# A Conceptual Blueprint for Making Neuromsculoskeletal Melically Useful
## 4 Description of Needs
### Clinical Needs  
Only certain clinical problems involving impaired movement are likely to benefit from a treatment design approach based on computational modeling. For example, there would be little benefit to performing computational modeling for clinical problems where \"cookie-cutter\" treatment plans already work well. At the other extreme, there would also be little benefit for clinical problems where genetic and molecular factors play a predominant role. Clinical problems involving orthopedic surgery, physical rehabilitation, or neurorehabilitation are likely to benefit the most from a computational approach to treatment design. Clinical situations where a patient could benefit from computational treatment design can be identified by the presence of three elements. The first element is a \"clinical measure\" [82]. This element is logical since computational models produce quantitative predictions. As stated by [PERSON], \"The 'acid tests' of clinical usefulness of any measure... are whether that measure predicts a different outcome than would be predicted without the measure, or whether the measure suggests a different treatment (or a different implementation of the same treatment) than would be recommended without the measure\" [82]. In essence, an appropriate clinical measure will define a quantifiable \"target\" that the clinician wants to \"hit\" through treatment. Clinical measures that meet these criteria will be highly correlated with some direct measure of clinical outcome, such as slowing disease progression, decreasing pain, or increasing function. Examples of clinical measures include medial knee contact force or the peak knee adduction moment for individuals with medial knee osteoarthritis or walking speed and bilateral symmetry for individuals with stroke or Parkinson's disease. Identification of the best clinical measure for any particular patient and movement impairment will require the combined input of clinicians, the patient, and researchers. The second element is \"clinical requirements.\" For computational models to be used for orthopedic surgery, physical rehabilitation, or neurorehabilitation treatment design, five clinical requirements should be met. First, the movement impairment should be influenced heavily by patient-specific factors. Such factors include unique neurological, physiological, or anatomical characteristics, as noted above. Second, standard treatment approaches should be either ineffective or inconsistent at restoring movement function. If standard treatment approaches already work well, then a computational approach to treatment design is unnecessary. Third, a clinical measure can be identified that can quantify initial clinical state and final treatment outcome. Without such a measure, the computational model would have no quantitative \"target\" to be \"hit\" through treatment. Fourth, clinical treatment parameters can be identified that can be changed by the clinician, can be modified in the model, and can affect the clinical measure. Identification of appropriate clinical treatment parameters requires close collaboration between clinician and researcher. Fifth, the clinical measure can be measured in the lab prior to treatment, predicted with a computational model to design the treatment, and measured again in the lab following the treatment. This process of \"closing the loop\" is essential for evaluating whether a computational model can predict functional outcome initially for treatments not designed with the model and subsequently for treatments designed using the model. The third element is a \"clinical model.\" Three model requirements should be met for a computational model to be used for clinical treatment design. First, a clinical model should be predictive, capable of predicting how the clinical measure will change in response to a proposed treatment implementation. Second, a clinical model should be minimally complex, containing only those model elements necessary to predict the clinical measure reliably for an individual patient.