Attitudes of physicians, nurses and pharmacists concerning the development of clinical pharmacy activities in a university hospital

It is essential to identify the expectations of physicians and nurses regarding clinical pharmacy (CP) services before its introduction in a hospital, because it is known thattheir expectations can substantially differ from the pharmacists’ point of view. Agreement of leading physicians, nurses and clinical pharmacists about the importance of CP activities in the hospital was evaluated using five point Likert scale questionnaire. Two groups of CP activities were set; the activities related to the hospital system (first group) and the activities connected with an individual patient (second group). Total mean score of agreement of physicians with the first and second group of CP activities is 4.28 and 3.73, respectively, while these scores are lower for nurses (3.87 and 3.38 for the first and second group, respectively). Pharmacists’ total mean scores are highest, 4.57 and 4.23 for the first and second group, respectively

Planning of clinical pharmacy services in the hospital using artificial intelligence methods

Za uvajanje kliničnega farmacevta v zdravstveni tim je nujno potrebna podpora najvišjega vodstva bolnišnice, ki je zadolženo za izvajanje varnostne politike pri zdravljenju in za finančno poslovanje bolnišnice. Sprejetost kliničnega farmacevta s strani drugih zdravstvenih delavcev je tem boljša, čim bolj njegove aktivnosti ustrezajo pričakovanjem in potrebam uporabnikom njegovih storitev, to so predvsem zdravniki in medicinske sestre. Ugotavljanje potreb in pričakovanj uporabnikov je zato prva in ključna aktivnost pri načrtovanju katerekoli nove storitve, kar klinična farmacija v slovenskem prostoru vsekakor je. V Univerzitetnem kliničnem centru Ljubljana (UKCL) se dejavnost klinične farmacije do leta 2010 ni izvajala. Čeprav je vodstvo ustanove uvedbo te dejavnosti podpiralo, pa so neformalno izražena mnenja posameznih predstojnikov kazala na to, da je mnenje glede uvedbe klinične farmacije do določene mere deljeno tudi med predstojniki kliničnih oddelkov. Pred začetkom uvajanja klinične farmacije je bilo zato ugotavljanje potreb in pričakovanj bodočih uporabnikov, predvsem predstojnikov kliničnih oddelkov ter njihovih glavnih medicinskih sester, ključnega pomena, saj je za kakršnokoli dejavnost na posameznem oddelku dovoljenje oz. podpora predstojnika nujni pogoj. Sodobna družba, ki jo lahko označimo tudi kot »podatkovno« družbo, se srečuje z izzivom obvladovanja podatkov in predvsem z iskanjem metod za njihovo analizo, saj podatki sami po sebi še ne prinašajo nobene dodane vrednosti. Potreba po razumevanju velikega števila podatkov je narekovala razvoj številnih metod za analizo podatkov, ki izhajajo iz umetne inteligence in statistike. Odkrivanje znanja iz podatkov je definirano kot kompleksno pridobivanje implicitnih, neznanih in potencialno uporabnih informacij iz podatkov. Rudarjenje podatkov (data mining) je postopek, s katerim iz velikih podatkovnih zbirk poiščemo pomembne skrite napovedne informacije. Osnova za podatkovno rudarjenje so zbirke podatkov, statistične metode in algoritmi strojnega učenja ter umetne inteligence. Podatkovno rudarjenje omogoča avtomatizirano napovedovanje bodočih trendov in obnašanja ter avtomatizirano odkrivanje prej neznanih vzorcev. V množici metod podatkovnega rudarjenja so bile razvite tudi številne metode, namenjene analizi majhnih vzorcev. Tudi medicina in farmacija sodita med znanstvena področja, kjer v določenih primerih ni mogoče zagotoviti velikega vzorca, na primer pri raziskovanju redkih bolezni. Podobno je področje tržnih raziskav in raziskav zadovoljstva uporabnikov (marketing research, customer satisfaction). V ta namen je bilo treba razviti prilagojene metode, ki so primerne za analizo majhnih vzorcev, ki pa obenem zagotavljajo ustrezno napovedno moč raziskave. Za analizo majhnih vzorcev z velikim številom spremenljivk se običajno uporabljajo tehnike izbora pomembnih spremenljivk za dani klasifikacijski problem. Vsako od spremenljivk opredelimo glede na njen pomen za določen razred. Na ta način lahko v praksi spremljamo le tiste spremenljivke, ki imajo največji pomen za dani problem, saj vsebujejo vso potrebno informacijo za opredelitev razreda. Navedene zakonitosti in znanja s področja umetne inteligence smo uporabili za analizo podatkov anketnega vprašalnika o vlogi kliničnega farmacevta v UKCL. Na osnovi obsežnega pregleda literature smo izdelali nabor aktivnosti kliničnih farmacevtov, ki je služil za oblikovanje vprašalnika. Izhodiščne potrebe in pričakovanja uporabnikov storitev kliničnega farmacevta v UKCL smo preverili z vprašalnikom za ocenjevanje pomembnosti posamezne aktivnosti po Likertovi ocenjevalni lestvici. Validacijo vprašalnika smo izvedli s pomočjo ekspertnega pregleda treh ekspertov, in sicer enega od predstojnikov, sodelujočih v raziskavi, farmacevta, zaposlenega izven bolnišnične lekarniške dejavnosti, in neodvisnega strokovnjaka s področja upravljanja s kadri. Vprašanja v vprašalniku so razdeljena na tri vsebinske dele: vloga kliničnega farmacevta v sistemu bolnišnice (17 vprašanj), vloga kliničnega farmacevta ob pacientu (19 vprašanj) in pomen kompetenc za uspešno delo kliničnega farmacevta (16 vprašanj). V prvih dveh vsebinskih delih je anketiranec izrazil svoje strinjanje ali nestrinjanje s trditvijo z ocenami od 1 (sploh se ne strinjam) do 5 (zelo se strinjam), pri tretji pa je z ocenami od 1 (ni pomembna) do 5 (zelo je pomembna) opredelil, koliko je pomembna posamezna splošna oziroma specifična kompetenca za uspešno delo kliničnega farmacevta. Vprašalnik smo poslali v ocenjevanje vsem predstojnikom, glavnim medicinskim sestram in farmacevtom v bolnišnici. Prejete odgovore smo zbrali v podatkovni tabeli, ki je služila kot osnova za vse nadaljnje analize. V prvem poglavju prikažemo rezultate analize z neparametričnimi ANOVA testi. Ugotovili smo, da se stališča posamezne poklicne skupine glede dejavnosti klinične farmacije razlikujejo tako pri posameznih aktivnostih kot tudi pri sklopih aktivnosti. Največje razlike opazimo med farmacevti in medicinskimi sestrami, pomembne razlike pa so tudi v stališčih zdravnikov in farmacevtov. Manj se razlikujejo stališča zdravnikov in medicinskih sester. Srednja vrednost strinjanja zdravnikov z aktivnostmi prvega in drugega sklopa je bila 4.28 oz. 3.73, medtem ko so bile te vrednosti pri medicinskih sestrah ovrednotene nižje, in sicer 3.87 oz. 3.38. Srednje vrednosti ocen farmacevtov za zadevna dva sklopa so bile bistveno višje, in sicer 4.57 in 4.23. Navedena vsebina je bila objavljena v reviji Acta Pharmaceutica 64 (2014) 447-461. V nadaljevanju smo v UKCL vzpostavili sodelovanje kliničnih farmacevtov na izbranih oddelkih v bolnišnici. Rezultate anket smo dopolnili z ekspertno oceno vodje lekarne glede uspešnosti sodelovanja. Tako dopolnjeno tabelo smo uporabili za analizo s pomočjo algoritmov strojnega učenja. V drugem poglavju so predstavljeni rezultati analize podatkov s pomočjo algoritma OrdEval (programsko okolje R, paket CORElearn), ki nam omogoča tudi ugotavljanje vrste vpliva posamezne aktivnosti na celotno zadovoljstvo. Rezultate smo uporabili za kategorizacijo aktivnosti kliničnih farmacevtov v skladu s Kanovim modelom. Šest aktivnosti smo razvrstili v skupino pričakovanih elementov (performance), deset v skupino privlačnih (excitement) in eno v skupino potrebnih (basic). Za ocenjevanje pomembnosti spremenljivk lahko uporabimo tudi algoritma ReliefF in MDL (programsko okolje R, paket CORElearn). Z njuno uporabo smo ocenili, katere dejavnosti klinične farmacije (atributi) so za zdravnike in medicinske sestre najbolj pomembne in katere najmanj, ter v kolikšni meri se pomembnost posamezne dejavnosti ujema z ekspertno oceno vodje lekarne in z rezultati predhodno uporabljenega algoritma. Rezultati so prikazani v tretjem poglavju in kažejo, da je uporaba različnih algoritmov smiselna, saj je v primeru ujemanja rezultatov zanesljivost ugotovitev večja. Tako smo ugotovili dobro ujemanje obeh algoritmov z oceno vodje lekarne pri osmih aktivnostih kliničnih farmacevtov. Za ocenjevanje zadovoljstva s klinično farmacijo smo nato izvedli še pol-strukturirane intervjuje s predstojniki petih oddelkov, na katerih se izvaja klinična farmacija, in s pomočjo odgovorov določili uspešnost kliničnih farmacevtov. Odgovore smo analizirali s pomočjo orodja za analizo besedila QDA Miner text management and qualitative coding software (QDA Miner 4, Provalis research). Ugotovili smo, da sta med dejavnostmi klinične farmacije dve, ki ju lahko uvrstimo v skupino obratnih (reversal) elementov, česar z uporabo algoritmov strojnega učenja nismo zaznali.For a successful introduction of clinical pharmacy services on the wards of a hospital, the support of hospital management is crucial. The acceptance of a clinical pharmacists by other members of the health care team depends mainly on their individual attitude, perception, and personal experience. It is necessary to investigate the needs and expectations of the users before the introduction of any new service, including clinical pharmacy. Clinical pharmacy (CP) service was not used in University medical Centre Ljubljana before 2010. Although the management strongly supported the introduction of clinical pharmacists into health care teams, some of the head physicians doubted it. Before the introduction of clinical pharmacy service in the hospital it was necessary to assess the attitudes of head physicians and head nurses about it, as well as the attitudes of pharmacists as potential providers of the new service. Contemporary society is characterized by plethora of hardly manageable data. The need to store and analyse a huge amount of data and discover useful information hidden in it lead to the development of new methods based on artificial intelligence approaches, statistical methods and machine learning algorithms. Data mining is a process to explore big data warehouses and data bases to discover hidden information and knowledge. Data mining techniques allow automatic prediction of future trends and behaviours, and automatic discovery of hidden patterns in data. In plethora of new methods, we can find some, which are capable to analyse small samples. Medical and pharmaceutical sciences often lack big samples, which is true also in marketing and user satisfaction studies. To analyse small samplesclassification approaches often extract important variables for a given problem. Each variable is evaluated according to the class in question. In practice this means that we can consider only the variables with the strongest impact on the outcome of a given classification problem. Based on these considerations we conducted a survey about the role of clinical pharmacist in University medical Centre Ljubljana. A comprehensive literature search was performed to prepare a list of all possible clinical pharmacy activities. The list was further used to construct a survey questionnaire with Likert measurement scale to conduct a descriptive observational attitude study of physicians’ and nurses’ opinion about the importance of each of the listed activities and competencies of clinical pharmacists. The questionnaire was validated by three experts, a non-hospital pharmacist, hospital head physician, and a human resource manager. The questionnaire is composed of three types of questions. In the first part of the questionnaire (17 questions), clinical pharmacy activities pertaining to the hospital system are stated, while the second part of the questionnaire (19 questions) contains activities directly connected with individual patient’s care. The third part (16 questions) of the questionnaire deal with clinical pharmacist’s competencies. The participants had to choose the level of agreement on the Likert scale from 1 (I totally disagree) to 5 (I totally agree) with each of the listed affirmative statements in the part one and two of the questionnaire, while indicating the importance (form least important – 1 to very important – 5) of a particular competence in the third part. The questionnaire was sent to 43 physicians – medical directors or heads of departments – and to their head nurses. The survey results were collected in a spreadsheet, which served as a basement for all following analyses. In the first chapter we discuss the results obtained with nonparametric ANOVA tests. We observed considerable differences between pharmacists, physicians and nurses in total mean score of agreement to each statement and to each group of statements. The largest differences exist between the mean scores of pharmacists and nurses. The differences between physicians and pharmacists are also important. The differences between physicians and nurses are smaller. Total mean score of physicians’ agreement with the first and second group of CP activities is 4.28 and 3.73, respectively, while these scores are lower for nurses (3.87 and 3.38 for the first and second group, respectively). Pharmacists’ total mean scores are highest, 4.57 and 4.23 for the first and second group, respectively. Afterwards this preliminary phase we established clinical pharmacy service on designated wards. The satisfaction with the new service was assessed by the head of the pharmacy and the values were entered into the database. This appended database was used to perform analyses with data mining algorithms. In the second Chapter we present the results obtained with OrdEval algorithm, which can determine the importance of each CP activity and their type according to the users’ expectations. The results were used to categorize the activities/competences according to Kano model. Using analysis of individual feature values, we identified six performances, 10 excitements, and one basic clinical pharmacists’ activity. Other feature evaluation algorithms, like ReliefF and MDL, can also be used for the assessment of the importance of the variables. Survey data were analyzed using these two algorithms to identify the most important clinical pharmacists’ activities/competences. The results were compared with the expert estimation of the head of the pharmacy and to the values calculated using OrdEval algorithm. The results are analyzed in the third Chapter. We conclude that using different data mining algorithms is justified. In the case of congruence of the results their reliability is improved. The results on importance of CP activities for both algorithms agreed with the expert estimation for eight CP activities/competences. Finally, in the fourth Chapter we present the pilot case study report on the text analysis of the text of five semi-structured interviews with head physicians about their satisfaction with clinical pharmacist after establishing the collaboration. The text was analysed with QDA Miner text management and qualitative coding software (QDA Miner 4, Provalis research). We identified two reversal CP activities according to Kano model which were not discovered in our previous analyses

Attitudes of physicians, nurses and pharmacists concerning the development of clinical pharmacy activities in a university hospital

It is essential to identify the expectations of physicians and nurses regarding clinical pharmacy (CP) services before its introduction in a hospital, because it is known that their expectations can substantially differ from the pharmacists’ point of view. Agreement of leading physicians, nurses and clinical pharmacists about the importance of CP activities in the hospital was evaluated using five point Likert scale questionnaire. Two groups of CP activities were set; the activities related to the hospital system (first group) and the activities connected with an individual patient (second group). Total mean score of agreement of physicians with the first and second group of CP activities is 4.28 and 3.73, respectively, while these scores are lower for nurses (3.87 and 3.38 for the first and second group, respectively). Pharmacists’ total mean scores are highest, 4.57 and 4.23 for the first and second group, respectively