The prevalence of Heart failure above 70 years of age is 10% and 5 year mortality rate above 60%, higher than for cancer. The readmission rate first after hospitalisation is 44% despite the availability of life prolonging and life quality enhancing treatment. There is a lack of resources for adequate diagnostic workup necessary for implementing evidence-based treatment. This projects aims at assessing the impact of guidelines based diagnostic workup and guidelines based treatment of heart failure on mortality and readmission rates. As the symptoms defining the degree of heart failure and the discharge medication only is available in the electronic patient files, artificial intelligence is used to retrieve this information to assess if treatment is according to guidelines.

The project is using first a rule based text processing approach using IBM Watson, then advancing to a machine learning approach using readmission and mortality as endpoints.

The project has access to digitally stored echocardiographic measurements as well as digital ECG's and lab data on 15 000 patients admitted with a diagnosis of Heart failure. If the retrieval of symptoms and function by artificial intelligence is successful, the next step is to assess if those benefitting the most from echocardiography can be identified using information from the ECG's, lab data or symptoms and functional capacity as described in the Electronic Health Records.

Inclusion Criteria: Diagnosis with ICD10 codes as follows; I50.*, I42.3, I11.* and COPD J44.* from 2007 through 2019.

In addition all subjects with NT-proBNP above age specific normal range in same period.

Exclusion Criteria:

none

All patients admitted to a University Hospital serving a population of approx. 600 000 inhabitants with heart failure diagnosis in the period 2007 to 2019. In total 14998 patients. Mortality according to The Norwegian Death Registry for all up until 31st of December 2019. In addition all patients with a diagnosis of COPD in the same period as well as all subjects with NT-proBNP above age specific normal range