<p>Omsättningen av försäljning av varor på Internet har ökat kraftigt de senaste åren. I takt med att försäljningen har ökat så har det även uppstått fler leverantörer/aktörer. Eftersom utbudet av leverantörer ökar innebär det att företagen måste differentiera sig i allt större utsträckning. Uppsatsen beskri-ver ett antal företag som framgångsrikt har lyckats att diffe-rentiera sig och vilka faktorer som dom själva anser har varit betydande för framgången.</p> / <p>The turnover for merchandise marketed in the Internet has seen a great increase over the last years. I rate with the in-crease in sales more suppliers has entered the market. Since the range of products has been raised the company’s are forced to take a more differentiated approach. This paper de-scribes a number of company’s that successfully has been able to differentiate themselves and what factors that was significant for this success.</p>

<p>För att få fram rätt information krävs en metod som belyser patienters behov och på så sätt kan hjälpa sjukvården att på bättre sätt ta hjälp av patienters åsikt. Syftet med uppsatsen är därför att få fram hur patienter upplever att deras informationsbehov av hälsoinformation tillfredställs. Vi vill även ta reda på hur detta behov ser ut idag och på vilka sätt det kan tillfredställas bättre.</p><p>Vi vill även testa om fokusgrupper är lämplig som metod för att besvara vårt syfte.</p><p>För att uppfylla syftet har vi samlat in data genom två fokusgrupper. En grupp bestod av personer med mer kontakt med sjukvården medan den andra bestod av personer med mindre kontakt med sjukvården. Med hjälp av resultatet från fokusgrupperna genomfördes en analys där vi fann områden som var särskilt viktiga. Vi har även utvärderat fokusgrupper som metod genom att jämföra med tidigare forskning inom området. Utifrån det insamlade materialet har vi utarbetat en modell som visar hur patienters informationsbehov kan tillfredställas. Denna modell är indelad i fyra delar utifrån hur stort behov patienten har av egen samt allmän information.</p>

<p> </p><p>This thesis is based on a case-study of an IT-system implementation within Jönköping County Council. This new IT-system will cover, and support, all health-care related activi-ties within the County Council. The thesis is concerned with aspects that happened within the pre-implementation phase, and the thesis is furthermore only concerned with so-called „soft‟ aspects- meaning aspects that are not of a technical nature. Instead, attitudes relating to the use of the system and various qualities of the system have been measured; and sub-sequently fit into the categories of the extended Technology Acceptance Model, or TAM2, proposed by Venkatesh & Davis (2000). We have limited our research to only look at the attitudes of doctors. The doctors are also divided into two distinct user-groups: where one group has received more training than the other, and have been assigned as „super-users‟, meant to support the regular users in their use of the system, and also to help train these other, „regular-users‟. The attitudes of the various users of the system have been measured by using a qualitative method, and more specifically interviews based on the elements of the TAM2 model. The results of the interviews were analysed using a number of models and theories relevant to the subject matter. The analysis of the results, and the subsequent conclusions which are drawn as answers to the research questions will be of use in future IT-system implementations, especially within the coming rounds of implementing the spe-cific IT-system which has been studied, in the rest of the Jönköping County Council. The main conclusions which have been drawn can be summarised as the following:</p><p>The overall user-technology-acceptance of the doctors towards this system is neutral, but has a strong tendency towards a negative attitude. This conclusion is drawn through combining the perceived usefulness, and its sub-elements, with the perceived ease of use, of the doc-tors- as described in the analysis section. One of the key causes for this is the low subjec-tive norm (or outside opinion affecting attitudes) which is spread about the system. One of the key proposals to avoid these low levels of user-technology-acceptance is to meet the negative subjective norm attitudes being spread, with positive responses; and to optimise the training of all users- but to specifically improve the attitudes and knowledge of the su-per-users, which has been proven to be critical for a successful implementation. This thesis shows the importance of managing the subjective norms of the participants in the imple-mentation project, negative press in the media should in our opinion be met by positive in-formation about the system and the implementation. We have also found that should the training be performed in a satisfactory way the opinions and attitudes of the users will be positively affected. The training of the super-users has been found to be of special impor-tance as the attitudes of colleagues have been shown to be the most important for users, when building early opinions about a system.</p><p> </p>

Kling (1999) defined social informatics as "the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their interaction with institutional and cultural contexts" (Kling, 1999). This extended abstract proposes a definition of literature and art informatics (LAI): the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their role in the creative efforts of writers and artists. Expanding social informatics to include creative activities in literature and the arts will benefit the social informatics research community in several ways. First, it will address the paucity of empirical scholarship meant to examine the role of information technology in the creative efforts of writers and artists. Second, it will open another area of inquiry within social informatics which will provide additional opportunities for validating bodies of theory that can usefully inform our scholarship. This abstract will also discuss a current set of studies being conducted as examples of one possible path for LAI research.

Increasing health information technology (HIT) adoption has led to growth in research on its implementation and use, the majority of which has been conducted in primary care and medical specialty settings. This thesis comprises three research projects that expand the knowledge base about HIT in surgery. A systematic review summarized the evidence about the effects of major categories of HIT (e.g., electronic health records, computerized order entry) on surgical outcomes and demonstrated improvement in the quality of surgical documentation, increased adherence to guidelines for perioperative prophylactic medication administration, and improvements in patient care with provider alerts. The review identified gaps in the literature about consumer HIT use by surgical patients and providers. A second study demonstrated modest use of a patient portal by surgical patients during hospitalizations and found increased inpatient use for patients who were white, male, and had longer lengths of stay. This study showed that a patient portal designed for the outpatient setting could be employed by surgical patients during hospitalizations. A third study analyzed the nature of the communications in patient portal messages threads between surgeons and their patients. Two-thirds of message threads involved medical care with predominantly straightforward and low complexity decision-making. This study highlighted the need for expanded models for compensation of online care. This thesis provides insights into the use and effects of HIT in surgical practice. As HIT continues to evolve, the unique perspectives of surgical providers and patients should be represented in the design, implementation, evaluation, and regulation of its use.

Breast cancer patients suffer from the symptoms of their illness as well as from burden of treatment imposed by their care. Patients with high levels of burden tend to be less compliant with treatment plans resulting in worsening outcomes. To address the problem of overburden, some providers have proposed practicing minimally disruptive medicine, where treatment plans are tailored to the patientsâ capacity to handle the care. While some researchers have developed surveys that identify and quantify factors that contribute to treatment burden, no studies have used the electronic health record to assess patient burden. We developed measures derived from outpatient and inpatient encounter data that included time spent in appointments, waiting time, unique appointment days, and total inpatient length of stay. We used these measures to differentiate burden of treatment in early stage breast cancer patients in the first eighteen months after diagnosis. This method allowed us to identify outliers and to characterize the pattern of treatment over time. Our measures could also be used to evaluate new therapeutic and operational interventions for their effect on treatment burden. In patients receiving chemotherapy at Vanderbilt, a non-inferior change in protocol successfully reduced treatment burden while a therapeutically superior treatment may have imposed an increase of burden on patients. As the complexity of healthcare increases and patients take on more responsibility to manage their care, understanding treatment burden is critical to helping providers prescribe care right-sized for the patient to improve compliance and clinical outcomes.

The widely used American Society of Anesthesiologistâsâ (ASA) Physical Status classification is subjective and requires time-consuming clinician assessment. Machine learning can be used to develop a system that predicts the ASA score a patient should be given based on routinely available preoperative data. The problem of ASA prediction is reframed into a binary classification problem for predicting between ASA 1/2 versus ASA 3/4/5. Retrospective ASA scores from the Vanderbilt Perioperative Data Warehouse are used as labels, allowing the use of supervised machine learning techniques. Routinely available preoperative data is used to select features and train four different models: logistic regression, k-nearest neighbors, random forests, and neural networks. Of the selected features, ICD9 codes were tested by incorporating temporality and hierarchy. The area under the curve (AUC) of the receiver operating characteristic (ROC) of each model on a holdout set is compared. The Cohenâs Kappa is calculated for the model versus the raw data and the model versus our anesthesiologist. Results: The best performing model was the random forest, achieving an AUC of 0.884. This model results in a 0.63 Cohenâs Kappa versus the raw data, and a 0.54 Kappa against our anesthesiologist, which is comparable to unweighted Kappa values found in literature. The results suggest that a machine learning model can predict ASA score with high AUC, and achieve agreement similar to an anesthesiologist. This demonstrates the feasibility of using this model as a standardized ASA scorer.

Integrating personalized risk predictions into clinical decision support requires well-calibrated models, yet model accuracy deteriorates as patient populations shift. Understanding the influence of modeling methods on performance drift is essential for designing updating protocols. Using national cohorts of Department of Veterans Affairs hospital admissions, we compared the temporal performance of seven regression and machine learning models for hospital-acquired acute kidney injury and 30-day mortality after admission. All modeling methods were robust in terms of discrimination and experienced deteriorating calibration. Random forest and neural network models experienced lower levels of calibration drift than regressions. The L-2 penalized logistic regression for mortality demonstrated drift similar to the random forest. Increasing overprediction by all models correlated with declining event rates. Diverging patterns of calibration drift among acute kidney injury models coincided with predictor-outcome association changes. The mortality models revealed reduced susceptibility of random forest, neural network, and L-2 penalized logistic regression models to case mix-driven calibration drift. These findings support the advancement of clinical predictive analytics and lay a foundation for systems to maintain model accuracy. As calibration drift impacted each method, all clinical prediction models should be routinely reassessed and updated as needed. Regression models have a greater need for frequent evaluation and updating than machine learning models, highlighting the importance of tailoring updating protocols to variations in the susceptibility of models to patient population shifts. While the suite of best practices remains to be developed, modeling methods will be an essential component in determining when and how models are updated.

The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and thus can provide insights into the functional importance of protein regions and the potential pathogenicity of protein alterations. To facilitate the spatial analysis of coding variation in protein structure, we develop PDBMap, an automated pipeline for mapping genetic variants into all solved and predicted protein structures. We then comprehensively evaluate the 3D spatial patterns of constraint on human germline and somatic variation in 4,568 solved protein structures. Different classes of coding variants have significantly different spatial distributions. Neutral missense variants exhibit a range of 3D constraint patterns, with a general trend of spatial dispersion driven by constraint on core residues. In contrast, germline and variants are significantly more likely to be clustered in protein structure space. Finally, we demonstrate that this difference in the spatial distributions of disease-associated and benign germline variants provides a signature for accurately classifying variants of unknown significance (VUS) that is complementary to current approaches for VUS classification.

Patient care is complex and imperfect. Understanding and improving patient care requires clinical datasets and scientific methodology. We designed a set of methods to characterize the state of patient care and identify opportunities for improvement from electronic health record (EHR) data. The state of patient care is the distribution of patients throughout a clinical workflow. An opportunity for improvement is a means to shift patient distribution away from suboptimal states. We tested our methods within Vanderbilt University Medical Centerâs (VUMC) EHR system and the adjuvant endocrine therapy domain. Our methods divide into three aims: 1) Determine sufficiency of the data, 2) Characterize the state of care, and 3) Identify opportunities for improvement. Data sufficiency is the rise and persistence of data in an EHR system. We built metrics for data sufficiency that can be used in cohort and data selection. We find that despite inconsistent and missing data, we can leverage EHR data for studies on patient care. To characterize the state of patient care, we built a state diagram for adjuvant endocrine therapy at VUMC, and used EHR data to determine the distribution of patient across states. We measured drug choice frequencies, rates of adverse events, and recurrence rates. We also determined the extent to which EHR data can characterize complete patient care. To identify an opportunity for patient care improvement, we identified a suboptimal state (failure to follow-up) among VUMC adjuvant endocrine therapy patients and framed a classification problem using EHR data. We used supervised machine learning to predict follow-up and identify significant predictors that may inform on improvement. Patients that fail to follow-up may receive the majority of their care outside of VUMC. Follow-up could be improved by 1) referral to VUMC primary care provider or 2) documenting where patients follow-up to reduce ambiguity of care. These methods characterized the state of patient care and opportunities for improvement among an adjuvant endocrine therapy patient population using VUMCâs EHR data. We believe these methods are extensible to other EHR systems and other healthcare domains. These methods are valuable for drawing new clinical knowledge from clinical datasets.