Development Areas in Knowledge Management Processes in

Social and Health Care Services: A Pilot Study

Nina Helander

, Annamaija Paunu

, Pasi Hellsten

, Hannele Väyrynen

and Virpi Sillanpää

Unit of Information and Knowledge Management, Tampere University, Tampere, Finland

Independent Scholar, Espoo, Finland

Modulight, Tampere, Finland

Keywords: Knowledge Management, Development, Social and Healthcare Sector, Assessment.

Abstract: Knowledge management is about providing the right information for the decision maker at the right time, in

the right format. This is equally essential in public sector as in the private sector enterprises. However, it is

often easier said than done where and how the development schemes are to be directed. More data and

information is needed. Public sector’s social and health care has a number of data sources where knowledge

management could make difference both in operational side, i. e. the care function and also in the management

of the function, i. e. resource allocation. However, these are quite often planned some time ago and in the

need of rethinking. The paper explores the possible points to be developed based on the knowledge

management process, how to combine the two for a better outcome.

1 INTRODUCTION

Knowledge management (KM) is a holistic and

systematic process that integrates technology and

human aspects, enabling genuine dialogue in the

management of organizations (Girard and Girard,

2015). In the process of KM, different stages can be

distinguished in which information and further

knowledge are processed from data (Choo, 2002).

Knowledge is collected, organized, stored, shared,

and utilized in a way that improves the decision-

making and functioning of the individual and the

organization (Vitt et al., 2002). However, there are

several challenges in KM, due to, for example,

technical infrastructure, poor quality data, human bias

in thinking, reluctance to share knowledge, or

inefficient practices and processes (Väyrynen et al.,

2017). Thus, the possibilities to develop the operation

are case-specifically many. It is plausible to assume

that costs are one centric way to guide the flow of

scarce resources for process development, especially

in the public sector, where the funds are from a shared

https://orcid.org/0000-0003-2201-6444

https://orcid.org/0000-0001-7602-1690

https://orcid.org/0000-0002-3636-280X

https://orcid.org/0000-0003-4949-5203

source and functions are regulated. Also there are

often political pressures and ambitions in the way

how the issues are to be approached. As the

development towards improved efficiency but also

‘better’ outcome is concerned, the data regarding the

costs are in a crucial role to direct development

schemes. In retrieval of this knowledge a KM process

will prove to be useful.

The aim of this paper is to identify the

development areas at different stages of the

knowledge management process in social and health

care sector (SHC). The article seeks to answer the

empirically weighted research question “What kind

of development areas in knowledge management

SHC operations can be identified at different stages

of the knowledge management process model?” The

context of the article is social and health care sector

and the empirical research has been conducted

through a pilot case study in that area. Through the

analysis of the pilot case, the article contributes to

knowledge management process literature. However,

as the empirical case of this study is highly

356

Helander, N., Paunu, A., Hellsten, P., Väyrynen, H. and Sillanpää, V.

Development Areas in Knowledge Management Processes in Social and Health Care Services: A Pilot Study.

DOI: 10.5220/0013067400003838

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3: KMIS, pages 356-361

ISBN: 978-989-758-716-0; ISSN: 2184-3228

specialized in nature, further generalizations based on

the results must be done with caution.

The structure of the article is as follows: After the

introductory chapter, Chapter 2 is followed by a

concise theoretical overview of the basics of

knowledge management. The focus of the article is on

the presentation of empirical pilot case study and the

analysis of the results, so the theoretical part is

followed by Chapter 3, which presents the

methodological choices and the pilot case study, and

Chapter 4, which presents the main results of the case

studies. Chapter 5 summarizes the results of the

article and contains an evaluation of the study and

presents topics for further research.

2 KNOWLEDGE MANAGEMENT

PROCESS

The purpose of knowledge management (KM) is to

collect, process, organize, store, share and utilize

knowledge in a way that improves the decision-

making and activities of the individual and the

organization (Jääskeläinen et al., 2020). With KM we

can identify our organization’s tacit knowledge and

combine it with explicit knowledge, also from

external sources, thus supporting the organizational

problem solving, decision making and strategic

development. KM is a holistic and systematic process

that integrates technology and human aspects

(Valkokari and Helander, 2007).

Knowledge, which is ultimately used in human

decision-making and action, is processed from data

through information into knowledge by adding

structure and meaning to it (Choo, 2002; Thierauf,

2001). This chain from data to knowledge emphasizes

the need for data processing and the nature of

enrichment. Thus, knowledge does not appear from

scratch, but is created by enriching data and

information. In order to make good, knowledge-based

decisions, data and information must be of high

quality and easily accessible in a form that is

understandable to decision-makers as timely as

possible (Vilminko-Heikkinen and Pekkola, 2017).

To ensure the functioning of this chain, it is necessary

to coordinate both the more technical side of KM and

the more human side in organizational structures,

processes, and practices (Jääskeläinen et al., 2020).

One practical tool for this knowledge processing

is the so-called knowledge process model, which is

comprised of following six phases: Knowledge need

identification; Knowledge retrieval and creation;

Knowledge maintenance and storage; Knowledge

sharing; Knowledge use; and Measurement and

learning.

The process usually begins with defining the

knowledge needs. The knowledge needs are first

identified so that they can later be met as well and

efficiently as possible. At this stage, knowledge is

obtained both from external sources (e.g. competitors

and customers), and internal sources (e.g. information

systems (IS) and communities of practice). The

knowledge created and collected from different

sources will be organized and maintained in the

organization’s repositories. This means the stage of

analyzing and organizing the knowledge, which

facilitates the next stages of the process, i.e.

knowledge sharing. However, knowledge only gains

its final value when it is used, for example, applied in

decision-making and operational development, and

when real changes occur in the organization's

operations. By assessing the changes that have taken

place and learning from the proceedings and thereby

identifying new development needs, the cycle starts

over. It should be noted that the process is in fact an

iterative process and that the variation between

phases is not always straightforward.

The process of KM presented above, and its

various stages include both the technical and the

human angle to knowledge (Jääskeläinen et al.,

2019). The model may be criticized as too a

mechanistic way to structure such a complex

phenomenon as KM is. In practice it is often the case

that some basic model is needed to identify the

challenges of knowledge management and to develop

best practices. Nor does applying the process model

of knowledge management to practice mean focusing

on the process rather than dialogue. On the contrary,

dialogue can be better built into organizations when a

clear framework is built. The different stages of the

process include both technical and human aspects,

thus the model serves as a good analytical tool for

identifying the challenges and opportunities of

knowledge management (Hellsten and Myllärniemi,

2019). The analysis of the stages in the process makes

it possible to improve the understanding of public

sector organizations of their own skills, knowledge

and knowledge resources and to support the use of

knowledge in problem solving, decision-making and

strategic development, as stated in traditional KM

literature (Dalkir, 2017; Grant, 1996; Hislop et al.,

2018).

Development Areas in Knowledge Management Processes in Social and Health Care Services: A Pilot Study

357

3 METHOD

Knowledge management (KM) process is studied

through an empirical case from Finnish SHC. The

aim was to develop a practical tool for assessing the

cost-effectiveness of utilising a gerontechnological

application in home care for the elderly (Colnar et al.,

2020a). The starting point for the pilot was a

previously developed cost-effectiveness assessment

model (CEAM) (Sillanpää and Korhonen, 2022),

which was to be tested and developed further. In the

pilot planning, it was important to understand the

purpose of home care, the services and operations,

and where the expected impacts could be visible (for

example, home care service processes or customer

well-being). Secondly, it was necessary to find out

where the data and information needed to assess the

effectiveness of the gerontechnological application

are located (e.g. the information system and the

parties managing the data and information, or the

entities producing data and information). A home

care operating area that had been offering

gerontechnology (medication dispensing robot) to its

customers for a long time was chosen to act as a pilot.

The home care administration selected the

participants in the pilot.

The next step was to identify changes that occur

in the implementation or in the use of a new

gerontechnological application by the customer. To

explore the changes, three participatory workshops

were conducted in spring 2022 for 70 healthcare and

home care professionals. Based on the results, a

critical question emerged as to what the core

information is needed to verify technology-

influenced changes in the CEAM. In addition,

consideration was given to how the necessary data

could be collected from the organisation's various

IS’s. To find answers, seven semi-structured

interviews were conducted with the management and

professionals of the SHC support services. The

interviews were recorded, transcribed and analysed

using qualitative content analysis. After the

interviews, more workshops were held in summer

2022 with appropriate interviewees to find out how to

refine the data and information needed. The next step

was to define the indicators for the evaluation and to

develop a CEAM together with researchers and city

coordinators (financial planning representative,

report designer and telecare technology expert). The

pilot dealt with client and health data in home care for

the elderly, so special attention was paid to the

information security of technology and personal data;

researchers were provided with anonymized data

from which individual participants could not be

identified. (Acosta et al., 2022)

Indicators identified and utilised in the literature

in assessing the cost-effectiveness of digital remote

monitoring services include hospital days, clinical

visits in primary or specialised health care, and the

number and costs of first aid visits. In home care, cost

indicators may include home visits by healthcare

professionals (nurses), number of contacts, and direct

costs of remote monitoring or remote access (e.g.

technology rental costs, user fees, Internet access

costs) (Polisena et al., 2009; Seto, 2008; Upatising et

al., 2015). To identify the effects of digital services,

in addition to direct costs, it is necessary to identify

the process requirements for new home care practices

and the costs related to process changes (Askedal et

al., 2017).

In the pilot, the indicators to be evaluated in the

CEAM were identified from workshop results,

interviews and literature, as well as from previous

research by Sillanpää & Korhonen (2022). The

researchers formulated guidelines for the use of the

CEAM, and the model was ready to be piloted in

home care for the elderly on the use of a medication

dispensing robot. The pilot was carried out with data

produced by the City, which made the actual data and

reports on the use of home care services, the number

of users of the technology and costs available for the

whole year. The pilot consisted of ten users of

medication dispensing robots (hereinafter referred to

as ‟medicine dispensing robot in use”) and ten

respondents of not using the medicine dispensing

robots (hereinafter referred to as ‟medicine

dispensing robot in NOT use). Customers with the

same level of functional capacity were invited to

participate in the pilot in both control groups

medicine dispensing robot in use/medicine

dispensing robot NOT in use), the selection was not

based on defined medical diagnoses. The CEAM

makes it possible to define a follow-up period (e.g.

quarterly), which makes it possible to obtain

information on the development of service

production, operations and costs by means of

visualization (e.g. graphic images) with the data of

the monitoring period.

4 EMPIRICAL RESULTS

The basic idea of the model is to compare the cost-

effectiveness of a technology-assisted service for two

elderly home care customer groups, a medicine

dispensing robot: a medication dispensing robot for

groups in use and a medicine dispensing robot not in

KMIS 2024 - 16th International Conference on Knowledge Management and Information Systems

358

use. The costs were assessed from the service

provider's point of view, and customer fees were

excluded from the CEAM. The template is based on

a spreadsheet program that contains three separate

pages. The first page contains instructions on how to

use the model: instructions to support the use are

recommended so that the indicators to be evaluated in

the model are understood and how the measurement

is utilized (Jääskeläinen et al., 2020, 2013). The

instructions are important for the measurement and

the calculation principles of the background data to

be the same regardless of the time of measurement or

the person using the model. In addition, the page

contains the indicators of the model, the sources of

data or other information used (quantitative,

qualitative, and financial information), instructions

for calculating costs, the service provider's service-

specific price, and the persons responsible for the

information needed for the meters, which is used on

page two of the spreadsheet program.

There is also a form to which data and information

from the organization's IS’s were added (e. g. McKlin

et al., 2001), in the pilot the data were entered

manually. The data and information needed to

implement the pilot, instructions for obtaining the

necessary information, and ownership of the data and

information had to be defined. The indicators to be

evaluated and entered were the use of home care

services/number of clients (searchable from the

patient IS), the number of home visits in physical

home care and the costs of visits (to be retrieved from

the service provider's financial administration). The

home care did not separately record remote visits

during the review period, but the remote visit was

recorded as a physical visit, so remote home care

visits as a separate examination were excluded from

the pilot. Indicators were also the number and cost of

emergency calls and the number of hospital days.

Resource-based information from home care staff is

important to illustrate how technology can support

resources and reduce peak-hour physical home care

home visits. Therefore, home care visit times had to

be classified and the number of visits in the time

frame had to be manually entered in the table. The

operating costs of the medicine dispensing robot were

classified into two categories: direct and indirect

costs. Direct costs derive from the price of the

equipment (e.g. lease or rental costs) and general

costs of service activities (e.g. general IT costs).

Indirect costs include technology support services for

both home care staff and clients: training costs related

to the use and administrative costs of the technology

used by the customer (e.g. customer and service

production data), as well as the number of service-

related calls or contacts.

The number of clients working with home care

services is essential information, and the customer

months during the year may be of different lengths in

the control group (e.g. not all clients are necessarily

home care customers for the entire follow-up period,

which was one calendar year). For this reason, the

customer months had to be made commensurate with

the calculation formula in the spreadsheet so that the

number of customer months in the two groups being

compared is proportionate.

The aim of the pilot was to test the developed

CEAM with the actual service usage and cost data of

the home care service provider for the elderly, and to

obtain preliminary results on the cost-effectiveness of

the medication dispensing robot in home care for the

elderly. The CEAM provides a tool for assessing the

economic impact of the use of technology. However,

evaluation is a combination of several indicators and

the context of the evaluation influences the set

indicators to be evaluated, and therefore the

limitations of the model must be taken into account

when considering the results. In general, technology

is expected to have an impact on the well-being of the

elderly and the well-being of employees at work. In

the pilot, qualitative impacts were identified (e.g. RAI

(Resident Assessment Instrument) system for

mapping of clients' functional capacity and service

needs, 15D indicator for assessment of the

effectiveness of treatment, but the qualitative impacts

were not measured in this pilot. The CEAM makes it

possible to measure the economic and quantitative

impacts of the use of technology, but qualitative

assessment of client and home care personnel is also

needed.

5 DISCUSSION AND

CONCLUSIONS

Making the knowledge management process to work

requires seamless cooperation between the technical

infrastructure and human factors, so that knowledge

management can truly create value for public

administration actors and citizens (Jääskeläinen et al.,

2019). This means also simultaneously know-how

requirement for the personnel and in our case also for

the other side of the table, the customers as they are

recently called. In addition to this, the process is

advised to be familiar for the decision makers as the

phases while interconnect often have their own

features and actions that can offer improvement. In

Development Areas in Knowledge Management Processes in Social and Health Care Services: A Pilot Study

359

summary both the overall picture is needed but also

the individual phases need to be known. KM process

model offers detailed enough picture to be used for

further development targeting and possibly even

schemes on issues in the practice.

Much of the previous public sector knowledge

management research has focused on issues related to

either IS’s, data quality, or specific aspect of

knowledge management. Less frequently, the various

stages of the entire knowledge management process

have been illustrated, from information needs to

information acquisition, analysis and sharing, and its

utilization to offer basis for process development. In

this paper, we have sought to look at this whole

process of knowledge processing and, through the

Table 1: Key empirical findings.

process

hase

Development area

Knowledge

need

identification

Identification of qualitative

data indicators, not only

quantitative. On both clients

and personnel.

Knowledge

retrieval and

creation

Permits process, various

IS’s, data comparability,

automation needed to retrieve

data from different IS’s, newer

data sources

Knowledge

maintenance and

storage

Automated systematic

identification of customer

relationship classification, data

accessibilit

Knowledge

sharing

Data acquisition and

information sharing between

the parties, esp. decision-

makers, policies and

rocedures on the matter.

Knowledge

use

Ownership, management

and development must be

desi

nate

Measurement

and learning

Development and usage of

the CEAM must be strategic,

defined key performance

indicators

pilot case study, have identified development areas of

knowledge management in SHC. The key empirical

findings concerning each of the KM process phases

are illustrated in next Table 1.

The paper emphasizes the utilisation of data and

information, especially in SHC and home care for the

elderly, by showing that data can create value for

organisations and employees if relevant data and

information are identified from data flows, who in the

organisation manages the data or owns the data, and

also for what purpose the data and information is used

and who benefits from the data and information. After

identifying these elements, the CEAM described in

this article can provide visualized information to

support decision-making, and the information can

guide the actors in the organization to react to

situations in their daily operations.

Although this study succeeded in presenting one

example of a CEAM with illustrative results, the

study has some limitations. First, the sample of the

pilot study was small and limited to a specific location

and a specific, individual gerontechnology in Finland.

Secondly, home care for the elderly is publicly funded

in Finland, although clients are charged for services.

For this reason, the results cannot be widely

generalised because, for example, the different

wellbeing areas may have different service fees and

the provision of services at different prices, or

because the model may have to be modified, for

example, for each wellbeing services sector (e.g.

indicators defined differently regionally). In addition,

the results cannot be compared with private sector

healthcare service production, such as health

insurance -based service production. In the future,

research with a larger sample and the context of home

care for older people at national level with uniform

indicators will be needed to formulate larger picture

of the development ideas. In future studies, it should

also be taken into account that home care clients may

use several gerontechnologies, and it would be

important and interesting to be able to assess the use

of several gerontechnologies and their combined

impact on the client's health and well-being as well as

the service provider's operating costs. However, this

requires, among other things, the automatic

identification of customer relationships from IS’s,

which has been included in the development

proposals. The aim of this study was to develop the

previous CEAM towards a more extensive cost-

effectiveness assessment and to implement the

evaluation in practice. The pilot identified several

qualitative effects of technology on both home care

clients and employees but measuring them at this

stage proved challenging. However, the model can be

KMIS 2024 - 16th International Conference on Knowledge Management and Information Systems

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used to illustrate changes in operational capacity

(RAI values, number of emergency visits), and thus

the customers' ability to function in everyday life has

been included in the model. More research is needed

to add qualitative indicators alongside the economic

factors of the CEAM (Colnar et al., 2020b; Colnar

and Dimovski, 2020; McKlin et al., 2001). However,

it would be at least as important to raise a debate on

the issues of principle, such as policies, rules of the

game, and processes that should change to enable

genuine knowledge management. Moreover, it

should always be borne in mind that knowledge

management is not an end, but only a tool - the goal

is to create value together and sustainably better than

before.

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