Department of Biostatistics and Medical Informatics
Introdução à Medicina 2005/2006
The mobility of elderly patients across healthcare institutions
Inês Videira,/ Inês Jorge,
/ Iolanda Ferreira,
/ Ivete Afonso,
Jennifer Pires,
/ Joana Ribeiro,
/ Joana Pina Vaz,
/ Joana Fernandes,
Joana Costa,
/ Joana Magalhães,
Adviser: Ricardo Correia, , Class: 10
Abstract
Introduction: The progress in Medicine and Informatics influences the development of health information systems allowing the communication between different healthcare institutions. Consequently, sharing patient’s data is possible, which contributes to a better quality of care. This data is spread in all the places where patients have received clinical services. Elderly people demand much of healthcare institutions and are the main consumers of the NHS, so it’s important they receive effective care and treatment, probably enhanced by the existence of information systems. In order to create an efficient information system and prioritize integrations, it’s necessary to know which medical institutions are the most required by elderly. Aim: To study elderly patients’ mobility across healthcare institutions. Methods:A transversal and retrospective study was held, being the unit of analysis all individuals over 65 years old living in Oporto region. A cross sectional telephone survey using random digit dialing was conducted asking for healthcare institutions visited during 2005. 100 participants completed the interviews, out of a target population of 113240 individuals. Results: Sample composed of 65 women,34 men and 1 missing value. The mean number of ages was 72.7 years old. The mean number of different healthcare institutions visited was 4.83 and 41% of the individuals attended at least 4 different types of institutions. In average, people go to 1 medical institution per type. Hospitals and pharmacies show bigger numbers of distinct institutions attended. During 2005, 10 in 24 of HSJ patients, 7 in 16 of HSA patients and 6 in 8 of IPO patients have visited other hospitals. Discussion:It was observed considerable mobility across different types of healthcare institutions opposing to preference registered to one institution within each type.Linking HSJ with HSA and HSJ with IPO would be advantageous for patients that attend both institutions. Information systems should exist to link different types of health services, providing a long-term benefit for those healthcare users.
Key Words: aged person; health information system; healthcare institution; medical record linkage.
Introduction
Medicine and informatics have developed side by side, progressing tremendously over the years. The organization of healthcare is intrinsically connected to this progress, which influences the further development of health information systems [1], improving population’s quality of life [2].
Clinical information can be defined as that which helps decision making. Physicians decide about the proper treatment to administrate by resorting to the two elementary pillars of medical information: clinical data (uniquely related to each patient) and clinical knowledge (specific of each physician, it improves the quality of healthcare [3]). This information is present in every medical record and is essential for it concerns individual patients [4]. A medical record is by definition handwritten or computer generated containing patient’s clinical data. The most relevant purpose of a medical record is providing evidence that will allow doctors to take the right decisions and follow the correct course of action towards the resolution of the patient’s problems [5].
The amount of clinical information is growing over the years, due to the fact that chronic diseases are rising, as well as interdisciplinary practice and tests [5], in addition to an increase in life expectancy. As the complexity of healthcare increases, the necessity of organizing the crescent information about patients arises. The adverse effects of uncoordinated information and gaps in communication affect mostly those patients who, for example, are receiving shared care, which involves more than one physician [6], and so communication is considered to be vital [7].
The necessity of a system that links allthe patient’s disseminated information is important, in order to provide better quality of care [8]. Nowadays, the physical location of a patient record can be substituted by a virtual one [9]. Thanks to information technology, it is possible to link databases found in various health services through, for example, the introduction of smart cards, portable health cards containing standardized patient’s data [2].
Patient’s data is spread in all the places where they have received clinical services [8], such as general practitioners, specialists, hospitals, private clinics and pharmacies [10].
It’s a fact that the Portuguese population is getting older, due to demographic changes in the country, which poses some questions related to the healthcare offered, utilization of medical services and healthcare expenditures [11]. Elderly people demand much more of health services than any other group, excluding babies [12], and are the main consumers of the National Health System (NHS) [13]. It is important that this group receives effective care and treatment [13], probably enhanced by the use of technology, required to construct a health information system linking medical services where they have received treatment. So, to create an efficient health information system and prioritize integrations, it is important to know which medical institutions are the most required by older patients.
The aim of this paper is to study elderly patients’ mobility across healthcare institutions.
Participants and Methods
Study design
This study is observational and transversal, as well as retrospective. Data is collected in a single moment (one phone interview per individual), asking for events occurred in the previous year.
Study participants
The study population consisted of the Portuguese speaking adultpopulation of Oporto area, aged 65 years or above.
The Oporto region is defined by:Espinho, Gondomar, Matosinhos, Maia, Oporto, Paredes, Stª Maria da Feira, Trofa, Valongo, Vila do Conde, Vila Nova de Gaia, being the total population 113240.
A random sample of 100 adult individuals aged 65 or over, living in the Oporto region was surveyed by telephone, according to a defined questionnaire.
Data collection methods
The telephone survey was made by Random Digit Dialing (method reviewed by Mitofsky and Waksberg). A two stagerandom sample was obtained. The sample units in the first stagewere households in Oporto area (numbers with 22 as a prefix). Each eligible household was contacted by randomdigit dialing,in which the last two numbers of the ninedigit telephone number, which were selected by a simple randomsampling method from telephone directories, were randomly generatedusing computer programs. Non-household numbers and those whichwere not in use were excluded, as households which inhabitants were less than 65 years old. The second stage was to select all individuals aged 65 years or over within each eligiblehousehold. After telephone contact and self introduction, theinterviewer explained the purposes of the study and oral consentwas sought. The adult who answered the telephone was asked howmany people aged 65 or above were living in that household. All people available to the study, which requests were fulfilled, were inquired to the study. In case of a physical disability of the elderly, the questionnaire would be applied to the person in charge.
Telephone interviewing took place over a 5 week time period during February and March 2006, between 9 am and 7 pm.The average interview lasted approximately ten minutes.
Questionnaire design
Data collected from the questionnaire included sociodemographic characteristics - age of individual, sex, town - and questions related to the aim of the study – number and type of health institutions attended, including hospitals, health centers, private laboratories, private physicians and pharmacies, as well as services attended in different institutions. Five questions were developed and modified in a small scalepilot after interviewing seven individuals. To help people remember the types of private clinics they visited, the inquirers agreed to pose the question step by step, thereby enumerating some of the existent types.
Variables description
The variables obtained from the questionnaire are age, gender, number of each type of healthcare institution attended (Hospital, health centre, Private Laboratories, Private Physician and Pharmacies) anddescription of the visited hospitals.
When applying the questionnaire, the gender – a categorical variable - was not directly asked. Instead it was presumed from the interviewer. The nominal variables concerning the number of different healthcare institutions attended were obtained by simply questioning how many different health care institutions were visited. In order to identify the attended hospitals, a nominal variable, it was provided a list, being some enumerated to help the individuals remember them. Different hospitals not existent in the list were also accepted and added.
From these variables new ones were created: the sum of healthcare institutions visited within each type and the sum of all institutions attended, both categorical.
In order to establish the different mobility patterns presented by the study, two different accessory variables were generated.Other key variables concerned relations between hospitals, being HSJ, HSA, IPO the most significant ones.
Statistic analysis
Data collected were submitted to statistic treatment, which included tests to establish several relations (see results). Simple frequency distribution was used to show the characteristicsof the subjects and their answers to the five research questions. Also, relations between variables were defined using multiple response tables, after making, if necessary, variable recodification and computation. A non-parametric test (Mann-Whitney) was made to compare the differences between men and women, concerning to the different types of healthcare institutions attended. It was considered p-level of 0.05.All analyses were performed using SPSS 13 for Windows.
Results
Sample description
During February and March of 2006, data were collected from households in a total of 100 questionnaires.Of the 1892 calls made, 1538 (81%) were invalid for the study, being 1226 (65%) non existent numbers and 312 (16%) non residential. Only 354 out of the 1892 calls made (19%) were valid (answered telephone calls which correspond to households). From these 354, 254 (72%) were excluded, as the individuals did not fulfill the age required or refused to answer to the inquiry. The response rate was, as a consequence, 58 % (100 out of 172) – see figure 1. The margin of sampling error for the sample as a whole is ± 5.82%.
The obtained sample consisted of 65 women, 34 men and 1 missing, with ages between 65 and 90 years old, being the mean number of age 72.7 years old.
Figure1. Characterization of made telephone calls. TAPC: total amount of phone calls; IPC: invalid phone calls; VPC: valid phone calls; NEN: non-existent number; NRN: non-residential number; IA: inappropriate age; RA: refused to answer; AQ: answered questionnaires.
Healthcare institutions attended
The mean number of the amount of different healthcare institutions visited in 2005 was 4.83. A percentage of 41 individuals attended at least 4 different types of institutions (hospitals, health centers, private laboratories and pharmacies), of which 22 also went to private physicians (see figure 3). About 1.02 different hospitals (σ=0.76) were attended as well as 0.77 different health centers (σ=0.55), 0.93 different private laboratories (σ=0.59), 0.70 different private physicians (σ=0.74) and 1.42 different pharmacies (σ=1.06). This data is supported from further analysis of figure 4, showing that the majority of individuals attend only one medical structure per type. This fact is more evident in health centers (68%) and laboratories (64%), followed by pharmacies (54%), hospitals (51%) and private physicians (45%). Although this is the most common situation, 24% of the inquired individuals went to more than one different hospital and 33% respondents attended more than one pharmacy.
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Figure 2.Distribution of the total amount of healthcare institutions elderly people visited in 2005
Figure 3. Mobility pattern considering distinct types of institutions, apart from how many were visited within each type. Hp: Hospital; HC: HealthCenter; Lab: Private Laboratories; PP: Private Physician; Ph: Pharmacy.
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Figure 4. Number of healthcare institutions attended to within the same type and the corresponding amount of individuals who visited them, in percentage (sample of 100 individuals).
Health institutions patterns
Table 1. Existingrelations between hospitals visited and associated amount of elderly people. HSJ: Hospital de São João; HSA: Hospital de Santo António; IPO: Instituto Português de Oncologia; VNG: Hospital de Vila Nova de Gaia; CHVNG: Centro Hospitalar de Vila Nova de Gaia; PH: Hospital Pedro Hispano; Prelada: Hospital da Prelada; Valongo: Hospital de Valongo.
When analyzing hospitals(see table 1) it is registered that Hospital de São João (HSJ) was the most visited, followed by Hospital de Santo António (HSA), Vila Nova de Gaia (VNG), Instituto de Português de Oncologia (IPO), Hospital da Prelada and Hospital de Valongo. Of the 24 elderly that attended HSJ, 10 also went to other hospitals: 4 visited HSA, 2 IPO, 1 Valongo, 1 Prelada, 1 HSA and Valongo, 1 IPO and Prelada.
The most common mobility pattern is going to one hospital, one health center, one private laboratory and one pharmacy, with a total of 11% respondents who did so.
Figure 5. Mobility pattern focusing on number of different institutions visited per type. Hp: Hospital; HC: HealthCenter; Lab: Private Laboratories; PP: Private Physician; Ph: Pharmacy.
Mobility results by gender
It was performed a Mann-Whitney Test to find out if the differences between men and women were statistically significant, from which it was verified that frequency distribution of women, concerning hospitals, ishigher than men (p=0.026). There were not observed statistically significant differences related to health centers (p=0.445), private laboratories (p=0.629), private physicians (p=0.661) or pharmacies (p=0.878). These results are supported by central tendency and dispersion measures (see table 2). The median equals one in both genders, for each type of healthcare institution.
Table 2. Mean numbers and standard deviation (SD) of attended healthcare institutions, within the same type, by men and women separately and both genders together
Hospitals / Health Centers / Private Laboratories / Private Physicians / PharmaciesMean / SD / Mean / SD / Mean / SD / Mean / SD / Mean / SD
Men / 0.76 / 0.60 / 0.71 / 0.46 / 0.97 / 0.63 / 0.79 / 0.88 / 1.41 / 1.10
Women / 1.14 / 0.80 / 0.82 / 0.58 / 0.91 / 0.58 / 0.66 / 0.67 / 1.45 / 1.10
Both Genders / 1.02 / 0.76 / 0.77 / 0.55 / 0.93 / 0.60 / 0.70 / 0.75 / 1.42 / 1.10
Discussion
Mobility has been registered across different healthcare institutions, so linking them with health information systems appears to be a relevant issue (4.83 different healthcare institution visited in one year).
People tend to be more faithful to one healthcare institution per type, being this fact more prominent in health centers and private laboratories. Hospitals and pharmacies show a bigger amount of institutions visited, suggesting certain mobility of elderly patients across those structures.
A significant percentage of individuals go to more than one hospital (24%) and considering that this type of institution is a fundamental key of the healthcare system, the existence of information systems linking the most demanded hospitals would probably improve the provided services. Almost half (42%) of HSJ’s users have their clinical information also located in other hospitals,beingHSA and IPO the most relevant. Therefore, HSJ should be primarily linked to HSA, with a long-term benefit for 21% (5 out of 24) of the elderly population attending HSJ. Next in a hypothetical priority list should be IPO and HSJ, given that 13% of HSJ’s patients would profit.From the perspective of HSA, 31% of its elderly patients also attend HSJ, from which becomes clear that having HSA linked to HSJ is highly beneficial.The same can be concluded for IPO, where 38% of its patients establish connections with HSJ. The existence of health information systems linking the most demanded hospitals of the Oporto’s region would benefit at least one third of the elderly population that visit them.
The little mobility registered within the same type of medical institutions (approximately one per type) contrasts with the mobility of patients across different types of healthcare structures.When focusing on the more frequent mobility pattern, it can be extrapolated that the majority of individuals attend only one hospital, health center, private laboratory and pharmacy, excluding private physicians, so those structures should be given priority in their linkage.
It was also studied the mobility between men and women, from which it was concluded that women visited, in average, more hospitals than men. The other health institutions show no significant differences between the two genders. It should be expected that an elderly person attends preferably one medical institution per type.
Based on the fact that sharing information leads to improvements in productivity and better quality of care [8], information systems should be implemented to allow the communication between different types of healthcare institutions.
Limitations
This cross-sectional study presented different types of limitations that may have affected some of the discussed issues. First, there is a noncoverage bias: not every individual in target population owns a household phone number. Also, the time period in which interviews occurred was restrict, what limited the representativity of the sample. Telephone surveys may not reflect the studied population, because it does not include people who refused to participate. Since the study is transversal, data collected in one moment may not also reflect the reality due to people’s memory lapses.
Acknowledgments
This project was supported by the Department of Biostatistics and Medical Informatics of the Faculty of Medicine – University of Porto.
We would like to thank our adviser, Dr. Ricardo Correia, for guiding us through this project and helping us to overcome our difficulties. We express our gratitude to Dr. Clara Tavares, who helped with the statistic analysis and telephone surveys, Dr. Luís Filipe Azevedo, for teaching us the random digit dialing method, Bruno Neves and Joana Mendonça, for assisting us with the bibliographic research. We also want to thank Prof. Dr. Altamiro Costa Pereirafor all the suggestions made.
References