dfgcompare: a library to support process variant analysis through Markov models.

BACKGROUND: Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare. RESULTS: This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers' behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice. CONCLUSIONS: We conclude that dfgcompare supports identifying differences among process cohorts.

Development and pilot testing of PHARAO-a decision support system for pharmacological risk assessment in the elderly.

PURPOSE: The aims of this study are to describe the development of PHARAO (Pharmacological Risk Assessment Online), a decision support system providing a risk profile for adverse events, associated with combined effects of multiple medicines, and to present data from a pilot study, testing the use, functionality, and acceptance of the PHARAO system in a clinical setting. METHODS: About 1400 substances were scored in relation to their risk to cause any of nine common and/or serious adverse effects. Algorithms for each adverse effect score were developed to create individual risk profiles from the patient's list of medication. The system was tested and integrated to the electronic medical record, during a 4-month period in two geriatric wards and three primary healthcare centers, and a questionnaire was answered by the users before and after the test period. RESULTS: A total of 732 substances were tagged with one or more of the nine risks, most commonly with the risk of sedation or seizures. During the pilot, the system was used 933 times in 871 patients. The most common signals generated by PHARAO in these patients were related to the risks of constipation, sedation, and bleeding. A majority of responders considered PHARAO easy to use and that it gives useful support in performing medication reviews. CONCLUSIONS: The PHARAO decision support system, designed as a complement to a database on drug-drug interactions used nationally, worked as intended and was appreciated by the users during a 4-month test period. Integration aspects need to be improved to minimize unnecessary signaling.

Pregnant women's view on the Swedish internet resource Drugs and Birth Defects intended for health care professionals.

INTRODUCTION: Pregnant women often have questions concerning fetal effects of drugs but there is limited reliable information specifically intended for them. This study investigated how pregnant women perceive and value the scientific resource Drugs and Birth Defects (www.janusinfo.se/fosterpaverkan) and compared their opinions with those of health care professionals. MATERIAL AND METHODS: Electronic questionnaire study. Pregnant women were recruited at their regular visits, and health care professionals via e-mail, at 10 antenatal clinics. Altogether, 275 pregnant women, 38 midwives and 30 physicians participated. RESULTS: Among the pregnant women, 81% found the information valuable, 70% that it was easy to understand, and 92% that it strengthened information from the staff. Concerning anxiety for negative fetal effects, 68% of the women answered that the anxiety decreased or was not influenced by the texts and 22% that it increased. Among physicians and midwives, 44% saw risks associated with pregnant women reading the texts and 24% answered that they would fully recommend them to use the database. The corresponding figures among pregnant women were 17 and 65%, respectively (P < 0.001). The professionals preferred, to a greater extent than pregnant women did, lay people to use a special edition. CONCLUSIONS: The majority of pregnant women seem to benefit from using a scientific resource on fetal impact of drugs intended for health care professionals. Some women are more worried after having read the information, but most of them still find it valuable. It is important that pregnant women who use the database can reach a medical professional to discuss the contents.

[Janusmed Renal Function, an appreciated CDSS to support prescription of drugs and appropriate dosing in patients with renal impairment]. / ¼Janusmed njurfunktion« ­ stöd för anpassning av läkemedelsdos.

Janusmed Renal Function is a clinical decision support system (CDSS) that provides evidence-based dosage recommendations for adult patients with renal impairment. Dosage recommendations are presented for each drug/active substance in relation to four stages of chronic kidney disease (CKD). In addition, substances that are nephrotoxic are labelled with a warning. The web version is available with free access for all caregivers in Sweden, and there is also a version available for integration in electronic health record (EHR) systems. A questionnaire distributed among users who already use a health record integrated version of the CDSS showed that physicians have good support from the system and perceive it as useful.

Patients' Use and Perceptions of a Drug-Drug Interaction Database: A Survey of Janusmed Interactions.

Janusmed interactions is a drug-drug interactions (DDI) database available online for healthcare professionals (HCP) at all levels of the healthcare system including pharmacies. The database is aimed at HCP but is also open to the public for free, for those individuals who register for a personal account. The aim of this study was to investigate why and how patients use the database Janusmed interactions, how they perceive content and usability, and how they would react if they found an interaction. A web-based questionnaire was sent by email to all users who had registered for Janusmed interactions as a "patient" (n = 3219). A total of 406 patients completed the survey (response rate 12.6%). The study shows that there is an interest among patients to use a DDI database to check their own or a relative's medication. The respondents found the database easy to use and perceive they understand the information aimed at HCP. Most patients stated they would talk to their HCP if they found an interaction and not adjust their treatment by themselves. However, the respondents in this study are actively searching for information and seem to have high health literacy. Thus, the findings are not generalizable for the general population.

Midwives' Perception of Using a Knowledge Base on Fetal Impact of Drugs.

A non-commercial knowledge base providing assessments of fetal risks of medicinal drugs is a useful tool in the everyday work of midwives. The information is freely available on the internet, and according to a questionnaire study, nearly 95% of the midwives are familiar with the database, 30% use the information weekly, and 80% express that it affects their medical decisions. A vast majority of the midwives also state that it is time-saving.

Prevalence of renally inappropriate medicines in older people with renal impairment - A cross-sectional register-based study in a large primary care population.

The aim of this population-based, cross-sectional study was to analyse the prevalence of renally inappropriate medicines (RIMs) in older people with renal impairment. We included 30 372 people aged ≥65 years with chronic kidney disease (CKD) stage 3, and 2161 with stage 4 attending primary care in Stockholm, Sweden. We used data derived from SCREAM, a database linking patient-specific data on demography, morbidity, healthcare consumption and dispensed drugs to creatinine measurements. Estimated glomerular filtration rate was calculated with CKD-EPI based on the first non-hospital serum creatinine in 2010. RIMs were defined as medicines needing dose adjustment or being contra-indicated in CKD stage 3 or 4 with reference to Swedish "Janusmed Drugs and Renal function." Dispensed prescription drugs were analysed during 1 year after creatinine assessment. Drug doses were considered as excessive if their mean volume dispensed per day exceeded the recommended dose. Contra-indicated medicines were regarded as inappropriate if dispensed at least once during 1 year. Excessive dosing was present in 42.5% of older people with CKD stage 3 and in 58.1% with stage 4. The prevalence of contra-indicated medicines was 9.4% and 38.0%, respectively. A limited number of RIMs accounted for the majority of excessive dosing, such as drugs acting on the renin-angiotensin-aldosterone system, betablockers and opioids. Commonly dispensed contra-indicated substances were NSAIDs and antidiabetics. We conclude that the prevalence of RIMs in older people with renal impairment is considerably high. Still, RIM use may be manageable as only a limited number of RIMs are frequently used.

Evaluating a Clinical Decision Support System for Drug-Drug Interactions.

Janusmed is a clinical decision support system, developed by the Stockholm County Council that supports physicians in identifying drug-drug interactions. To determine how Janusmed is used in and affects the clinical practice, an evaluation study is currently being carried out that analyzes multiple data sources through descriptive statistics. The study focuses on how Janusmed affects the behavior of the physicians, in particular, to what extent physicians reconsider their prescription decisions based on warnings from Janusmed.

A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: design, development and proof of concept.

OBJECTIVES: To develop and verify proof of concept for a clinical decision support system (CDSS) to support prescriptions of pharmaceutical drugs in patients with reduced renal function, integrated in an electronic health record system (EHR) used in both hospitals and primary care. METHODS: A pilot study in one geriatric clinic, one internal medicine admission ward and two outpatient healthcare centers was evaluated with a questionnaire focusing on the usefulness of the CDSS. The usage of the system was followed in a log. RESULTS: The CDSS is considered to increase the attention on patients with impaired renal function, provides a better understanding of dosing and is time saving. The calculated glomerular filtration rate (eGFR) and the dosing recommendation classification were perceived useful while the recommendation texts and background had been used to a lesser extent. DISCUSSION: Few previous systems are used in primary care and cover this number of drugs. The global assessment of the CDSS scored high but some elements were used to a limited extent possibly due to accessibility or that texts were considered difficult to absorb. Choosing a formula for the calculation of eGFR in a CDSS may be problematic. CONCLUSIONS: A real-time CDSS to support kidney-related drug prescribing in both hospital and outpatient settings is valuable to the physicians. It has the potential to improve quality of drug prescribing by increasing the attention on patients with renal insufficiency and the knowledge of their drug dosing.

Towards capturing innovation effects of a CDSS (NjuRen).

The e-service NjuRen is a clinical decision support system used by physicians to calculate patients' renal function and provide support for selection of appropriate drug and dosage for patients with renal failure. Project NjuRen is a collaboration between Stockholm County Council and Jönköping International Business School and aims at evaluating the socio-economic impact of implementing IT-systems in healthcare. The project consist of several steps, first the development and adaptation of a model to measure innovation effects. In the second step the development of a survey to capture factual impacts and effects. Finally, in the third step to translate the effects into socio-economic terms. The result will help decision makers to identify the achieved benefits and outcomes that the implementation of the system has brought with it.