Category Archives: Healthcare Systems

Meaningful use of eHealth in Acute Hospital in Europe – Benchmarking eHealth deployment

A few months ago I posted about a report titled A Composite Index for the Benchmarking of eHealth Deployment in European Acute Hospitals Distilling reality into a manageable form for evidence-based policy co-author with Cristiano Codagnone. On 23th March I was invited by the Catalan Agency for Health Information, Assessment and Quality to present the benchmarking exercise, including in the analysis data about Acute hospital in Catalonia gathered by TicSalut.

I wonder if this benchmarking exercise could be understood as a meaningful use of eHealth in Acute Hospital in Europe. The composite indicator is presented in a transparent manner so any practitioner or policy-maker can utilise the weights to benchmark its acute hospital within its country and with other European countries. This can naturally lead to the definition of a different approach to the construction of eHealth deployment and usage composite indicators and may at any rate produce standardised and comparable longitudinal and cross-sectional data.

Enhanced data gathering through active collaboration with Healthcare Actors

Citizens and ICT for Health in 14 EU countries: results from an online panel survey

Social determinants of Health and ICT for Health (eHealth) conceptual framework

Lately I have been designing, launching and gathering an online panel survey to a representative sample of Internet users in 14 European countries (approximately 14,000 responses). To ground the questionnaire I have developed a conceptual framework inspired and based on the two main sources. On the one hand, the Marmot Review team:

On the other hand, a Framework for Digital Divide Research developed by Jan van Dijk in several publications:

In a recent presentation about Health and Web 2.0 I tried to match both frameworks and I have posted about Inverse care law 2.0  several times using different scientific and statistical sources.  It is worth pointing out (and obviously reasonable) that I have not found any references or mentions to ICT for Health in the literature about social determinants of Health gathered through Marmot Review team website.

a-conceptual-framework-for-action-on-the-social-determinants-of-health-discussion-paper-for-the-commission-on-social-determinants-of-health

However, both frameworks (see red boxes in both figures) mention individual and social characteristics as social determinants of health and of the Internet usage. Furthermore, van Dijk includes HEALTH and ABILITY as a personal category (and I have added Health as a sphere of participation in Society and emphasis the Divides).

deeping-digital-divide

Based on and inspired by this two frameworks I have developed Social determinants of Health and ICT for Health (eHealth) conceptual framework.

social-determinants-of-health-and-ict-for-health-conceptual-framework

All concepts and boxes  of this framework are based on scientific references and the relationships established by arrows have been empirical or theoretical driven. I’m currently working on it, however I have shared this framework to gather inputs to improve it. I would love to know your comments and ideas.

UPDATE: Citizens and ICT for Health in 14 EU countries: results from an online panel survey

A Composite Index for the Benchmarking of eHealth Deployment in European Acute Hospitals Distilling reality into a manageable form for evidence-based policy

A Composite Index for the Benchmarking of eHealth Deployment in European Acure HospitalsIn a previous post entitled Benchmarking HIT Adoption in European Healthcare Organisations several challenges, including transparency, were mentioned. To tackle of those challenges, during the past few months I had the pleasure to collaborate with my colleague Cristiano Codagnone in the development of JRC Scientific and Technical Report entitled “A Composite Index for the Benchmarking of eHealth Deployment in European Acute Hospitals Distilling reality into a manageable form for evidence-based policy” published May 2011 .

Compared to other areas of the Information Society, where benchmarking has been conducted more systematically for longer (i.e. eGovernment), it is evident that benchmarking of eHealth deployment is lagging behind.

In this context, the results of the eHealth Benchmarking, Phase III survey, carried out by Deloitte and IPSO on behalf of Unit C4 of DG INFSO, with the rich information provided on about 1,000 European acute hospitals, could be a strategically important tool to close this gap. As we show in more detail later, this survey sheds light on key issues such as hospitals’ deployment of ICT infrastructure, applications, and much more.

The reasons why benchmarking of eHealth deployment is lagging behind are structurally related to the multi-dimensional complexities of this field, to the relatively greater difficulty/costs of getting the data (i.e. data cannot come from web-based measurement, as it can for eGovernment benchmarking), and especially to the challenges of making sense of the data.

This report uses multivariate statistical methods to analyse with a selective but deep vertical focus the results of the above-mentioned survey. The objectives of this exercise are two-fold:

a) to make sense of the results by constructing a composite index;
b) to extract key policy messages and new directions for future research.

The main objective is the elaboration of a composite index of eHealth deployment with a view to proposing a roadmap towards systematised and replicable benchmarking. In addition, we also explore the possible link between benchmarking and eHealth impact.
Therefore, our focus is much more selective but deeper than the broader descriptive analysis produced by Deloitte and Ipsos. In addition, we do not simply conduct multivariate statistical analysis but we put this into a conceptual and theoretical perspective and we follow it with a discussion of the results and with a set of policy and research recommendations.

This first introductory section is followed by four more. Section 2 provides the general conceptual and theoretical framework for benchmarking within an international policy perspective. Section 3 presents the data and the methodology used. In Section 4, we present and comment on the results of our multivariate statistical analysis. Finally, in Section 5 we discuss these results and extract recommendations for future research and policy making.

The Composite Index

The Hospital eHealth Deployment CI has been developed following a totally transparent multistage approach, which is graphically rendered in the figure below:

Composite index Figure

ehealth_figure7

Countries with more intensive (per capita) healthcare spending in ICT score higher in our hospitals eHealth Deployment CI and it seems now perfectly sound that Italy, France and Germany have lower than expected CI in view of the fact that their ICT expenditure is considerably less intensive than in countries such as for instance Denmark, Sweden, and Norway. The data used are too aggregate and we do not dare going further than simply pointing out a mere statistical association. Yet, at least the direction is comforting: if it was negative (high rank in CI associate with low level of spending intensity) than we might have had a problem.

figure_12

We replicated the operation done with ICT expenditure in healthcare with the following supply side indicators: “Hospital beds – Per 100,000 of population”; “Practising physicians – Per 100,000 of population”; “Number of Computer tomography scanners per 100,000″.

Again we stress that our aim was explorative and we looked for mere trends and statistical associations, with no claim to demonstrated significant statistical correlations and even less so infer causal relation. Yet, all of the trends illustrated in the following figures are comforting and not counterintuitive with respect to what one would expect as a result of wide introduction of eHealth on the above three supply side indicators: a) it would be counterintuitive and challenging to find the our CI is higher in countries with the highest number of hospital beds; b) it would be counter-intuitive and challenging to find the our CI is higher in countries with the lowest number of practicing physicians; c) it would be counter-intuitive and challenging to find the our CI is higher in countries with the highest number of computer tomography scanners. The trends in the figures do not support such instances. Naturally, we do not claim that having a higher CI enable to use fewer beds, to support more physicians, and to substitute scanners, for a much more in depth and granular analysis would be needed to substantiate this hypothesis. We simply observe that at least the direction of the trend is in line with what one may expect from relatively higher deployment of eHealth in hospitals.

Despite very relevant comparability problems, we can risk concluding that the results of the eHealth Benchmarking Phase III survey show that progress has been made in Europe with respect to the levels of eHealth deployment registered in previous, less systematic and extensive data gathering activities such as Business Watch and Hine. For instance, the penetration of Electronic Patient Records (EPRs) has increased from the 34% reported for 2006 by Business Watch to the current 81%. This 81% penetration of EPRs puts
Europe way ahead of Japan and US, where only between 10% and 15% of hospitals have introduced them. However, there are also several indications of areas in need of policy action, of which we emphasise the following four:

1) The CI shows large scope for improvement. The average EU27 CI stands at 0.347, whereas that of top scoring Sweden is just slightly above 0.5. This means that there is still room for general improvement.

2) Wide variation across countries. In particular, the lowest deployment measured by our CI is concentrated mostly among the new Member States and candidate countries. Of the bottom 13 countries, 12 are from this group – Greece is the exception. The only new Member State that scores above the EU27 average is Estonia, confirming its excellence in the domain of ICT. This calls for awareness raising policies and possibly financial support targeting this group of countries.

3) The summary indexes of the four dimensions identify areas to be prioritised. Whereas infrastructure deployment is quite high in most countries, electronic exchange of information lags behind fairly generally (across countries). It is important to close this gap, since these exchanges constitute one of the pillars of the vision and promises of ICT-supported integrated personal health services. These services are the key to producing better health outcomes while pursuing system sustainability and they must be developed around a seamless view of the user, for which exchange of information and timely clinical decisions are crucial. Yet, our analysis shows that electronic exchanges are still limited among the potential interacting players. Furthermore, cross-border exchanges are extremely limited, a gap that from the perspective of EU policy should be quickly addressed.

4) Predominant intramural orientation. From both simple descriptive statistics and from our multivariate statistical analysis, it emerges clearly that the deployment of eHealth in hospitals has been predominantly focussed on intramural needs and applications. For instance, levels of deployment for Personal Health Records and home-based Telemonitoring are very low. We need to stress that if the objectives and targets of the upcoming European Innovation Partnership on Active and Healthy Ageing are to be realised, much more progress will be needed in terms of both electronic exchange of information and user-oriented applications and services, such
as PHR and Telemonitoring.

Evaluation of Integrated Care: From methods to governance and applications – Economics of eHealth

Based on The Economics of eHealth (I) and some inputs from my colleague Cristiano Codagnone I have developed my presentation to “Recent Developments and Future Challenges of Integrated Care in Europe and Northern America” – 11th International Conference on Integrated Care organised by The International Network of Integrated Care, The Julius Center of the University Medical Center Utrecht and the University of Southern Denmark (March 30 – April 1, 2011 in Odense, Denmark). I would like to thank Dr. Albert Alonso for his invitation to participate in the conference.

The Economics of eHealth (I)

Lately, I have been reading and checking the research literature  about Health economics and ICT . This is the first post of a collection about this topic. Soon I’ll be sharing with you all my notes.

The Economics of eHealth (I)

Health economics, an applied field of economics, draws its theoretical inspiration principally from four traditional areas of economics: finance and insurance, industrial organisation, labour and public finance (Fuchs, 1997). Culyer and Newhouse, editors of the Handbook of Health Economics (2000), developed a schematic of Health Economics based on a first approach developed by Williams (1987).

Health Economics Scheme

Culyer and Newhouse (2000) stated that Box A contains the conceptual foundation; Box B is concerned with the determinants of health; Box C concerns the demand for health care while Box D contains the supply-side economics. These four boxes are the disciplinary “engine room”. On the other hand, the four peripheral boxes E, F, G and H are the main empirical fields of application. Box E deals with the ways markets or quasi-markets operate. Box F is more specifically evaluative and normative. Box G focuses on the great variety of health care delivery institutions, insurance and reimbursement mechanisms and the various roles played by different agencies. Box H is concerned with the highest level of evaluation and appraisal across systems and countries.

Within this broad scheme health economists have started to tackle the landscape of ICT in health systems. Investments in ICT in health care are greater than they have ever been, and in most countries, not less than 2.6%-6% of health budgets are dedicated to ICT. These investments are being presented as a means to improve productivity, quality of health and/or system efficiency (Lapointe, 2010). Case studies reported by OECD (2010) stated that ICT implementation benefits could be grouped according to four inter-related categories of objectives: (1) Increasing quality of care and efficiency; (2) Reducing operating costs of clinical services; (3) Reducing administrative costs and (4) Enabling entirely new modes of care. Therefore, ICT in health systems affects the disciplinary “engine room” of health economics, due to its impact on health care demand and supply, as well as the main empirical fields of application.

Lessons learned from these case studies pointed out that successful implementation and widespread adoption are linked to the ability to address three main issues: (1) Alignment of incentives and fair allocation of benefits and costs; (2) Lack of commonly defined and consistently implemented standards; and (3) Concerns about privacy and confidentiality.

Within this context OECD (2010) claimed that Governments could provide motivation for high-performing projects through targeted incentives and also occupied a central position as initiator, funding provider, project facilitator, and neutral convener, playing a special role to encourage the utilization of standards to reach a common goal. Furthermore, the main findings of this study could be summarised as follow: (1) Establish robust and coherent privacy protection; (2) Align incentives with health system priorities; (3) Accelerate and steer interoperability efforts; and (4) Strengthen monitoring and evaluation.

However, it is worth pointing out that this study mentioned an absence, in general, of independent, robust monitoring and evaluation of programmes and projects to determine the actual payoff from the adoption and use of ICT. Due to the special characteristics of ICT market, Christensen and Remler (2007) mentioned that the main barriers to ICT adoption in health sector (low product differentiation, high switching costs in replacing technologies, and lack of technical compatibility of all the different components of ICT) explained why it lags behind other sectors in ICT adoption, even though the centrality of information exchange in the care process and its usefulness in management, accountability, research and financial transaction (Street, 2007).

A particular problem in health sector is that there is no measure of performance analogous to profits from private sector firms, and health care organisations tend to pursue multiple objectives. Furthermore, ICT implementation may have effects that are multidimensional and often uncertain in their reach and scope, and difficult to control. In addition, the realisation of benefits from ICT implementation strongly depends on contextual conditions (Street, 2007). On the one hand, these difficulties are further exacerbated by data limitations, definitional problems and lack of appropriate sets of indicators on adoption and use of ICT comparison. On the other hand, dimensions related with measurement errors, time lag, redistribution and mismanagement of ICT are being pointed out within the application of “productivity paradox” (Brynjolfsson, 1993; Brynjolfsson & Hitt, 1998) into health care. These dimensions are essential to understand the competitiveness and profitability of health care organizations investment in ICT (Lapointe et al., 2010).

There has been a significant and growing debate internationally about whether or not these much touted benefits and savings are gained or, indeed, even measured (OECD, 2010). This debate has been supported decision makers to belief that clear profitability has not been demonstrated (Meyer, 2010). The need to accurately quantify the added value of ICT in health care sector has reached a critical requirement level (Meyer, 2008).

To understand the real impact of ICT within health care adopting a single analytical approach is inadvisable and that insight into the overall effects of ICT is best gained from consideration of a mix of study types (Street, 2007). Empirical studies into the impact of ICT could be grouped into four broad categories: (1) Aggregate analyses that take a macro perspective by looking at the economy as a whole; (2) Industry or sectoral level analyses that focus on specific industries or sectors within the economy; (3) Firm or organisational-level analyses and (5) Case studies that focus on specific examples of ICT (Street, 2007).

Due to the characteristics of health sector below mentioned applying an aggregate analysis or a sectoral level analysis remains difficult. Street (2007) has summarised the key advantages and challenges associated with each analytical approach:

Summary of the key advantages and challenges associated with each  analytical approach

All the references cited could be found at my online personal reference manager.

Benchmarking HIT Adoption in European Healthcare Organisations

About the conference

The HIMSS Europe Health IT Leadership Summit, scheduled for 29 September – 1 October 2010 in Rome, Italy, is a new, executive level forum for education, collaboration and dialogue. Top leaders from healthcare, IT and government will convene to help advance the quality of healthcare delivery. The event will feature conference and education sessions as well as a leadership summit designed to foster intensive knowledge exchange and networking opportunities at the most senior level.

Benchmarking HIT Adoption in European Healthcare Organisations
Uwe Buddrus, Managing Director, HIMSS Analytics Europe
HIMSS Europe Health IT Leadership Summit,  29 September – 1 October 2010 Rome, Italy.

Mr. Uwe Buddrus has introduced HIMSS Analytics Europe

HIMSS Analytics Europe (HAE) is a wholly-owned subsidiary of the Healthcare Information and Management Systems Society (HIMSS). The company collects and analyzes healthcare information related to IT processes and environments, products, IS department composition and costs, IS department management metrics, healthcare trends and purchase-related decisions. HIMSS Analytics Europe delivers high quality products, services, and analytical expertise to healthcare delivery organizations, healthcare IT companies, state and federal governments, financial companies, pharmaceutical companies and consulting firms. HAE’s offerings include comparative Hospital IT adoption benchmarking and a European-formulated EMR Adoption Model (EMRAM) scale to help hospital directors, IT executives and clinicians compare and measure their progress in the adoption and use of healthcare information technology. Country level and application specific reports also provide insights into major IT adoption trends.

and its EMR Adoption Model

Understanding the level of electronic medical record (EMR) capabilities in hospitals is a challenge in the European healthcare IT market today. HIMSS Analytics Europe has adapted the EMR Adoption Model created by HIMSS Analytics and established across the U.S. and Canada. The model identifies the levels of electronic medical record (EMR) capabilities ranging from limited ancillary department systems through a paperless EMR environment. HIMSS Analytics Europe has developed a methodology and algorithms to automatically score hospitals in our database relative to their IT enabled clinical transformation status, to provide peer comparisons for hospital organizations as they strategize their path to a complete EMR and participation in an electronic health record (EHR).

presenting also EMR Adoption Model Modification for Europe

Key proposed modifications are:

  1. Stage 1 – can also be achieved if hospitals without Lab, Radiology or Pharmacy are able to process results delivered back to the hospital for online access.
  2. Stage 2 – can be achieved if the hospital has either CDR / EPR or a Clinical Data Warehouse installed.
  3. Stage 4 – can also be achieved by ePrescribing as a form of CPOE.
  4. Stage 5 – can also be achieved if full Radiology-PACS is implemented as  an alternative to closed loop medication (both are required in Stage 6).

Benchmarking HIT Adoption Model includes:

  • Data (adopted eHospital Census);
  • Information (Statistical analysis and reports);
  • Intelligence (Conclusion, Decision and Strategies);
  • Successful HIT Adoption.

Questions: Address not only Hospitals but Primary care and the continuum of healthcare.
Reply:
Difficulties to measure this  continuum of healthcare around Europe with different health care systems

Questions: How about regional level instead of national level?
Reply:
Another challenge to compare data around Europe

Questions: What about patients and Personal Health Records?
Reply
: It is too early to measure this around Europe.

Questions: How and why do you place the different applications within the stages? Is it based on USA model?
Reply
: There is no consensus around it, it is difficult to identify the same terms and vocabulary around Europe for the same kind of applications.

Questions Representativeness of eHospital census? isn’t better to compare just Hospitals not countries?
Reply
: Totally agree

Questions Compare this model with a performance model, even impact assesment?
Reply
Key challenge

Questions What about addressing Europe challenges? Privacy and interoperability, reimbursement
Reply
Another key challenge

Questions What is the Cost and ROI of moving from stage to stage?
Reply
It has not been developed yet, it would be a great work.

Digital Health Care Demand or eHealth Care Demand in Europe – Eurostat

Lately, I have been working with aggregate data from Eurostat ICT usage household survey focused on the usage of the Internet and Health by European citizens. It is worth mentioning that this is a working in progress. Below you can find all variables available per year.

Table 1. Variables available per year at Eurostat ICT usage household survey

Caption Years
The type of goods and services I ordered over the Internet in the last 12 months for on-work use are: Medicine 2009
The type of goods and services I ordered over the Internet in the last 12 months for non-work use are: Non-prescribed medicine 2009
The type of goods and services I ordered over the Internet in the last 12 months for non-work use are: Prescribed medicine 2009
I would like to do on-line: health-related services (eg interactive advice on availability of services in different hospitals, appointments for hospitals) I_GOVHLP *** 2006
I’ve already done on-line: health-related services (eg interactive advice on availability of services in different hospitals, appointments for hospitals) I_GOVHLY *** 2006
I have used Internet, in the last 3 months, for seeking medical advice online with a practitioner I_IHAD 2003 2004 2005
I used Internet, daily, for seeking medical advice online with a practitioner 2003 2004
I used Internet, monthly, for seeking medical advice online with a practitioner 2003 2004
I rarely used Internet for seeking medical advice online with a practitioner 2003 2004
I used Internet, weekly, for seeking medical advice online with a practitioner 2003 2004
I have used Internet, in the last 3 months, for making an appointment online with a practitioner I_IHAP 2003 2004 2005
I used Internet, daily, for making an appointment online with a practitioner 2003 2004
I used Internet, monthly, for making an appointment online with a practitioner 2003 2004
I rarely used Internet for making an appointment online with a practitioner 2003 2004
I used Internet, weekly, for making an appointment online with a practitioner 2003 2004
I have used Internet, in the last 3 months, for seeking health information on injury, disease, nutrition, improving health, etc.) I_IHIF 2003 2004 2005 2006 2007 2008 2009
I used Internet, daily, for seeking health information on injury, disease or nutrition 2003 2004
I used Internet, monthly, for seeking health information on injury, disease or nutrition 2003 2004
I rarely used Internet for seeking health information on injury, disease or nutrition 2003 2004
I used Internet, weekly, for seeking health information on injury, disease or nutrition 2003 2004
I have used Internet, in the last 3 months, for requesting a prescription online from a practitioner I_IHPR
2003 2004 2005
I used Internet, daily, for requesting a prescription online from a practitioner 2003 2004
I used Internet, monthly, for requesting a prescription online from a practitioner 2003 2004
I rarely used Internet for requesting a prescription online from a practitioner 2003 2004
I used Internet, weekly, for requesting a prescription online from a practitioner 2003 2004

Due to the data available, I have chosen 2005 to carry out a cluster analysis to develop a typology of Digital Health Care Demand or eHealth Care Demand in Europe. This is just a first step, the aim is to analyse the drivers and barriers of eHealth in Europe from the demand side. The next table shows the percentage of total individuals by country, (I_GOVHLP and I_GOVHLY  2006)

Table 2. eHealth variables available 2005 at Eurostat ICT usage household survey

COUNTRY I_IHIF I_GOVHLP I_GOVHLY I_IHAD I_IHAP I_IHPR
AT 0,160251 0,227954 0,029526 0,004705 0,007748 0,002626
BE 0,1914 0,183276 0,017496
BG 0,122097 0,009661
CA 0,58
CY 0,080042 0,195897 0,00136 0,002788 0,000551 0,001706
CZ 0,034713 0,096489 0 0,003308 0,003896
DE 0,325024 0,021079
DK 0,238382 0,41458 0,030644 0,02493 0,016902 0,007123
EA 0,162218 0,251884 0,017979 0,005567 0,003255
EA16
EE 0,163774 0,19904 0,030725 0,108567 0,083567 0,049192
EL 0,021608 0,131537 0,001472 0,003506 0,001282 0,000433
ES 0,12751 0,035553 0,024121 0,004409 0,000907
EU15 0,181379 0,245943 0,021312 0,019812 0,005783 0,004567
EU25 0,160867 0,227894 0,019862 0,017239 0,005385 0,004105
EU27 0,160867 0,216619 0,018973 0,017239 0,005385 0,004105
FI 0,38971 0,273988 0,012912 0,027487 0,033743
FR
HR
HU 0,096088 0,119384 0,053411 0,007618 0,004358 0,001954
IE 0,104775 0,043551 0,008591 0,006391 0,001092 0,000679
IS 0,394597 0,388167 0,119582 0,027373 0,013017 0,007694
IT 0,087271 0,17439 0,008182 0,015709 0,003513 0,001996
LT 0,085267 0,179464 0,020233 0,012467 0,002665 0
LU 0,410491 0,367017 0,015976 0,016174 0,006943 0,007728
LV 0,073536 0,156269 0,009821 0,004649 0,001445 0,00052
MK
MT 0,161734 0,122316 0,010373 0,004483 0,003271 0,001495
NL 0,407242 0,387001 0,008289 0,017196 0,007235 0,014481
NO 0,257325 0,36145 0,021409 0,0113 0,008636 0,002586
PL 0,071422 0,174514 0,00652 0,004253 0,001301 0,000916
PT 0,100267 0,174263 0,003997
RO 0,062036
RS
SE 0,232107 0,295204 0,061132 0,042376 0 0,009893
SI 0,153678 0,268123 0,011941 0
SK 0,091329 0,214051 0,013288 0,000493 0,001545 0,000192
TR 0,031177 0,002588 0,000698 0,000024
UK 0,254573 0,200448 0,02922 0,044428

With these values I have constructed new categorical variables considering  descriptive statistics (percentiles per each variable) as follow: less than 25% is LOW, between 25% and 75% is MEDIUM and more than 75% is HIGH.

In order to develop a typology of countries’ utilization of the Internet related with Health, a Non-Hierarchical Cluster Analysis of K-means was undertaken, to five of the five variables identified above I_IHIF, I_GOVHLP, I_GOVHLY, I_IHAD, I_IHAP (Table 2). These factors were selected due to their significance within the cluster analysis.

Table 3. Results of K-means—quick cluster analysis. Method of analysis: non-hierarchical cluster, final cluster centroids.

Clusters

1. Digital Health Care Demand Leaders (n=8)

2. Digital Health Care Demand Primary Strivers (n=3)

3. Digital Health Care Demand Secondary Strivers (n=12)

ANOVA

Sig.

I_IHIF

2,5

3

1,67

12,733

0

I_GOVHLY

3

1,67

1,58

18,345

0

I_GOVHLP

2,5

3

1,58

14,053

0

I_IHAD

2,5

2,33

1,67

6,793

0,006

I_IHAP

2,5

2,67

1,92

4,173

0,031

Cluster one consists of countries where citizens place a greater emphasis on the Internet for health purposes, specially those variables related with health care services. This group is thus referred to as representing ‘Digital Health Care Demand Leaders’. Cluster two is characterised by a minimum difference, with less emphasis on variables related with health care services (transactions) and more emphasis on information, so are consequently labelled ‘Digital Health Care Demand Primary Strivers. Finally, Cluster 3 is labelled ‘Digital Health Care Demand Secondary Strivers

And now… the floor is yours…. pick up a country a put it on a cluster… soon I will post the characterization of the cluster analysis, including traditional, non-digital, variables from the health systems. Furthermore… what about 2010?

Austria AT
Belgium BE
Bulgaria BG
Cyprus CY
Czech Republic CZ
Germany DE
Denmark DK
Estonia EE
Greece EL
Spain ES
Finland FI
France FR
Croatia HR
Hungary HU
Ireland IE
Iceland IS
Italy IT
Lithuania LT
Luxembourg LU
Latvia LV
Macedonia MK
Malta MT
Netherlands NL
Norway NO
Poland PL
Portugal PT
Romania RO
Sweden SE
Slovenia SI
Slovakia SK
Turkey TR
United Kingdom UK
EU (15 countries) EU15
EU (25 countries) EU25
EU (27 countries) EU27

THANKS indeed Ismael Peña for his inspiring work

The integration of Information and Communication Technology into medical practice

I’m delighted to announce that the article entitled “The integration of Information and Communication Technology into medical practice” has been accepted and is already in press at the  International Journal of Medical Informatics. As soon as possible I will upload a pre-print version.

PREPRINT

Please cite this article as:

Lupiáñez-Villanueva, F., Hardey, M., Torrent, J., & Ficapal, P. (2010). The integration of Information and Communication Technology into medical practice. Int J Med Inform, 79(7), 478–491.

PUBMED link

ABSTRACT

OBJECTIVES:

To identify doctors’ utilization of ICT; to develop and characterise a typology of doctors’ utilization of ICT and to identify factors that can enhance or inhibit the use of these technologies within medical practice.

METHODS:

An online survey of the 16,531 members of the Physicians Association of Barcelona who had a registered email account in 2006 was carried out. Factor analysis, cluster analysis and binomial logit model were undertaken.

RESULTS:

Multivariate statistics analysis of the 2199 responses obtained revealed two profiles of adoption of ICT. The first profile (38.61% of respondents) represents those doctors who place high emphasis on ICT within their practice. This group is thus referred to as ‘integrated doctors’. The second profile (61.39% of respondents) represents those doctors who make less use of ICT so are consequently labelled ‘non-integrated doctors’. From the statistical modelling, it was observed that an emphasis on international information; emphasis on ICT for research and medical practice; emphasis on information systems to consult and prescribe; undertaking teaching/research activities; a belief that the use of the Internet improved communication with patients and practice in both public and private health organizations play a positive and significant role in the probability of being an ‘integrated doctor’.

CONCLUSIONS:

The integration of ICT within medical practice cannot be adequately understood and appreciated without examining how doctors are making use of ICT within their own practice, organizational contexts and the opportunities and constraints afforded by institutional, professional and patient expectations and demands.

Please cite this article as:

Lupiáñez-Villanueva, F., Hardey, M., Torrent, J., & Ficapal, P. (2010). The integration of Information and Communication Technology into medical practice. Int J Med Inform, 79(7), 478–491.

PUBMED link