The aim of this thesis is to develop a method to predict consultation durations in a digital primary care setting and thereby create a tool for designing a more efficient scheduling system in primary care. The ultimate purpose of the work is to contribute to a reduction in waiting times in primary care. Even though no actual scheduling system was implemented, four machine learning models were implemented and compared to see if any of them had better performance. The input data used in this study was a combination of patient and doctor features. The patient features consisted of information extracted from digital symptom forms filled out by a patient before a video consultation with a doctor. These features were combined with doctor's speed, defined as the doctor's average consultation duration for his/her previous meetings. The output was defined as the length of the video consultation including administrative work made by the doctor before and after the meeting. One of the objectives of this thesis was to investigate whether the relationship between input and output was linear or non-linear. Also the problem was formulated both as a regression and a classification problem. The two problem formulations were compared in terms of achieved accuracy. The models chosen for this study was linear regression, linear discriminant analysis and the multi-layer perceptron implemented for both regression and classification. After performing a statistical t-test and a two-way ANOVA test it was concluded that no significant difference could be detected when comparing the models' performances. However, since linear regression is the least computationally heavy it was suggested for future usage until it is proved that any other model achieves better performance. Limitations such as too few models being tested and flaws in the data set were identified and further research is encouraged. Studies implementing an actual scheduling system using the methodology presented in the thesis is recommended as a topic for future research. / Syftet med denna uppsats är att utvärdera olika verktyg för att prediktera längden på ett läkarbesök och därmed göra det möjligt att skapa en mer effektiv schemaläggning i primärvården och på så sätt minska väntetiden för patienterna. Även om inget faktiskt schemaläggningssystem har föreslagits i denna uppsats så har fyra maskininlärningsmodeller implementerats och jämförts. Syftet med detta var bland annat att se om det var möjligt att dra slutsatsen att någon av modellerna gav bättre resultat än de andra. Den indata som använts i denna studie har bestått dels av symptomdata insamlad från symptomformulär ifylld av patienten före ett videomöte med en digital vårdgivare. Denna data har kombinerats med läkarens genomsnittliga mötestid i hens tidigare genomförda möten. Utdatan har definierats som längden av ett videomöte samt den tid som läkaren har behövt för administrativt arbete före och efter själva mötet. Ett av målen med denna studie var att undersöka som sambandet mellan indata och utdata är linjärt eller icke-linjärt. Ett annat mål var att formulera problemet både som ett regressionsproblem och som ett klassifikationsproblem. Syftet med detta var att kunna jämföra och se vilken av problemformuleringarna som gav bäst resultat. De modeller som har implementerats i denna studie är linjär regression, linjär diskriminationsanalys (linear discriminant analysis) och neurala nätverk implementerade för både regression och klassifikation. Efter att ha genomfört ett statistiskt t-test och en två-vägs ANOVA-analys kunde slutsatsen dras att ingen av de fyra studerade modellerna presterade signifikant bättre än någon av de andra. Eftersom linjär regression är enklare och kräver mindre datorkapacitet än de andra modellerna så dras slutsatsen att linjär regression kan rekommenderas för framtida användning tills det har bevisats att någon annan modell ger bättre resultat. De begränsningar som har identifierats hos studien är bland annat att det bara var fyra modeller som implementerats samt att datan som använts har vissa brister. Framtida studier som inkluderar fler modeller och bättre data har därför föreslagits. Dessutom uppmuntras framtida studier där ett faktiskt schemaläggningssystem implementeras som använder den metodik som föreslås i denna studie.

Background: Increased pressure, rising demand and cost constraints have driven a need for radical service re-design in the NHS. To deliver re-design objectives it is necessary to understand how they are perceived by service users and providers. Aim: To investigate the views of patients and health care professionals (HCP) on aspects of health policy and service re-design affecting primary care. Setting: Patients and HCPs from one geographical area in England. Method: Themes from phase one qualitative interviews were explored quantitatively using a questionnaire in phase two and a discrete choice experiment (DCE) in phase three. Factor analysis was used to explore HCP responses in phase two. In phase three the DCE was administered to explore patients‟ relative priorities of a range of attributes. Results: HCPs had concerns that the Quality Outcomes Framework (QOF) detracted from the patient‟s agenda and did not improve health outcomes. GPs felt continuity of care was important Monday through Friday but were not keen on its provision out of hours. Neither did they feel nurses could run chronic disease management clinics without a GP present. Patients felt continuity could be provided by different HCPs for different conditionsPatients stated continuity of care and consultation duration were the most important attributes in a primary care service. However, in the DCE they prioritised both being seen on the day and by a GP over longer appointments. Patient preference to be seen by a GP may reflect the low uptake of non-medical prescribing in the area. Conclusion: Continuity of care, while remarked as being important to both HCPs and patients, appears less important when weighted against other primary care service attributes. HCPs appear to want longer consultations whereas patients saw time as encompassing both the time to wait for an appointment and consultation length. For patients, the quality of the time with a HCP may be more important than its duration, additionally patients appear to want choice but not necessarily to choose.