Today, Intellectual Capital plays a principal role in the delivery of corporate performance. This importance is reflected in the fact that companies, without the force of any regulations, start to produce intellectual capital statements to communicate their performance; accounting guidelines are being developed and standards are being questioned and reviewed; software companies such as SAP, Hyperion, Oracle, or Peoplesoft are developing applications to address this, and even governments are beginning to measure the intellectual capital of cities, regions, and countries. Accenture writes that today's economy depends on the ability of companies to create, capture, and leverage intellectual capital faster than the competition. Cap Gemini Ernst & Young believes that intangibles are the key drivers for competitive advantage. KPMG states that most general business risks derive from intangibles and organizations therefore need to manage their intangibles very carefully. PricewaterhouseCoopers writes that in a globalized world, the intellectual capital in any organization becomes essential and its correct distribution at all organizational levels requires the best strategy integrated solutions, processes and technology. Even though the leading management consulting firms recognize the importance of intellectual capital – they seem to suffer from the same predicament as the field as a whole. Intellectual capital is defined differently and the concept is often fuzzy. In this special issue of the leading journal in the field we would like to bring together the definitions, approaches, and tools offered by the leading management consulting firms. It will be a unique opportunity to disseminate your understanding of this critical area of management and allow you to illustrate your approaches and tools.
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Home care patients often use many medications and are prone to drug-related problems (DRPs). For the management of problems related to drug use, home care could add to the multidisciplinary expertise of general practitioners (GPs) and pharmacists. The home care observation of medication-related problems by home care employees (HOME)-instrument is paper-based and assists home care workers in reporting potential DRPs. To facilitate the multiprofessional consultation, a digital report of DRPs from the HOME-instrument and digital monitoring and consulting of DRPs between home care and general practices and pharmacies is desired. The objective of this study was to develop an electronic HOME system (eHOME), a mobile version of the HOME-instrument that includes a monitoring and a consulting system for primary care.
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Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (= one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
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