often rapid switching between the different actions. The non-sequential workflow observed in the case elaboration in this study can be assumed to be necessary to cope with the complexity of the problems (as described in action theoretic approaches  ).
The third objective was to reveal whether the identified pattern is associated with the solution of the case. The complete model pattern was significantly correlated with a higher frequency of the correct solution (φ = 0.55). It appeared that the higher cognitive actions Evaluation , Representation and Integration were crucial for successful problem-solving. A reason for this finding might be that these cognitive actions exceed the other five cognitive actions with regard to their cognitive complexity needed to execute these actions as they require the ability for abstract thinking. For problem-solving of complex medical cases by medical students the quality of process was strongly associated with the quality of product in our study (cf. van Gog  ). Furthermore, this finding can be explained through the attributes of complex problem-solving  . Here, working on a case does not happen in a sequential order but rather in a dynamic and complex process where transitions from one action to another back and forth are necessary due to multiple problems and aims which change over time. Therefore, the ability to build an inner representation from the case information and its evaluation enabled the students to reach the correct solution. Surprisingly, the extent of general prior medical knowledge (PME) was neither related to the complete model pattern nor to the correct solution of the case. Therefore, this result suggests that the completion of the model is independent from the person. The question remains whether the higher cognitive actions are a predictor for diagnostic accuracy or rather a prerequisite. Furthermore, the fulfilment of the model could not simply be attributed to students with higher grades. According to content specificity, knowledge in a certain field is a prerequisite for the strategies applied. Although content specificity was controlled through the learning phase, the subjects did not consistently use or not use the complete model nor did the grades of the assessment after the learning phase relate to the use of the complete model. This result indicates that the cognitive actions described could be indeed fundamental abstractions, that they are not completely based on content specificity. Further research should clarify the counterintuitive finding regarding general prior knowledge (as tested with the PME). For example, the relation of knowledge types (factual knowledge, conceptual knowledge and procedural knowledge  ,  ) and meta cognitive knowledge and regulation  ,  to the cognitive actions, the completion of the model and the solution of the case should be investigated.
The implementation of the model into a cognitive architecture (i.e. ACT-R; adaptive control of thought–rational) would be interesting. Cognitive architectures have also been used to model the problem-solving processes of mathematicians and then implemented to foster the mathematical problem-solving of high-school students  . Although medical problem-solving is different from mathematical problem-solving a transfer of this application seems highly desirable. Additionally the model could be used as a tool for expertise research in medical problem solving and for research on specific biases of decision making of physicians  .
Potential Applications for Medical Education
There is an abundance of educational models using sequential steps  ,  . For clinical reasoning, the most common models are problem-based learning  ,  ,  or worked examples  ,  ,  . These models were designed for instructional purposes of core curriculum knowledge but have been criticized to be unsuitable for a description of realistic free individual medical problem-solving as happens in daily clinical work  . The findings in this study demonstrate that the proposed model is well-suited to describe realistic free individual medical problem-solving of medical students. The value of the model consists in its capacity to enable one to trace back the cognitive steps students take during the medical problem-solving process, independent of the correct solution. This is different from current educational strategies where the focus lies on the correct solution rather than the process towards the correct solution (cf. van Gog  ). One educational application which can be drawn from this study is the necessity to foster higher level reasoning (evaluation, representation and integration) during case elaboration. This could for example be applied by supporting students to express a verbal representation during their individual problem-solving process. Furthermore, training students to present their patients also may foster higher level thinking; research is needed to verify how this might work. This study showed that the majority of the students were already able to think on the higher-level. Therefore, instruction and encouragement alone could be a resource-oriented approach  . In case-based learning, worked examples could advance students’ learning to higher-level thinking as especially Integration could be fostered. With the model it is now possible to evaluate instructional strategies regarding their underlying cognitive actions. However, before the model should be used in this way it is important to understand why the students chose certain cognitive actions and did not choose others. Future studies on this subject could be stipulated by selection strategy research (i.e.  ).
Limitations of the Study
The qualitative design, the data preparation, as well as the analysis made it necessary to include a limited number of participants and a limited number of cases and domains per participant, respectively. On the other hand, qualitative research chooses to rather focus on carefully constructed valid measures (over thirteen hours of transcribed, coded and analysed material) than on less meaningful yet reliable measures, and for a qualitative study, the sample is relatively large. The composition of participants in the study was selected by stratification in groups regarding to their years of study, age and sex. However, the findings support that the completion of the model and solution of the cases were not linked to the participants at all. A natural limitation created by the think-aloud method is that only what is expressed verbally can be analysed, coded and interpreted. Furthermore, the model is rather complex and not easy to code. The eight cognitive actions were chosen in order not to miss a cognitive action. For further investigations, it could be useful to work with a simplified model by fusing both the cognitive action of Analysis and Exploration as well as Plan and Implementation .
Our model represents one way of approaching the cognitive processes behind clinical reasoning. Our model was drawn inductively from various models and pilot study data. Certainly other existing models have been proposed that could also fit. Recently elaborated and extensive modelling did find steps similar to our proposed model  . Nonetheless, to our knowledge our study represents the first empirical verification of a model to describe the process of individual medical problem-solving among medical students and it strongly suggests a link between higher cognitive actions and successful case solutions.
The model used in this study investigates the complex and dynamic nature of the medical problem-solving process. We have investigated and validated a first model to describe the cognitive actions during problem-solving of clinical medical students. This provides the platform for further research especially for the evaluation of novel instructional methods that intend to foster clinical reasoning.
Funds for this project were provided by Dr. med. Hildegard Hampp Trust administered by LMU Munich, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Eight-Step Problem Solving Process for Medical Practices
Whether you are hoping to solve a problem at your practice or simply trying to improve a process, the easy-to-follow OODA Loop method can help.
Practice managers know that there are four key objectives at the core of process improvement:
• To remove waste and inefficiencies • To increase productivity and asset availability • To improve response time and agility • To sustain safe and reliable operations
The question is, how do we do all this? I would suggest a proven technique known as the OODA Loop.
The OODA Loop consists of four overlapping and interacting processes. Managers must: O bserve the current situation and form theories, O rient the picture by setting improvement targets and determining root causes, D ecide by developing solutions, and A ct by means of implementing and evaluating. The OODA Loop can be subdivided further into an eight-step problem solving process.
Observe Step 1: Clarify the Problem This is a critical step. You need to recognize the correct problem and be sure it is completely understood by all. It helps to state the problem by developing a “problem statement” in terms of what, where, when, and the significance. You also need to “lay eyes” on the situation, ensuring you have first-hand observation. This will then help in drafting a flowchart that diagrams the steps of the process. Lastly, you need to conduct surveys and interviews, talking with the “customer” or end user who determines the value of the process under review. Step 2: Break Down the Problem and Identify Performance Gaps It is tempting to jump to action but you must refrain from doing so just yet. Gather and review the key data. Understand what data is necessary and what role it plays in problem solving. Are there gaps in your analysis? Are there bottlenecks in the process you are reviewing? Under this step, you must also look at waste in your practice as it relates to the problem. There are generally eight types of waste: defects, over production, waiting, over processing, transportation, intellect, motion, and excess inventory. You should always look for waste in your processes.
Orient Step 3: Set Improvement Targets Where do you want to be? Determine your desired outcome for the practice. Be sure to look at both strategic and tactical targets. Strategic targets are visions of what your practice strives to become. Tactical targets define the performance level necessary to make your strategic vision a reality. Remember to keep your tactical targets challenging but achievable. Step 4: Determine Root Causes This is the most vital step in the problem solving process. All too often practice managers find themselves addressing problems that have been “solved” many times before. This is usually due to directing problem solving efforts at the symptoms of a problem rather than at the root cause of the problem. It often helps to do much brainstorming and when you think you understand the cause of the problem, ask what caused the problem (continue to ask “why?”).
Decide Step 5: Select Solutions When selecting solutions, consider both quality and practicality. Be sure to also gain acceptance (or “buy in”) from those that must implement the solutions. Some key factors to consider when analyzing solutions include effectiveness, feasibility, and impact. When developing your action plan, be sure that you have created a clear and detailed plan that everyone can understand. Most importantly, build consensus with others by involving all of your team appropriately to cultivate a sense of ownership in the solution and in its success. Effective communications can be a deciding element as to whether the plan succeeds.
Act Step 6: See the Plan Through Collect data according to the action plan. Remember the old adage, “You can’t manage what you can’t measure.” You may need to implement a contingency plan as conditions change and you need to keep the project on focus. Continue to provide required training during this step as well. Step 7: Confirm Results and Process Ensure the plan is producing the intended results. Monitor the project for performance relative to: a) the baseline developed in steps 1 and 2; b) the improvement targets established in step 3; c) where you thought you would be at this stage; and d) meeting targets by the established deadline. You should return to any step as necessary. Step 8: Standardize Successful Processes This is the most commonly skipped and under completed step of the entire problem solving process. You can define this step by asking a series of questions : What is needed to standardize the improvements? Is the appropriate documentation in place? Were other opportunities or problems identified by the problem solving process?
If the answer to this last question is yes, begin the process over ... that is why it is referred to as the OODA Loop.
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